dataesr/scientific-paragraphs-categorization huggingface dataset:
This taxonomy of object models is depicted Fig. 4. Lastly, a fourth axis characterizes models that are compatible with some class of image simulation actions. This property is modeled as a capacity (which is a state borneBy a model). For example, a model is a CT-simulation compatible model if it bears some CT simulation capacity, defined as a capacity to enable action, which enablesToFulfil some role of model in CT simulation. In practice, membership to the CT simulation compatible model class is inferred using a dedicated rule expressing a condition about the presence of those physical parameters that are required for the corresponding class of simulators. Consequently, a fully annotated model is composed of a set of data files (containing geometry and physical parameters) and a RDF file describing the semantic content of these data files.
Implementation: The VIP platform was created using the Google Web Toolkit (GWT). To design the annotation software, we used Jena, which is a library developed by the Apache Foundation (http://jena. apache. org/). One of the main challenges was to manipulate the annotations on the web platform during the creation of a model. Indeed, as the application is running inside the web-browser of the client, it is not possible to include all the needed libraries (like Jena). Consequently, all the operations consisting in creating and manipulating the annotations had to be done on the server-side while being driven by user interaction on the client-side. These constraints led us to the definition of a communication protocol between the client and the server to exchange information on annotations (Fig.
5 ). A simple Java objects model was designed, composed of representational objects used to send the information to display to the client (Data Transfer Objects). This approach makes it possible to iteratively create the semantic annotations on the server-side. We created 10 models which illustrate most of the features of our ontological representation (Table  4 ). Eight models were derived from the BrainWeb (http://brainweb. bic. mni. mcgill. ca/brainweb/) simulator's geometry files.
BrainWeb We also created physical parameters maps allowing the models to be used as input of simulation. Two models were also created using as input 3D scanner models from a database available on the "Institut de Recherche contre les Cancers de l'Appareil Digestif" (IRCAD) web site (http://www. ircad. fr/). We present here one model (named BrainWeb-Severe-Multiple-Sclerosis-with-Maps) to illustrate the annotation file created during the annotation process. Fig. 6  presents the visualization of the annotations on the VIP platform as a tree structure representing the content of the model. This view is automatically built by parsing the annotation file on the server side. Fig. 7  presents an extract of the annotations file which was created using the graphical annotation tool available on the platform.
Currently, the SPARQL query tool is not included in graphical user interface of the platform as the users are not expected to be familiar with this language. However, we designed pre-defined queries that can be executed by a simple click. For example, the query presented in Fig. 8  displays the list of models of the repository containing anatomical objects and uses the knowledge imported from the FMA (and included in our ontology) to display the regional parts associated to the anatomical objects contained in the models. This kind of knowledge can be used for example, for query expansion. Indeed, if the user is looking for an object that cannot be found in the existing models, the system can then suggest querying the regional parts of this object, or the objects that the queried object is a part of.
In this study we put emphasis on the reuse of existing ontologies and on the extraction of the relevant subsets that are needed for our application. Our concern in reusing ontologies like FMA, PATO, MPATH, RadLex and ChEBI is really to use ontologies that are likely to be chosen by biologists in their own model developments and thus to facilitate future interoperability with their models and software. However, such subsets are not easy to specify since simulation studies may involve a wide spectrum of objects, from physical phantoms to biological objects at various scales and concerning various species. Our strategy consisted in designing a flexible module extraction procedure to be able to easily extend those modules in the future if needed. An alternative could have been to include complete ontologies, but we felt that it was equally important to keep our application ontology as small as possible to enable the use of reasoners, supporting intelligent querying or reasoning, which would not be possible with large ontologies such as the entire FMA.
One of the remaining problems is the consistency of the modules according to future evolutions of the original ontologies. Indeed, the extraction of the modules, even supported by automatic extraction tools, still needs manual operations (e. g. execution of the extraction query, importation in the global ontology, etc. ). Reuse of FMA: Our choice of FMA was motivated by the fact that FMA is a mature, highly recognized ontology. Moreover, it is quite exhaustive, at least for human anatomy. It also puts a strong emphasis on part-whole relationships, which is an important feature for us for the following reason. Anatomical object layer parts depict objects and refer to corresponding anatomical entities. It seemed important to us that users can specify anatomy at the level of precision they feel appropriate, e.
Choice of an extraction tool: We made the choice of vSPARQL as a tool to extract relevant subsets of existing ontologies. vSPARQL  [16]  (in conjunction with Gleen  [17] ) extends the SPARQL language and offers the possibility to create a recursive query. This feature is particularly useful to extract parts of an existing ontology without knowing its structure. Actually, few other solutions exist to perform such task. The simplest one is provided by the native CONSTRUCT clause of the SPARQL language (http://www. w3. org/TR/rdf-sparql-query/), and enhanced with the property paths expressions in SPARQL 1. 1. The CONSTRUCT query returns a single RDF graph specified by a graph template. It allows specific triples to be extracted from an ontology in order to create a module. Alternatively, ModTool  [35]  is an alternate solution which offers tools to extract a module from existing ontologies.
However, it provides less flexibility than vSPARQL in designing the query for the extraction. A review of the different approaches to create ontology modules is presented in  [10]. Current status and limitations of the implementation: All the capabilities described above (section III. 2) are available, and can be tested by connecting to the VIP Portal 6. However, only the VIP project partners used the models' repository capabilities yet. Of course, only a subset of the capabilities of the ontology is exploited in the VIP implementation yet. For example, our ontological model offers a great flexibility to define pathological structures either using pathological entities (chosen from the MPATH extract) or defining them as anatomical structures (chosen from the FMA extract) bearing (using the hasForQuality object property) one or more pathological qualities (chosen from the PATO extract).
Acknowledgments This work was funded by the  French National Research Agency (ANR)  under grant  ANR-09-COSI-03. The authors warmly thank the authors of the vSPARQL software for their support, especially  Landon T. Detwiler. Many  thanks to the NeuroLOG ontology task force and especially  Gilles Kassel  in Amiens. Many thanks too to  Pr. Stefan Schulz  from the  University of Graz in Austria  for his interesting feedback.
2 189 vSPARQL extraction (Recursive children of (ref [27]) foreign body + manual addition) vip-atom ChEBI V93 254 vSPARQL extraction (Recursive children of (ref [28]) chemical entity) OntoNeuroLOG-dataset-processing OntoNeuroLOG 124 Existing module of OntoNeuroLOG (ref [12]) Dataset OntoNeuroLOG 76 Existing module of OntoNeuroLOG (ref [12]) Medical-dataset-expression OntoNeuroLOG 38 Existing module of OntoNeuroLOG Core-ontology-programs-software OntoNeuroLOG 153 Existing module of OntoNeuroLOG OntoNeuroLOG-extension-of-DOLCE* OntoNeuroLOG 183 Existing module of OntoNeuroLOG DOLCE-particular DOLCE 38 Existing module of OntoNeuroLOG (ref [11) * this module gathers several ontology files from OntoNeuroLOG, such as 'Inscription, Expression and Conceptualization' (IEC), 'Capacities and Functions' and 'Artifacts'.
STATISTICAL ANALYSES All data are shown as mean ± SEM. ANOVA and post hoc analyses were performed using SAS v. 12 software. For body weight changes, statistical analysis was performed using a two-way ANOVA (factor strain × diet, with repeated measures on factor week). For food intake, a two-way ANOVA (factor strain or diet, with repeated measures on factor week) was performed. Finally, for body composition and serum analyses, a one-way ANOVA was used followed by post hoc comparisons with the Dunnett's test. P-values <0. 05 were considered statistically significant.
For this purpose, the measured values were corrected using the HBOM iterative procedure in the same way as for the background density ρ, i. e. applying the parameterized detector effects multiple times and extrapolating the fluctuations to an ideal detector  [29]. Since the correction is based on the properties of the measured heavy-ion event, it takes into account all correlations in the event. The corrected width of the δ p ch T distribution is given in Table  2  for central collisions and various cone radii. The FastJet package provides a measure of fluctuations, σ FJ , which is defined from the distribution of individual jet momentum densities p i T,ch jet /A i such that 15. 9% of all clusters within an event satisfy p i T,ch jet /A i < ρσ FJ √ A  [31]. This measure corrects to first order the area dependence of fluctuations (σ ∝ √ A), but is not sensitive to the tail of the distribution.
Detector effects The jet response in the ALICE detector is evaluated using simulations with the PYTHIA6  [32]  event generator and GEANT3  [33]  for detector response, using the same reconstruction software settings that are used for the reconstruction of Pb-Pb events. The effect of the high track density in Pb-Pb events on the tracking efficiency was studied using HIJING  [34]  events with the GEANT3 detector simulation. It is found that the tracking efficiency is ∼2% lower in central Pb-Pb collisions than in peripheral collisions and pp collisions. This additional centrality-dependent inefficiency was introduced to the PYTHIA events by a random rejection of tracks. The jet response is determined on a jet-by-jet basis by comparing jets before (particle level jets) and after detector simulation (detector level jets), that are geometrically matched.
Results Jet spectra are measured with resolution parameters R = 0. 2 and 0. 3 in four centrality classes: 0-10%, 10-30%, 30-50% and 50-80%. Figure  7  shows the measured Pb-Pb jet spectra reconstructed from charged constituents with p T > 0. 15 GeV/c. The jet spectra are unfolded for detector effects and background fluctuations, and corrected for the jet finding efficiency as described in the preceding sections. The upper panels show the inclusive jet spectra while for the center and lower panels the jet spectra with a leading track bias of at least 5 and 10 GeV/c are shown. The markers represent the central values of the unfolded jet spectra. It should be noted that the unfolding procedure leads to correlations between the data points, because the width of the response function is similar to the bin width: neighboring p T -bins tend to fluctuate together (correlated) while bins with some distance tend to be anti-correlated.
Results One hundred seventy-four models of six-bladed β-propellers were identified in the PDB and examined by eye for suitability as templates for protein design. The NHL repeat structure PDB entry 1RWL  (27) , the sensor domain of a protein kinase from Mycobacterium tuberculosis, was found to be the most visually appealing due to the apparent symmetry of the Cα trace and was selected on this basis alone. In common with almost all β-propeller proteins, this structure shows a so-called "Velcro" strap, the last β-strand completing the first domain (Fig. S2 )  (19). The sequences of the blades were considered as separate genes, aligned and used for ancestral sequence reconstruction of the parent blade (Fig. 1  A and B)  (28). Three out of the six blades have an identical number of amino acids per blade, and insertions or deletions with respect to these blades were not allowed.
Materials and Methods Protein Design. The sensory domain of PkdN of M. tuberculosis (PDB ID code 1RWL) was chosen as a template due to its compact and symmetrical fold  (27). The model was divided into individual "blades" for sequence comparison, the sixth blade including the N-terminal sequence, which forms a "Velcro" strap. A phylogenetic tree derived from the blade sequences was used to predict possible ancestral blade sequences using FastML  (28). Blade number 6, which is the combination of the N-and C-terminal residues composing the Velcro strap, was used to root the tree and was excluded from ancestral reconstruction. For each of the four nodes in the tree (blades 1-5), 25 different putative ancestral sequences were determined. Comparison of these possible ancestral sequences with the 1RWL sequence revealed that the third blade (residues 107-148) was closest to the most likely ancestor and was used as the template to construct a hexameric protein with perfect C 6 symmetry using RosettaDock  (29).
During a stochastic symmetrical docking process with C 6 symmetry constraints, 1,000 solutions were created. The bestscoring solution showed sixfold symmetrical β-propeller architecture. Using the Molecular Operating Environment (MOE) (Chemical Computing Group), this model was used to build a single polypeptide chain carrying six identical repeats. The different putative ancestral sequences were mapped onto this model, and their energies were calculated by using a program that calls the Rosetta protein modeling suite. This program takes an input list of sequences and a template protein structure. Each sequence is mapped onto the structure by mutating residues at all positions, and the all-atom protein model is then relaxed using the Fast Relax protocol before scoring with the standard all-atom score function (score12_full.
Data were collected at beamline 17A of the Photon Factory, Tsukuba, Japan, using an ADSC Quantum CCD detector. The X-ray wavelength was 1. 000 Å, and each image was collected from 1°oscillations. Data were processed with HKL2000  (40). The computational models were used for molecular replacement using MOLREP  (41) , followed by refinement with Refmac5 (42) and PHENIX  (43). Manual modifications to the model were carried out with COOT  (44). Data handling was carried out with the CCP4 suite  (45). An overview of data collection and refinement statistics is given in Table  S3. For the Ramachandran statistics of each structure, see Table  S4. Two separate structures were determined for Pizza2-SR. All structures were deposited in the PDB and assigned the following PDB ID codes: 3WW7 (Pizza2), 3WW8 (Pizza3), 3WW9 (Pizza6), 3WWA (Pizza7H), 3WWB (Pizza2-SR Form A), and 3WWF (Pizza2-SR Form B).
Sedimentation velocity experiments were carried out using an Optima XL-I analytical ultracentrifuge (Beckman-Coulter) using an An-50 Ti rotor. For sedimentation velocity experiments, cells with a standard Epon two-channel centerpiece and sapphire windows were used. Four hundred microliters of protein (1. 0 mg/mL) and 420 μL of reference buffer (20 mM Hepes, pH 8, 100 mM NaCl) were used in each experiment. The rotor temperature was equilibrated at 20 °C in the vacuum chamber for 2 h before starting each measurement. Absorbance (280 nm) scans were collected at 5-min intervals during sedimentation at 50,000 rpm (182,000 × g). The resulting scans were analyzed using the continuous distribution c(s) analysis module in the program SEDFIT  (47). Sedimentation coefficient increments of 200 were used in the appropriate range for each sample.
The frictional coefficient was allowed to float during fitting. Partial specific volume of the proteins, solvent density, and solvent viscosity were calculated using the program SEDNTERP  (48). Electrospray Ionization Mass Spectrometry. Samples for Nanoflow ESI were prepared by extensive dialysis against 20 mM ammonium acetate. The protein concentration was adjusted to 10 μM by dilution with 20 mM ammonium acetate. The mass spectra were obtained by Synapt G2 HDMS mass spectrometer (Waters) with a nanoESI source. The mass spectra were calibrated with (CsI) n Cs + ions from m/z 1,000 to m/z 10,000. MassLynx version 4. 1 software (Waters) was used for data processing and peak integration. The temperature of the ion source was set to 70 °C. An aliquot of 3 μL of the sample solution was placed in a nanospray tip (HUMANIX) and electrosprayed at 0.
ACKNOWLEDGMENTS. We thank  Prof. Satoko Akashi  for electrospray mass spectrometry measurements and  Dr. Shinichiro Egashira  for help with RT-PCR. A. R. D. V. acknowledges the  RIKEN Foreign Postdoctoral Research program  for a postdoctoral grant and the  Japan Society for the Promotion of Science  for a grant-in-aid, as well as the  RIKEN Integrated Cluster of Clusters  for computational time.
Real-time PCR for the SNV capsid gene was performed using SMBV-infected amoeba as template, and the expected fragment was amplified from those cells, but not from the controls (water or uninfected amoebas). A primer pair was designed to amplify a large fragment of the capsid gene based on the consensus sequence of virophage capsid genes available in GenBank. An amplicon was generated from SMBV-infected amoebas and then confirmed by sequencing. To analyze the influence of the virophage on giant virus replication, purified SMBV were diluted and filtered. The aliquoted virophage solution was stored at -80°C until use. An A. castellanii monolayer was co-infected with APMV (TCID 50 per cell rate of = 10) and 100 μl of the solution containing the undiluted virophage isolated from SMBV. After 16 hours, we analyzed the cells by EM.
SMBV genome After sequencing, assembly and annotation, we obtained a partial SMBV genome (scaffold) of 1,213,607 bp (Figure  6A ), which is comparable in size to the largest genomes described for mimiviruses to date (Figure  6B ). We achieved approximately 98. 8% coverage of the genome (considering APMV covered orthologous) (Genbank access number: KF959826). The SMBV genome has a GC content of 27. 0% (Figure  6A ) and is approximately 50,000 bp larger than the APMV genome. A total of 938 ORFs, ranging in size from 150 to 8835 bp (Figure  7A ), were annotated as putative genes. The average ORF size is 1001. 8 bp. Using these ORFs, a gene similarity search was conducted using the Blast2GO platform. Many of the ORFs within the SMBV genome had only been found previously in viruses from the Mimiviridae family, including those which putatively encode proteins with roles in protein translation or DNA repair.
Overall, SMBV shared the most ORFs in common APMV (~91%), followed by ACMV (~6%), Mimivirus pointrouge (~0. 5%), Moumouvirus goulette (~0. 5%) and others (~2%) (Figure  7B ). An analysis of the SMBV genome identified 19 ORFs related to DNA replication, 10 involved in DNA recombination, 14 linked to DNA repair and five related to tRNA aminoacylation, which is important for protein translation (Figure  7C ). We also identified 264 domains that are putatively related to protein binding and 200 domains linked to catalytic activity (Additional file 1: Figure  S1A ). The sequence similarity between SMBV ORFs and the homologous genes in GenBank ranged from 35 to 100%, with many candidates showing 50 to 60% similarity, indicating that SMBV genome has many unique features (Additional file 1: Figure  S1B ).
SMBV phylogeny To determine which viruses SMBV is most closely related to, we performed the following analyses:  [1]  we constructed phylogenetic trees by aligning the ribonucleotide reductase (Figure  6B ) and helicase gene (data not shown) sequences from SMBV with GenBank sequences and  [2]  we constructed a Venn diagram showing a presence-absence analysis of ORFs from SMBV and related viruses (Additional file 3: Figure  S3 ). Both phylogenetic trees show that SMBV clusters with members of Megavirales order group A (MGA), which includes APMV (the prototype of the family) and ACMV (Figure  6B ). MGA was most closely related to Megavirales group B (MGB), which includes Acanthamoeba polyphaga moumouvirus (APMOUV), and was distinct from Megavirales group C, which includes MCHV and other viruses.
Other NCLDV family members were also included in the analysis, and their position in the phylogenetic tree generated in this study corroborates previous phylogenetic studies. The phylogenetic trees were constructed by MEGA 4. 1 using neighbor joining, maximum parsimony and other methods with 1,000 bootstrap replicates (n. differences model) based on the ribonucleotide reductase predicted amino acid from the SMBV and other nucleocytoplasmic large DNA viruses. The same tree topology was observed for all phylogenetic approaches. Next, a Venn diagram (Additional file 3: Figure  S3 ) was constructed using the predicted ORFs from SMBV and the ORF data set from its closest relatives, including APMV and ACMV (from the MGA group), APMOUV (from the MGB group) and MCHV (from the MGC group). The SMBV ORFs were aligned with the ORF data set of each viral counterpart using the Blastn allagainst-all method feature of Blastall 2.
RNV virophage sequence analysis The RNV capsid gene was also sequenced and analyzed (primers: 5' ATGTCTAATTCAGCTATTCCTCTTA3' and 5'TCACATTTTAAGTTCTTTTCTCAAT3"). We then manually aligned the sequence with conserved virophage sequences from GenBank and used Modeltest software to determine which model of evolution was most appropriate for our analysis. Phylogenetic trees based on the capsid gene sequence were constructed using MEGA 4. 1 with neighbor joining, maximum parsimony and other methods with 1,000 bootstrap replicates. The RNV capsid sequence was deposited in GenBank (KJ183141). Our results show that the RNV capsid gene shares 100% identity with SNV and high identity with other SNV genes. Therefore, RNV clusters with SNV-like viruses and does not cluster with Mavirus or the Organic Lake Virophage (Additional file 4: Figure  S4 ).
Sequencing analysis The SMBV genome was sequenced using a 454 platform (Roche). For genome assembly, we used the read mapping approach implemented by the CLC Genomics Workbench 5. 5. 1 program to generate contig sequences, and the Perlbased genome assembly tool ABACAS. 1. 3. 1 (algorithmbased automatic contiguation of assembled sequences) that yielded a partial genome (scaffold) of 1,213,607 bp (KF959826) (draft). The open-reading frames (ORF) were predicted using the Markov-based methods employed by Glimmer3 and FGENES. We also transferred annotations from a closely related genome using the Rapid Annotation Transfer Tool. The predictions were manually curated, and the ORFs were assigned a final identity. ORFs smaller than 150 bp were ruled out. Finally, 938 ORFs were annotated as putative genes.
Acknowledgments We thank  João Rodrigues dos Santos  and  Gisele Cirilo dos Santos , colleagues from  Gepvig  and the  Laboratório de Vírus , for their excellent technical support. We would also like to thank  CNPq ,  CAPES ,  FAPEMIG, Pro-Reitoria de Pesquisa da Universidade Federal de Minas Gerais (PRPq-UFMG) , and  Centro de Microscopia da UFMG.