dataesr/scientific-paragraphs-categorization huggingface dataset:
It contributes to our understanding of P. rubens' metabolic capabilities and provides a valuable resource for future studies in the field of systems biology. Furthermore, we demonstrate that iPrub22 exhibits a predictive capacity for specialised metabolite production on the reference medium (i. e. glucose as C-source, ammonia as N-source). Finally, a particular effort was made to explore the specialised metabolism integrated into this model. Although the generation of more data in recent years has led to an increasing size of networks, the percentage of specialised metabolites in the models remains unchanged. Therefore the effort undertaken to update the reconstruction databases seems linear, but the proportion devoted to specialised metabolites warrants further investigation. Exploring the presence of specialised metabolism further highlights the lack of information within the databases and shared identifiers.
Materials and methods Reconstruction is performed following the steps recommended in the 2010 Reference Protocol  [60]. The iPrub22 reconstruction is provided in the SBML community standard format  [97]  with a quality score of 74% evaluated by MEMOTE (v0. 13. 0)  [54]. To ensure transparency and reproducibility, the reconstruction process and related documents are available in Supporting Information. As a final step, the model was registered in BioModels  [98]  and given the identifier MODEL2306150001. The primary stages: Generating the draft Data origin. The genomic data for P. rubens Wisconsin 54-1255 (i. e. 12,557 protein and gene sequences), as well as the General Feature Format file associated, were downloaded from the Ensembl Fungi browser under accession number GCA_000226395  [13].
Functional annotation. Penicillium rubens functional annotation is performed using the Trinotate pipeline (v3. 2. 1)  [31]  for its boilerplate SQLite database. Thus, homology analyses are launched with blastx and blastp (v2. 5. 0) with an e-value cut-off of 0. 001 against the UniProt database (download on 27 June 2020). The best hit of a sequence is considered its annotation. Search for protein domain is carried out by HMMER (v3. 3)  [99]  against the PFAM database (download on 27 June 2020) with an e-value cut-off of 0. 001 and a threefold in the per-domain output of 0. 01. SignalP (v4. 1g) [100] and TMHMM (v2. 0c) [101] are used to search for peptide signals and transmembrane domains, respectively, with their default settings. Protein subcellular localization is determined using DeepLoc (v1.
0)  [102]. Data that are mandatory for the GSMN reconstruction, Gene Ontology Terms (GOTs)  [27, 28]  and EC numbers are captured respectively using an internal Trinotate script and by querying the KEGG database  [29]  using its own orthology identifiers (KO). The resulting KO list is enriched by a P. rubens proteome analysis performed on the KAAS webserver (Automatic Annotation Server v2. 1)  [30]  against a dataset composed of 367,616 genomic sequences exclusively selected from 37 fungi kingdoms (Best Bidirectional Hit-method and other settings by default-July 2020) and by the exploitation of pre-existing P. rubens data  [13]  on KEGG (Genome information accession-T01091). EC-numbers are then retrieved from these three selected KO sets using KEGG-API REST (REpresentational State Transfer) application programming interface in July 2020.
Annotations combined with the information contained in the P. rubens General Feature Format are aggregated to generate a GenBank file using the script: https://github. com/ ArnaudBelcour/gbk_from_gff. This file was then used as input to the PathoLogic software from the Pathway Tools suite (v23. 0 default settings)  [103]. This qualitative metabolic model reconstruction module allows the inference of the reactome from the annotated genome. The database containing the information from the annotation was then exported in attribute-value flat files, which were necessary for further analysis in the AuReMe workspace  [63]  (see Data consensus and integration for further details). Orthology completion. Using OrthoFinder (v2. 3. 12)  [32]  and Blastp (v. 2. 5. 0), Reciprocal Best Normalized Hit (RBNH) strategy was applied to identify functionally identical genes.
Blastp was parametrised with an e-value of 0. 001 and performed against seven selected templates: Arabidopsis thaliana  [33] , Aspergillus nidulans  [34, 35] , Aspergillus niger  [34, 35] , Neurospora crassa  [36] , Penicillium species complex  [34, 35] , Saccharina japonica  [37]  and Schizosaccharomyces pombe  [34, 35]. Upstream, data from the different selected templates were filtered to keep only the sequences present in the associated GSMN models. External sources. The reconstruction is enriched and completed by external data sources. First, data from the Pathway/Genome Database Concepts PchCyc (PGDB belonging to Tier 2) available on BioCyc are searched from the special SmartTables  [38]  provided. Second, compliant information present in the first  [26]  and latest  [39]  versions of the network are extracted and parsed to be added to the draft.
Only those reactions supported by genomic information that either has a MetaCyc compatible reaction ID or reactions for which all reactants and products have a MetaCyc ID (in which case the original reaction ID is retained) are incorporated into the draft. Data consensus and integration. AuReMe (AUtomatic REconstruction of MEtabolic models-v2. 4) dedicated to GSMNs reconstruction  [63]  was used to reconstruct the P. rubens GSMN. This ToolBox encapsulates the various programs needed to create a high-quality network and maintain a consistent steps record. Results obtained from PathwaysTools and OrthoFinder are injected into AuReMe to generate the intermediate subnetworks resulting from the functional annotation and the orthology search, respectively. Reconstruction was based on the data present in MetaCyc (v23.
0) [104] As data templates for orthology refer to other databases like KEGG  [29]  or BiGG [105], a mapping operation using MetaNetX dictionary  [53]  (i. e. intrinsic to AuReMe MNXref version 2018/09/14) was performed to obtain identifiers compliant with MetaCyc. These data are then merged and completed with the information from external sources. Gene annotations are evaluated and compared using the Fungi-Fun [106] web server (v2. 2. 8 Beta). The resulting draft is then analysed both qualitatively using topological analysis and quantitatively using constraint-based analysis, refined by manual curation and enriched with data extracted from the literature (See the following section). The majority of the Figures are drawn using R (v3. 5. 1) and the following packages: ggplot2 (v3. 3. 5) [107], hrbrthemes (v0.
The final stages: from draft to high GSMN quality Potential producibility-Topological analyses and manual curation. The reconstruction is refined by examining the presence of known compounds (i. e. called targets) in P. rubens. The metabolites list to be verified is first established according to data described in the literature, molecules isolated in the laboratory and results identified by antiSMASH/fungiSMASH  [42]. This list is enriched by metabolites present in the LOTUS database (https://lotus. naturalproducts. net)  [43]  to focus more precisely on the specialised metabolism. Lastly, the metabolites present in the draft are filtered according to the annotations containing the term "secondary" in the MetaCyc ontologies. Then, the potential topological producibility tests were carried out using the MeneTools suite (v3.
As the gapfilling process is based on heuristics, it was decided to use filtered repair networks corresponding to MetaCyc subsets. They were built and queried successively, independently, and then jointly until the entire MetaCyc database (v23. 0) was used. The MetaCyc subsets were divided into two main categories. On the one hand, the subset was composed of reactions reported in fungal reconstructions (i. e. using the following 3 PGDBs available on MetaCyc-Candida albicans SC5314, Exophiala dermatitidis NIH/UT8656 and Saccharomyces cerevisiae S288c -and a fungi meta-network  [44] ). On the other hand, the subset was composed of reactions for which no genomic data is associated (i. e. spontaneous reactions or unknown enzymes). Gap-filling processes are performed iteratively from the targets with the highest confidence on the most relevant repair networks.
Acknowledgments The authors gratefully acknowledge the financial support of the  French National Research Agency (ANR) , project number  ANR-18-CE43-0013. We gratefully thank  Erwan Corre  ( Sorbonne Universite, CNRS , FR2424,  ABiMS, Station Biologique de Roscoff, Roscoff, France ) and  David Touboul  ( UniversiteParis-Saclay, CNRS ,  Institut de Chimie Des Substances Naturelles, UPR 2301, Gif-Sur-Yvette, France ) for their valuable advice. We also gratefully acknowledge  Jeanne Got  and  Anne Siegel  from the  Institute for Research in IT and Random Systems (IRISA ) for their helpful advice and for generously sharing their data. Furthermore, we express our gratitude to the  Roscoff Bioinformatics  platform ABiMS (http://abims. sb-roscoff. fr),.
Approach. The proposed method is based on precomputed databases of microdosimetric parameters and DNA damage distributions generated using the Geant4-DNA Monte Carlo Toolkit. First, a specific energy z was assigned to each cell of an irradiated population for a particular absorbed dose D , abs following microdosimetric formalism. Then, each cell was assigned a realistic number of DNA damage events according to the specific energy z, respecting the stochastic character of its occurrence. Main results. This study validated the MINAS TIRITH tool by comparing its results with those obtained using the Geant4-DNA track structure code and a Geant4-DNA based simulation chain for DNA damage calculation. The different elements of comparison indicated consistency between MINAS TIRITH and the Monte Carlo simulation in case of the dose distribution in the population and the calculation of the amount of DNA damage.
However, MINAS TIRITH only considers the variability of cellular responses related to the distribution of energy deposition and not to cell nucleus shape variability (geometry or volume) within the cell population or other types of individual variability. Thus, to quantify this energy deposition variability within the same cell population, a microdosimetry formalism was applied  (Kellerer and Chmelevsky 1975a , 1975b , Rossi and Zaider 1996). The method implemented in MINAS TIRITH is based on the combination and sampling of the spectra. In brief, the first set of spectra was reconstructed from a database calculated through microdosimetric simulations using Geant4 and Geant4-DNA, whereas the second set was calculated based on the results of the IRSN radiation-induced damage simulation chain.
Materials and methods 2. 1. Construction of the databases 2. 1. 1. The microdosimetric database In the first step, a database of microdosimetric quantities was built for monoenergetic particles: electrons from 1 keV-21 MeV, protons from 10 keV to 20 MeV, and alpha rays from 10 keV to 21 MeV. Geant4  (Agostinelli et al 2003 , Allison et al 2006 , 2016)  and Geant4-DNA tools  (Incerti et al 2010a , 2010b , Bernal et al 2015 , Incerti et al 2018) , version 10. 6, were used to build this database. For each sampled primary energy and for each particle type, a simulation was performed including a target volume corresponding to the mean geometry of a human endothelial cell nucleus that was represented by an elliptical cylinder measuring 2 μm in height, with half major axis of 9. 5 μm and half minor axis of 5.
CL stop i The population y CL stop i was considered non-negligible (proportion greater than 5%) for electrons between 1 keV and 16 keV, for protons from 10 to 800 keV, and for alphas from 10 keV to 3. 5 MeV. From these two populations, the f y CL exit ( ) and f y CL stop ( )spectra were constructed for each sampled energy and particle type. These spectra were then integrated, spline-smoothed, and scaled into 1909 points with ordinates between 0 and 1 to constitute the spectra databases F y CL exit ( )and F y.
CL stop ( ) The number of points was owing to a dynamic sampling step that was necessary to ensure a correct reconstruction by interpolation of sigmoid-shaped curves. Its value was obtained after several trials to determine the best compromise for the reconstruction. Simultaneously, over the energy and particle range where the population of stopped particles in the elliptical cylinder is non-negligible, a database of F CL stop ( )was obtained for the chord lengths using the same method as described for F y. CL stop ( ).
The damage data base In parallel, a database of damage was built for different mono-energies sampled between 10 keV and 20 MeV for protons, 10 keV-21 MeV for alpha rays, and 1 keV-1 MeV for electrons. To construct this database, a nanodosimetric simulation chain developed previously was used  (Meylan, et al 2016) , coupled to the Isochore cell nucleus geometry  (Thibaut et al 2022). This simulation chain facilitated the calculation of the topology of radiation-induced damage at the nuclear scale through the coupling of realistic DNA geometries to nanodosimetric simulations, using the track structure code Geant4-DNA, version 10. 6. The number of simulated particles for each sampled energy was chosen to have sufficient statistical power: 25 000 for electrons, 1500 for alphas, and between 1500 and 10 000 for protons depending on the LET.
Regarding the irradiation geometry, in case of the microdosimetric database, the particles are emitted from the surface of the nucleus, in an isotropic manner. In these simulations, the number of radiation-induced damages (nDSB , i nSSB i ) was determined for each track of each projectile type. The chord length (CL i ) and imparted energy ( i e ) of each track in the nucleus were extracted. Based on the data extracted from these simulations, spectra in terms of nDSB/e and nSSB/e (denoted F nDSB/ ( ) e and F nSSB/ ( ) e ) were constructed for each sampled energy. In addition, the cumulative probability spectra of DSB and SSB occurrence as a function of chord length (denoted F CL DSB ( )and F CL SSB ( )) were established.
Because of these elements and based on the microdosimetric database, MINAS TIRITH realistically distributes the number of tracks for each type of particle p in the phase space observed by each cell nucleus in the population according to the following method: i. The geometric chord length spectrum f CL , p geo ( ) of the irradiation was calculated from the dimensions of the endothelial cell nucleus model and the f p ( ) q spectrum. This geometric chord length corresponds to the distance between the entrance and exit points in the direction of the particle. ii l l l l ( ) ( ) ( ( )) ( )     å åå = ´- + ´= = = where m nucleus is the mass of the endothelial cell nucleus, F CL j geo stop p E , l ( ( ) )  is the chance for the particle to stop in the cell nucleus geometry, and thus F CL 1 j geo stop p E , l ( ( ) )  - is the chance for an exit.
Approximations and assumptions made by the method The method of damage number and type distribution in the cell population proposed here has several limitations and approximations: • The cell nucleus geometry is fixed because the database must be simulated for a particular geometry. Currently, only the geometry representing the mean size of an endothelial cell nucleus has been considered; however, other databases can be calculated in the future. However, the variability of the nuclei size within the same population was not considered by the MINAS TIRITH tool. However, the method proposed here is applicable to other cell nuclei geometries. • The tool decorrelated the energy component to the angular component of the phase space. This parameter is important because it influences the dose deposition.
Evaluation of the reconstruction of spectra by interpolation First, it is necessary to ensure that the interpolation reconstruction from the spectra calculated using the Geant4 and Geant4-DNA codes is accurate. To assess the reliability of the reconstruction method over the entire energy and particle range of the database, the following method was established for each energy E l and particle p: i. The F x p E , l ( )spectra were removed from the database. Here, x represents the interpolated observable type (y , CL nDSB/e or nSSB/e). ii. The F x p E , l ( ˆ) spectra were calculated through interpolation from the other spectra in the database. iii. The mean absolute percentage error (MAPE) between the N x n p E , l  and x n p E , l was assessed using MAPE 1 N x x x. p,E n 1 N n p,E n p,E n p,E l l l l | |  å = - = The MAPE indicator was chosen to quantify the reliability of the interpolation reconstruction (De Myttenaere et al 2016).
An interpolated reconstruction was considered reliable when the MAPE was less than 15%. The fact that this indicator is expressed in terms of percentage assigns the same importance to all points in our spectrum. Moreover, it is important to note that this method overestimates the error. For the calculation of each MAPE , p E , l the worst case was considered because the F x p E , l ( )spectrum was removed from the database. During the normal use of MINAS TIRITH, a point p E , l ( ) has never been estimated far away from its neighbors during this test.
The phase space of the secondary particles in this irradiation campaign was obtained as follows: i. The geometry of the AMANDE neutron source was created using MCNP  (Goorley et al 2012) , and the neutron creation probability was defined using NeuSDesc software (Birgersson and Lövestam 2009) (figure  1 ). In the first phase space, primary neutrons were recorded from the MCNP PTRAC file with their energies and directions. ii. In the second simulation, using Geant4, these neutrons were transported through a volume corresponding to the dimensions of the culture medium of the irradiated cell population inside a polystyrene flask (figure  1 ) placed at the surface of a cubic PMMA phantom with sides measuring 15 cm. Consequently, the positions, directions, and energies of the secondary particles were obtained through this simulation.
Evaluation of the number of damages by track sampling method In the third step, it must be verified that the method for distributing the number of damages for each track yields results that are consistent with those of the simulation chain  (Meylan et al 2016). Therefore, the mean number of damages per track obtained using the MINAS TIRITH tool was compared with that obtained using the simulation chain developed at IRSN for one cell nucleus. Thus, the Monte Carlo simulations that facilitated the creation of the damage spectrum database were reproduced using the MINAS TIRITH tool. Subsequently, the mean number of damages per track was compared for each simulation at each energy. In addition, this validation step enabled the evaluation of the time-saving factor (CPU 16, Mem 31 Go) allowed by MINAS TIRITH compared to the Monte Carlo simulation, according to the type and initial energy of the simulated particles.
Distribution of the specific energy in the cell population Figure  4  presents microdosimetric spectra resulting from the simulation of the secondary particle transport of the 15. 1 MeV mono-energetic neutron irradiation of the endothelial cell population. The f z 1 ( ) and zf z 1 ( ) spectra obtained by Monte Carlo simulation of the secondary particle transport with the Geant4/Geant4-DNA code and those obtained with the MINAS TIRITH tool were compared and found to be consistent. However, certain differences were observed. For instance, an over-representation of tracks with a z between 0. 15-0. 3 MeV μg -1 and between 4-8 MeV μg -1 with the MINAS TIRITH tool was observed. Moreover, a very slight underrepresentation of tracks with a z occurred between 20 and 100 MeV μg -1. Simultaneously, the major difference between the zf z 1 ( ) spectra from the two simulation methods was related to the tracks with a z between 20 and 100 MeV μg -1.
Discussion The MINAS TIRITH tool is a solution for distributing radiation-induced DNA damage in a cell population while respecting the stochastic character of energy deposition. This type of modeling typically requires the use of a Monte Carlo track-structure code coupled with realistic DNA geometries. However, these simulations are expensive in terms of time and computational resources and are not applicable at the scale of a cell population (typically 10 5 cells per part of a flask of endothelial cells). Therefore, it is impossible to verify the results provided by the MINAS TIRITH tool through direct comparisons with those provided by the Monte Carlo methods. Thus, to verify the MINAS TIRITH tool, a step-by-step validation of the method was required. The MINAS TIRITH tool distributes the tracks in the cell population with respect to the stochastic character of the energy deposition from the reconstructed spectra.
First, a nearly perfect match of the f z 1 ( ) spectra was noticeable, with some discrepancies observed for the z spectra between 0. 15-0. 3 MeV μg -1 and between 4-8 MeV μg -1. Simultaneously, the zf z 1 ( ) spectra were similar for the two methods. It can be concluded from these observations that MINAS TIRITH faithfully reproduced the distribution of tracks in the cell population, respecting the dosimetric characteristics for each particle type. The MINAS TIRITH tool is based on a spectral sampling approach for the distribution of tracks and the associated damage. The spectra involved in this method were reconstructed via interpolation from a database. The reliability validation of this reconstruction facilitated the validation of this step of the method. Figure  2  shows that the F y CL ( )  spectra were faithfully reconstructed with respect to the values of the MAPE indicator over the entire energy range and for every light-charged particle type.
Further, (δ) ConnectionLens graphs interconnect data through extracted entities; like any trained model, our extractors introduce some errors (false positives, false negatives, wrongly typed entities). In some cases, especially when a dataset has some regularity, users can provide guidance on what entities are to be expected in certain parts of the data, thus contribute to increasing the graph quality. To articulate such guidance, they need to be able to inspect the data, and to formulate extraction hints. Moreover, ( ) journalists formulated the need for tangible, intuitive data analysis results (diagrams, graphs, tables) that they can download from our graph data lake and share, for instance, within newsrooms, to convince colleagues or managers of the interest of spending time to analyze complex data.
Moreover, when two NE nodes are identical, i. e. they have the exact same label, they are fused and only one is kept in the integrated graph. This allows to easily find connections across sources, that are (much) harder to find manually. For instance, there is only one node "Thales", extracted from N18 and N43. Figure  1  shows the data graph obtained from (i) an XML sample of the large HATVP French transparency dataset (ministers' declarations about their wealth, stocks they own, business interests, etc. ), on the left; and (ii) a CSV file listing the 40 most influential French companies (known as CAC40), on the right. Each (black) circle is a data node in the integrated data graph, edges are connecting them accordingly to their relationships in the datasets. Value data nodes are quoted; named entities are highlighted (blue for people, purple for dates and yellow for organizations).