dataesr/scientific-paragraphs-categorization huggingface dataset:
The atmospheric model The non-hydrostatic AROME NWP model is used in this study, with the same forecast configuration as the one operationally used at Météo-France in 2016 (AROME-France, cy41t1,  Seity et al. , 2011; Brousseau et al. , 2016)  with a 1. 3 km horizontal resolution and a domain centered over France (Fig. 1 ), which notably covers the northwestern Mediterranean Sea. The AROME orography is extracted from the Global 30 Arc-Second Elevation Data Set (GTOPO30) database  (Gesch et al. , 1999). The vertical grid has 90 hybrid η levels with a first-level thickness of almost 5 m. The time step is 50 s. In AROME, the advection scheme is semi-Lagrangian, and the temporal scheme is semi-implicit. The 1. 5-order turbulent kinetic energy scheme from  Cuxart et al. (2000)  is used. Due to its high resolution, the deep convection is explicitly solved in AROME, whereas the shallow convection is solved with the eddy diffusivity  Kain-Fritsch (EDKF, Kain and Fritsch, 1990)  parameterization.
The ICE3 one-moment microphysical scheme  (Pinty and Jabouille, 1998)  is used to compute the evolution of five hydrometeor species (rain, snow, graupel, cloud ice and cloud liquid water). Radiative fluxes are computed with the  Fouquart and Bonnel (1980)  scheme for short-wave radiation and the RRTM (Rapid Radiative Transfer Model,  Mlawer et al. , 1997)  scheme for long-wave radiation. The surface exchanges are computed by the SURFace EXternalisé (SURFEX) surface model  (Masson et al. , 2013)  considering four different surface types: land, towns, sea and inland waters (lakes and rivers). Output fluxes are weightaveraged inside each grid box according to the fraction of each respective tile defined with physiographic data from the ECOCLIMAP database  (Masson et al. , 2003)  before being provided to the atmospheric model at every time step.
A no-slip lateral boundary condition is applied, and the bottom friction is parameterized by a quadratic function with a coefficient depending on the 2D mean tidal energy  (Lyard et al. , 2006; Beuvier et al. , 2012). The diffusion is applied along isoneutral surfaces for the tracers using a Laplacian operator with the horizontal eddy diffusivity value ν h fixed at 15 m 2 s -1. For the dynamics (velocity), a bi-Laplacian operator is used with the horizontal viscosity coefficient η h fixed at 1. 10 8 m 4 s -1. The time step is 120 s. The runoff forcing consists of daily observations for 25 French rivers around the northwestern Mediterranean Sea (see  Sauvage et al. , 2018 , for the complete list) collected from the Banque Hydro database (hydro. eaufrance. fr) and in the monthly climatology of  Ludwig et al.
3c ). These differences are induced by both the spin-up strategy and the restart mode of NEMO for each forecast run. Indeed, the spin-up (without assimilation) makes NEMO-NWMED72 slowly diverging from PSY4 but also allows it to produce its own finescale structures permitted by its resolution (1/72 • ) and in response to the AROME-France high-resolution atmospheric forcing, whereas directly using the PSY4 3D fields would have let the ocean model adjustment affect the short-range forecast. The choice to restart NEMO for coupled forecasts from the spin-up first and then from a previous forecast was also made to be close to the cycling done in an operational context, i. e. , using a previous forecast as initial conditions for the surface scheme (and as a background for the AROME 3D-Var data assimilation scheme, not done here).
The sources for wave-atmosphere coupling within the SURFEX-OASIS interface and the WASP parameterization will be included in the next release (v9) of SURFEX but can be provided on demand by the authors for older SUR-FEX versions (back to v7_3). Outputs from all simulations discussed here are available upon request to the authors. The Antilope product can be made available for research purposes upon request (contact: olivier. laurantin@meteo. fr). The surface weather station data and the chains of thermistors on the Lion and Azur Météo-France moored buoys are available in the MIS-TRALS/HyMeX database (https://mistrals. sedoo. fr/, last access: 5 August 2021) (MISTRALS, 2021) after subscription. Oceanographic buoy data and the PSY4V3R1 daily analyses of Mercator Ocean International are available through the Copernicus Marine Environment Monitoring Service (CMEMS) portal (https://marine.
Computation scaling on Météo-France HPC for a 42 h-range forecast. :23:00 247-08:45 consortium and will be included in the cycle 48 Météo-France official release. The source codes of the other components are available online. -WaveWatchIII was used in version 5. 16, which is distributed under an open-source style license through a password-protected distribution site at https://polar. ncep. noaa. gov/waves/wavewatch/ (last access: 5 August 2021) (NOAA/NCEP, 2021a). Since version 6. 07, WaveWatchIII is distributed using GitHub (https://github. com/NOAA-EMC/ WW3, last access: 5 August 2021) (NOAA/NCEP, 2021b) without any username and password required to access the software package. -NEMO is available at https://www. nemo-ocean. eu/ (last access: 5 August 2021) (NEMO, 2021) after user registration on the NEMO website.
Acknowledgements. This work is a contribution to the  HyMeX program  (Hydrological cycle in the Mediterranean EXperiment -https://hymex. org,last access: 5 August 2021) through INSU-MISTRALS support. The authors acknowledge the  Occitanie French region  for its contribution to  César Sauvage 's PhD at CNRM. The authors gratefully acknowledge  Véronique Ducrocq  ( Department of Operations for Prediction (DirOP) of Météo-France & CNRM ) for motivating, carefully following and promoting this work and for her encouragements during the redaction process. The authors acknowledge the MISTRALS/HyMeX database teams (ESPRI/IPSL and  SEDOO/OMP)  for their help in accessing the surface weather station data and the chains of thermistors on the Lion and  Azur Météo-France  moored buoys. The authors finally thank  Olivier Laurantin  from the  Observing System Department (DSO) of Météo-France , who provided the Antilope product.
Background: Exposure to endocrine disrupting chemicals (EDCs) represents a critical public health threat. Several adverse health outcomes (e. g. , cancers, metabolic and neurocognitive/neurodevelopmental disorders, infertility, immune diseases and allergies) are associated with exposure to EDCs. However, the regulatory tests that are currently employed in the EU to identify EDCs do not assess all of the endocrine pathways. Objective: Our objective was to explore the literature, guidelines and databases to identify relevant and reliable test methods which could be used for prioritization and regulatory pre-validation of EDCs in missing and urgent key areas. Methods: Abstracts of articles referenced in PubMed were automatically screened using an updated version of the AOP-helpFinder text mining approach.
To this end, a computational strategy was developed, and then used to explore the scientific literature by text-mining (TM). Therefore, we took advantage of a recently developed tool called AOP-helpFinder based on artificial intelligence (AI), which uses available text information to automatically identify and extract links between items from dictionary lists (i. e. EDC and test methods). This bioinformatics approach was combined with a manual screening of databases (DBs) and of existing guideline documents in order to capture as much as possible existing knowledge.
The AOP-helpFinder tool for the identification of EDC tests AOP-helpFinder, a recently developed tool which is based on artificial intelligence (AI), uses available text information to automatically identify and extract links between items from dictionary lists. AOP-helpFinder is a hybrid approach that combines the Natural Language ToolKit procedure (NLTK) and Dijkstra graph theory  (Carvaillo et al. 2019). Initially, AOP-helpFinder was developed to identify relevant associations between a given substance (or list of substances) and biological events present in Adverse Outcome Pathways (AOP)  (Jornod et al. 2020; Rugard et al. 2020 ). In the present study, the AOP-helpFinder version 2  (Jornod et al. 2020) , which was designed, initially, to preprocess and screen scientific abstracts extracted from the PubMed DB, was adapted to automatically retrieve reliable associations between EDCs and test methods.
Screening of databases Several data sources were considered and checked in order to identify as many assays as possible (validated or not) for EDCs (Table  S2 ). Some DBs, which are more chemical based (CompTox, OpenFoodTox, T3DB and ToxNet), were not explored further due to their website structures which were not compatible with and/or were not relevant to our study. Thus, eight DBs were screened (see Table  S2  for the full list and the specificities of the data sources evaluated). Each compiled bioassay (collected information and reference codes from different sources) was manually checked and cleaned when necessary to avoid redundancies in the final table.
A note was added when the test method was changed or discarded by regulatory authorities. For all of the categories, the compiled information was preserved using several columns for data source details. For both, 'excluded' and 'validated and on-going' tests, the first 19 columns were dedicated to the codes and the dates of the bioassay source(s). An assay was often found in various sources and under different versions. Our results tables were designed to clearly display the optimal amount of source information that we were able to gather. The first column contains the OECD TG number if the bioassay is a standardized test from the OECD Test Guideline Program, the second column mentions the number of associated OECD GD(s) (if available), the third column mentions the dates of previous versions of the OECD TG and the fourth column the date of the final version.
Screening of the test guideline documents and databases We manually explored available open access databases such as test guidelines (i. e. OECD & EPA), nationally recognized institutions documents (e. g. JaCVAM, BraCVAM, KoCVAM etc. ) and specialized databases (e. g. DB-ALM, TSAR etc. ). The decision tree workflow used for the DB exploration, as well as for the analysis of articles during manual curation is described on Fig. S1. Then, all identified tests were checked against three guideline sources (i. e. OECD test guidelines (OECD TG), OECD guidance documents (OECD GD) and EPA OCSPP guidelines (all last accessed as of May 11, 2020), define their validation status (i. e. 'validated assays', 'assays under ongoing validation', 'assays with withdrawn validation' and 'non-validated assays').
Data preparation A total of 10,651 and 11,238 scientific abstracts related to 'Endocrine Disruptors' and 'Endocrine Disrupting Chemicals', respectively, were identified using both keywords and extracted from the 30 million publications in the PubMed DB (as of May 6, 2020). As expected, even if the first article on ED was published in 1992, most of the publications were more recent for both terms (only 95 articles for EDs before 2000 and 142 for EDCs). Using the DOI of the selected abstracts, the two lists were merged to remove duplicates. A large number of abstracts overlapped (9715 articles). 12,175 unique abstracts were retained for further analysis. In addition, as the main objective was to identify non-validated experimental test methods, abstracts which contained exclusion criteria terms (e.
Characterization of non-validated test methods As a result of the screening, using both sources (literature and DBs) and the information obtained from the test guidelines and guidance documents, only 113 and 121 tests were not yet validated from the literature and the DBs, respectively. Half of the tests were identified from the PubMed DB using TM (113 out of 226), illustrating the complementary of using both approaches. Further analyses were performed on these 226 test methods (i. e. , test method without exclusion criteria). Very few overlaps were found (8, between the literature and different DBs) which highlights the need to explore various data sources as they appear to be complementary. Among DBs, ToxCast (64 selected assays) and DB-ALM (30 selected assays) were the major sources of non-validated test methods, followed by PubChem 13), TSAR (9), Watchfrog (4) and ISO (3) (Fig.
This approach is highly dependent on the available information (e. g. , what is reported in the publication and the documentation, keywords used when submitting manuscripts, etc. ). Although the available literature allowed the identification non-validated tests (>100), this information may be limited since papers often are not centered on assays and the description of assays frequently are not extensive. Databases are much more informative as to the description of  assays. Furthermore, the TM procedure is limited by the terms used for the searches and the structure of the abstracts. Therefore, it is critical to have multi-disciplinary experts involved in the creation of the dictionaries in order to be able to retrieve as much information as possible. The main outcomes present in the non-validated EDC test methods that we identified in the different DBs were related to the reproductive system, the growth/developmental systems, lipogenesis/adipogenicity, thyroid, steroidogenesis and liver metabolism-mediated toxicity, and frequently involved the androgen receptor, thyroid hormone receptors, glucocorticoid receptors and the aryl hydrocarbon receptor.
Conclusion This study provides, to our knowledge, the first characterization of existing relevant test methods to assess suspected EDCs. The identified list is a preliminary step, that will need to pass through other filtering in order to prioritize the most relevant tests for pre-validation, with a final aim of application in regulation. Using diverse data sources, a computational strategy combined with manual curation allowed the retrieval of 259 test methods for EDCs that are not yet validated. These findings can be used to select and then prioritize methods to be pre-validated and subsequently validated for EDC characterization. This approach will assist in filling in the gaps in knowledge concerning the modes of action or adverse effects of these substances. In addition, it will accelerate and improve EDC testing as developed in the EURION EU cluster and,.
Materials characterization Surface nano-features and CoO NPs sizes were investigated with a JEOL ARM-200F transmission electron microscope (TEM) working at 200 kV. Several TEM images and Image J software were used to determine the particle size distribution by counting between 270 and 2700 particles, depending on the density of population on each material. The chemical composition of the coatings was determined by EDX mapping (coupled to Scanning-TEM) with a JED 2300 detector. Complementary microstructure analysis was performed by Raman spectroscopy in a backscattering geometry using a LabRAM BX40 (Horiba Jobin-Yvon) micro-spectrometer equipped with a diode pumped solid-state laser excitation source (Compass 315 M-50, λ = 532 nm). The crystalline structure of the materials was investigated by grazing incidence α i = 1 • X-ray diffraction (GI-XRD) using a powder diffractometer D8 ADVANCE A25 from Bruker.
Grazing incidence X-ray diffraction analyses The X-ray diffraction patterns recorded on S1-S5 coatings are presented in Fig. 5. a. They clearly identify the broad peaks centred around 25 • as the (002) reflection planes of graphite, indicating disordered-like materials with a low graphitization level of the carbon coatings. The presence of CoO NPs inside an amorphous carbon matrix was confirmed by GI-XRD investigations. Diffraction patterns recorded for samples S3 to S5 revealed, even if diffraction data remain a little noisy, increasing CoO peaks (COD 1541662  [43] ), in agreement with Raman analyses. Indeed, the presence of (111) and (200) reflection planes revealed the formation of face-centered-cubic phase of cobalt oxide nanostructures  [44]. Zhou et al. [45]  also reported the formation of composite of CoOamorphous carbon matrix, during synthesis of cobalt nanoparticles supported on nitrogen-doped porous carbon nanowires at calcination temperatures below 600 • C due to incomplete carbonization and reduction.
Indeed, at this temperature many oxygen functional groups are still present on the materials as indicated by the oxygen content (Table  1 ) and EDX mapping, which hinder the complete reduction of the CoO particles into metallic Co. The (200) reflection, which is the most intense, was selected for an individual profile fitting using WinPlotr software  [46]. Data where initially converted into suitable format via PowDLL  [47]. According to the assumptions of Scherrer's formula, Gaussian profile shape was selected (Fig. 5. b). The results led us to get some basic information about intensities and FWHM evolutions within the samples (S3, S4 and S5). Main results are summarized in Table  2. Even if the estimated standard deviation is quite important (Fig. S5. a in SI section), especially for S3, data analyses evidenced an increase of diffraction peaks intensity from S3 to S5, therefore, with CoO content within coatings.
The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model's ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.
Introduction In the age of high-throughput sequencing technologies, cancer is considered to be a genetic disease for which driver genes are constantly being discovered  [1]. The study of mutational and molecular patterns in cancer patients aims to improve the understanding of oncogenesis. However, many of these gene alterations seem to be specific to certain cancer types  [2]  or exhibit different behaviors depending on the molecular context, particularly in terms of response to treatment. This prompted a shift from univariate biomarker-based approaches to more holistic methodologies leveraging the various omics data available. To study these observed differences in drug response in various cancers, some approaches based on mathematical modeling were developed to explore the complexity of differential drug sensitivities.
A number of machine learning-based methods for predicting sensitivities have been proposed  [3] , either without particular constraints or with varying degrees of prior knowledge; but they do not provide a mechanistic understanding of the response. Some other approaches focused on the description of the processes that might influence the response by integrating knowledge of the signaling pathways and their mechanisms and translated it into a mathematical model  [4] [5] [6]. The first step of this approach implies the construction of a network recapitulating knowledge of the interactions between selected biological entities (driver genes but also key genes of signaling pathways), extracted from the literature or from public pathway databases, or directly inferred from data  [7]. This static representation of the mechanisms is then translated into a dynamical mathematical model with the goal to not only understand the observed differences  [5]  but also to predict means to revert unexpected behaviors.
One way to address issues related to patient response to treatments is to fit these mechanistic models to the available data, and to train them on high-throughput cell-line specific perturbation data  [4, 5, 8]. These mechanistic models are then easier to interpret with regard to the main drivers of drug response. They also enable the in silico simulations of new designs such as combinations of drugs not present in the initial training data  [6]. However, these mechanistic models contain many parameters that need to be fitted or extracted from the literature. Some parsimonious mathematical formalisms have been developed to make up for the absence of either rich perturbation data to train the models or fully quantified kinetic or molecular data to derive the parameters directly from literature.
One of these approaches is the logical modeling, which uses discrete variables governed by logical rules. Its explicit syntax facilitates the interpretation of mechanisms and drug response  [9, 10]  and despite its simplicity, semi-quantitative analyses have already been performed on complex systems  [11]  for both cancer applications  [9, 12]  and drug response studies  [13, 14] , and have proved their efficacy  [15, 16]. The nature of this formalism has shown its relevance in cases where the model is not automatically trained on data but simply constructed from literature or pathway databases and where biological experiments focus on a particular cell line  [17]. We propose here a pipeline based on logical modeling and able to go from the formulation of a biological question to the validation of a mathematical model on pre-clinical data, in this case a set of cell lines (Fig 1 ), and the subsequent interpretation of potential resistance mechanisms.
A logical model summarizing the main molecular interactions at work in colorectal cancers and melanomas is thus built from the literature and completed with databases. As previously mentioned, the objective is to understand whether it is possible to model and explain differences in responses to BRAF inhibition in melanoma and colorectal cancer patients using the same regulatory network. The fact that the two cancers share the same network but differ from the alterations and expression of their genes constitute our prior hypothesis. We then use model checking tools to verify the consistency of this model with a series of qualitative assertions retrieved from literature. Finally, we use available public omics data from these cancer cell lines to transform the generic model into personalized cell-line models.
This step is based on the previously published PROFILE method and extends it to the interpretation of treatment responses. The relevance of the personalized models is validated by their ability to recover the differences in BRAF inhibition sensitivities, from both drug and CRISPR screenings. The study therefore presents a global approach, from the biological question to the validation of personalized qualitative models, which links data and knowledge and integrates in particular two methodological innovations: a computer-readable model-checking approach for logical models in the MaBoSS formalism  [11] , and the interpretation of drug treatments using personalized models.
PLOS COMPUTATIONAL BIOLOGY biological reality: if a node represents a gene, its activation can be interpreted biologically as the transcription of this gene; for a node representing a protein, its active and inactive states can be interpreted as the phosphorylated or non-phosphorylated states of the protein. Ideally, the level of node activation should reflect these different mechanisms and be inferred from the most appropriate data types. Here, in order to integrate data directly into logical models, we vary the speed of the reactions according to the data from the patients or cell lines as described below. In the present article, all simulations are performed according to asynchronous updating with MaBoSS software  [11, 31]. This algorithm, using continuous time Markov chain simulations on the Boolean network, provides a stochastic way to choose a specific transition among several possible ones.