dataesr/scientific-paragraphs-categorization huggingface dataset:
Data from EXCEL files are uploaded into the BaGaTel database. Once the ontology has been built, the specific vocabulary has been fixed, and the data and knowledge collected have been integrated in a common database, the question of their querying and analysis for decision support issues can be addressed. The database and the vocabulary of the ontology have been designed for two complementary applications. The first one is to allow researchers in food science to answer research questions. The second is to help food producers formulate food products that answer different constraints (nutritional, sensory, and environmental). The structure of the database and the vocabulary had to be suitable to facilitate the querying of data by both researchers and food producers. The objective of this paper is to demonstrate the ability and the suitability of the developed tools to provide relevant data in response to questions which involve multi-criteria indicators.
Several questions in the context of formulation and environmental concerns are addressed. In Section 2, this paper presents the different data available on dairy products in BaGaTel database, and more specifically on hard cheeses, with a focus on the parameters used to formulate the requests. Section 2 also describes briefly PO² ontology. Section 3 reports on how the use of BaGaTel database structured according to PO² allows answering to specific questions relating process, product quality and eco-design parameters.
Available data and addressed questions There are a significant number of research projects conducted on hard cheeses, from which data were successfully imported into the BaGaTel database following the PO² structure. Complementary data were imported into the field of in-mouth food breakdown, aroma and taste compounds release and sensory acceptability from projects done on model cheeses similar to hard cheeses  (Bigaski Ribeiro et al. , 2016; Boisard et al. , 2013 Boisard et al. , , 2014;; Feron et al. , 2014; Gierczynski, Labouré, Sémon, & Guichard, 2007; Guichard et al. , 2017; Lawrence et al. , 2011; Mosca, Andriot, Guichard, & Salles, 2015; Phan et al. , 2008; Tarrega, Yven, Sémon, & Salles, 2008 , 2011). These projects covered a wide range of cheese composition (e. g. , lipid, protein, salt, microorganisms), technical processes from very simple model systems to real cheese production, cheese structure at different scales, sensory perception (i.
e. , aroma, taste and texture), in-mouth breakdown (e. g. , masticatory parameters, food bolus structure), as well as in vitro and in vivo aroma and taste compounds release. Other data from a process eco-design project (CellExtraDry; Eco-design preservation processes of functional yeast enriched with anti-oxidants. Funded by ANR-IC-Qualiment) were also imported into BaGaTel, covering inputs and outputs used for the transformation processes (e. g. , consumptions and wastes of energy, water, steam) of stabilised micro-organisms, together with quality data for yeasts (e. g. , enumeration, antioxidant capacity), the processing conditions (e. g. , temperature, pressure) and resulting environmental impacts computed by LCA. The summary of CellExtraDry project is provided as Supplementary material file SF4.
Energy consumption for processes and processing conditions in the case of hard cheeses are also available from TRUEFOOD  (EU-FP6-FOOD-16264: 2006 -2010)  and SmartRipe (EU-FP7-KBBE-613827: 2013-2015) projects. As not all parameters were measured at the same time on the same cheese sample, the aim of this study is to investigate how and to what extent it is possible to benefit from data integrated from different projects to answer to specific questions. Questions focus on relationships among composition, sensory quality and rheological properties of hard cheeses, and also on their environmental impact. The first three questions addressed the problem of formulation by combining data about cheese composition during production, sensory quality and rheological parameters. In fact, sensory description and rheological parameters are commonly used in the literature to assess the quality of cheese samples differing in composition and process  (Foegeding, Brown, Drake, & Daubert, 2003; Hailu et al.
Life Cycle Assessment methodology LCA can be divided in four steps: goal and scope definition, data inventory, impact assessment and interpretation  (ISO 14040:2006). Step 1: Goal and scope definition. This phase consists in defining the objectives of the study, system boundaries, functional unit, data needed, assumptions and limits. The goal of the LCA presented in this paper was to show to what extent the BaGaTel database can help LCA practitioners perform the data inventory step. A simplified LCA was thus performed on a specific sample to illustrate the contribution of BaGaTel in a LCA approach. The system boundaries exactly fitted the system described in section 2. 1. 2. The functional unit was the production of one cheese of 10. 5 kg ripened for 120 days, corresponding to the use of 115 L of raw milk.
The data needed mainly came from data included in databases, no complementary measurement was performed. Because of the production process of the sample under study, no allocation had to be assumed. Obviously, such assumptions on data and allocation can limit the reliability of the obtained results. Nevertheless, this paper aims to show how the BaGaTel database can be helpful in LCA studies, but does not claim to provide thorough LCA results. Such a simplified approach was sufficient to achieve our goal. measured data, secondary data are obtained from calculations or databases (e. g. , Ecoinvent). The data inventory is detailed in the results section to show how the BaGaTel database can be useful to perform this step. Briefly, data used to perform the simplified LCA came from BaGaTel database, supplemented by Ecoinvent database.
However, for clarity reasons, only results on climate change, ozone depletion and acidification indicators are presented in the results section. System modelling and impact assessment were performed by using Simapro software (V8. 4. 0. 0 PRé consultant). Step 4: Interpretation. An LCA is an iterative process and the aim is to analyse results and hypotheses and discuss them at each step 1, 2 and 3. This will help decision makers to make an improved choice. This paper aims at showing how the BaGaTel database can be helpful in LCA studies, but does not claim to provide thorough LCA results. As a consequence, LCA assumptions and results are not deeply discussed in this paper.
Implementation of data In a first step, experts in food science, especially in food process, oral physiology, sensory perception and eco-design worked to develop a consensual vocabulary gathered in PO² ontology, which has been used for the structuring of BaGaTel. In a second step, data were utilised in BaGaTel from different collaborative national or international projects, theses and scientific publications from scientists involved in the NutriSensAl project (http://plasticnet. grignon. inra. fr/PortailNutriSensAl/). BaGaTel contains data for different types of parameters covering different steps of the process, raw materials (useful data to perform LCA), composition of the food product at the different steps, structure of the food product, evaluation of the environmental, sensory and nutritional qualities of the final product, taking into account food breakdown in the mouth and digestive track when relevant.
In a third step, data from different projects dealing with the same type of food product were compared. The challenge was then to identify data that were comparable. For example, concerning cheese products, measurements made using various devices and different calibrations of the device, sensory evaluations were made with various panels and on products from different sensory spaces, according to the specific project. Thanks to our network of scientific and technical experts in the field, it was possible to identify types of cheeses and parameters that could be compared among them. After selection of the most relevant parameters, with the aim of always having the same parameter and same unit of measurement, some transformations of data and adjustments were done. For example, all scores obtained in sensory analysis were standardised on a 10-point scale.
Combining data about hard cheese composition, process, sensory quality and rheological parameters We chose to focus on "hard cheeses"; these cheeses have common characteristics, such as composition, technological process and structure. In the BaGaTel database two projects, PRASEL and TRUEFOOD, on real hard cheeses had common steps in their processing: renneting, draining, pressing, moulding, demoulding, and ripening. By using the RDF integrated data, BaGaTel was queried with Q1 (Which hard cheese sample has the highest value for "taste intensity"?) by a SPARQL query shown in Supplementary material Fig. S1. The answer, presented in Supplementary material Fig. S2 , is sample "Fd24" that has a taste intensity of 6. 82/10. This sample also had the highest aroma intensity (6. 45/10). This sample belongs to TRUEFOOD project.
The answer to Q2 ("What is the composition of this sample at different steps in the process?") is given in Table  2. The lipid and protein content are given at three steps "milk reception", "in the vat" and "cheese ripening". Table  2  also shows the answer given by BaGaTel to Q3 ("What are the characteristics measured on this sample?"). The pH value has been measured at different steps ("moulding", "step in the vat", "demoulding") and the sensory properties have been measured at the "chewing" step. No rheological properties could be found in BaGaTel database to answer to Q3, while these parameters are important to evaluate the quality of cheeses. Hence, it was necessary to estimate this missing data using data from other projects imported into BaGaTel. The rheological parameters such as Young modulus have been demonstrated to be relevant to predict masticatory activity, especially with the aim of formulating food products for specific populations such as elderly people  (Engelen, Fontijn-Tekamp, & Bilt, 2005; Peyron, Maskawi, Woda, Tanguay, & Lund, 1997).
Combining data about hard cheese composition, processing and environmental impact The other questions, Q4 and Q5, address environmental concerns to BaGaTel database. No environmental data have been collected for the Fd24 sample. Therefore, this section will show how BaGaTel database could be used to estimate environmental impact of sample Fd24. Environmental impact has been quantified by using LCA in the CellExtraDry project  (2014) (2015) (2016) , in the case of production of stabilised micro-organisms. BaGaTel database can be very useful to support life cycle inventory of data [step (ii) in LCA methodology] since it already contains data coming from LCA inventories and results from the CellExtraDry project. The answer to the question Q4 ("Which parameters need to be measured throughout the process to perform a Life Cycle Assessment of the sample?"), is summarised in Supplementary material Table  S1.
Measured parameters (e. g. , electricity consumption, mass of water, volume of compressed air) are obtained as well as the step when data related to these parameters have been collected. That provides detailed items that have been previously measured to perform LCAs. LCA practitioner then has to consider if these are relevant for their system, and if other similar items have to be included. For instance, the mass of saccharose used for the fermentation step in CellExtraDry project might not be relevant to a cheese production system. However, by analogy, a LCA practitioner can easily extrapolate this result to conclude that it would be important to measure the initial mass of raw milk. Combination of BaGaTel querying and expertise of a LCA practitioner is crucial to state all relevant data that have to be gathered.
This approach may help LCA practitioners to transfer knowledge from one domain to another domain by suggesting relevant parameters to be measured. This is possible thanks to the high-level description of the available data using the PO 2 ontology. The BaGaTel database can therefore be also queried to obtain relevant quantified data on specified items. For instance, from Supplementary material Table  S1 , it can be deduced that energy consumption is an important parameter, since it was measured at different steps (fermentation, centrifugation, drying). The query Q5, ("What are the values of the relevant parameters needed to conduct the Life Cycle Assessment?"), could be reformulated as "What is the energy consumption of a French hard cheese production process?". Unfortunately, no data were directly available in the BaGaTel database to answer this question.
Nevertheless, because PO 2 ontology also integrates material and methods related to the experiments, the BaGaTel database could be queried on this to evaluate the energy consumption during the different steps of hard cheese production presented in Section 2. 1. 2. For each step, the material used could be identified, duration of its use and power of the equipment. Then energy consumption was calculated by using Equation (3): Energy consumption = Power of equipment × Use duration of equipment Equation (3) Results are presented in Table  3. Mass flow data concerning sample Fd24 could also be obtained by querying BaGaTel database, these are presented in Table  4. Finally, using data described in Tables  3  and 4  showed that raw milk production and energy use during ripening were the main causes of environmental impacts of ripened cheese  (Berlin, 2002; Finnegan, Yan, Holden, & Goggins, 2017).
Finally, when a LCA practitioner has finished: LCA inventory data and LCA results can be stored in the BaGaTel database, together with all the corresponding metadata (e. g. system boundaries, functional unit, impact assessment method) necessary to eventually re-use them. From these inventory and results, LCA data could enrich existing databases (e. g. , LCA Food database, World Food LCA database, EcoInvent database) that are currently poorly documented with regards to impacts due to processes as a function of (i) the food product submitted to the process and (ii) the process operating conditions.
Conclusions This study demonstrates that the common vocabulary and structure provided by PO 2 ontology allow for combining and integrating data from different projects in a common database, BaGaTel, and therefore giving relevant answers to several questions. BaGaTel can add value to evaluate the relevance of a data set to be used for the estimation of missing data for a specific sample, taking into account the data available. The question addressing the formulation of hard cheese, BaGaTel database could be useful in estimate missing data on cheese rheology, by restricting the query to a set of samples with similar measured characteristics. On the question about ecodesign of a food product, it gave hints about relevant data that have to be acquired and could also be useful in estimate missing data.
Thanks to the fact that data on cheese quality, process and ecodesign are in the same database, it will now be possible to estimate missing data on the environmental impact of projects only focused on food quality and also estimate relevant parameters for food quality in samples from project only focused on eco-design. Such an approach is very useful for knowledge and data capitalisation, as well as to produce new knowledge and data by combining and integrating existing resources. As a consequence, the number of available observations in the database is a key-parameter for making BaGaTel structured by PO 2 valuable. At the same time, such a tool makes it possible to identify the lack of data, which have to be estimated or experimentally measured, to be able to answer challenging questions.
, supplemented by data and emission factors of Ecoinvent database, a simplified LCA could be performed. This simplified LCA referred to the production of one cheese of 10. 5 kg ripened over 120 days, corresponding to the use of 115 L of raw milk. A simplified LCA was performed, but not detailed, because this paper aims at showing how the BaGaTel database can be helpful in LCA studies, but does not claim to provide thorough LCA results. For this reason, LCA results will not be deeply discussed. Results for climate change, ozone depletion and acidification indicators are presented in Fig. 5. For clarity reasons, other indicators are not shown. Milk reception was the main contributor to climate change and acidification, as well as for ten other indicators (not shown), due to raw milk production.
An observation stored inBaGaTel allows one to have both raw and computed data about several different attributes such as pH, taste intensity or energy consumption of the equipment. under the license Creative Commons Attribution International 4. 0 International (CC BY 4. 0) (https://creativecommons. org/licenses/by/4. 0/). PO² ontology guided the structure of the BaGaTel database, as detailed in Supplementary material file SF5, integrating and storing all the available data presented in Section 2. 1. 1. These data are represented with the standard format Resource Description Framework (RDF). RDF integrated PO² ontology is implemented in OWL 2 (https://www. w3. org/2001/sw/wiki/OWL) and its core layer is available on the AgroPortal repository (http://agroportal. lirmm. fr/ontologies/PO2).
T2K experiment T2K is a long-baseline neutrino oscillation experiment in Japan. Neutrinos are sent to the 50 kton Super-K detector with a baseline of 295 km. Primary protons are extracted from the 30 GeV J-PARC proton synchrotron to the dedicated neutrino beamline, where protons collide with a carbon target to produce secondary mesons, mainly pions. These mesons decay in the 96 m long decay pipe to produce a tertiary neutrino beam. Depending on the current polarity of the magnetic focusing horns, the beamline can produce either ν µ -dominant ν-mode beam or νµ -dominant ν-mode beam. The neutrino beam simulation incorporates hadron production data from NA61/SHINE experiment at CERN  [55]. This analysis uses the ν-mode beam data from November 2010 to May 2013, resulting total statistics of 5.
Acknowledgment We thank the  J-PARC  staff for superb accelerator performance. We thank the  CERN NA61/SHINE Collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC  (Grant No. SAPPJ-2014-00031 ),  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN)  and  Ministry of Science and Higher Education , Poland; RSF,  RFBR , and  MES , Russia; MINECO and ERDF funds, Spain; SNSF and SERI, Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada , and GridPP in the United Kingdom. In addition, participation of individual researchers and institutions has been further supported by funds from  ERC  ( FP7 ), " la Caixa Foundation  ( ID 100010434 , fellowship code  LCF/BQ/IN17/11620050 ), the  European Unions Horizon 2020 Research and Innovation programme  under the  Marie Skłodowska-Curie  grant agreement no.
ROS assessment, H 2 O 2 detection and H 2 O 2 quantification General ROS were assessed using carboxy-H 2 DCFDA staining and a FACSCalibur flow cytometer (Becton Dickinson) and data were analyzed using Flowjo software. For compartment-specific H 2 O 2 detection, mammalian expression vectors encoding HyPer targeted to nucleus (with a nuclear localization signal), and mitochondria (with a mitochondrial matrix localization signal) were purchased from Evrogen. HyPer targeted to the cytosol with a nuclear export signal and HyPer targeted to the membrane using the membrane-targeting signal in protein GAP-4 were constructed in our laboratory. Cells were transfected with HyPer expressing vectors using jetPRIME reagents (Polyplus Transfection). Further experiments were performed 24 h after transfection.
Quantitative redoxome analysis Cells were subjected to different treatments and proteins were extracted. Purification of these proteins was performed according to a published protocol with slight modifications  [25]. Protein digests were analyzed in triplicate with an Orbitrap Fusion Tribrid equipped with an EASY-Spray ion source. Label-free liquid-chromatography tandem mass spectrometry (LC-MS/MS) acquisition was performed. Peptides from MS/MS data were processed with Proteome Discoverer Software (v 2. 1) using Sequest search node. All results were 1% FDR filtered before exporting. The resulting files were imported into Progenesis-Qi software for report edition. Variations of protein abundance were considered as significant if their Anova P values were < 0. 05. Details of the protocol and analysis are described in Supplementary Materials and Methods and Supplementary Fig.
1. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [26]  partner repository with the dataset identifier PXD013833. Ingenuity Pathways Analysis software (IPA, QIAGEN) was used to classify genes onto canonical pathways, cellular and molecular functions and networks. Pathways enriched with a P < 0. 05 were considered to be statistically significant. More details on IPA are found online (http://www. ingenuity. com). Database for Annotation, Visualization and Integrated Discovery (DAVID) software (https:// david. ncifcrf. gov, Version 6. 8) was used to determine and represent functional protein classes and biological processes enriched from Gene ontology (GO) database. Functional annotations with P < 0. 05 (EASE score) (modified Fisher's exact test) were selected.
Transcriptomic data analysis Genes expression data were extracted from two publicly accessible transcriptomic datasets: NCI-60 CellMiner Database (https://discover. nci. nih. gov/cellminer/Database Version: 2. 1) and affymetrix normalized data for breast cancer cell lines of the Institut Curie collection (http://microarrays. curie. fr/publications/recherche_translationnelle/ plateforme_genomique/). Using NCI-60 CellMiner Database, each gene transcript Z-score (input HUGO name) and gene name were used as input identifier. The data file retrieved contained the average log 2 intensity and Z-score of each gene expression in 60 cell lines. Using the Institut Curie database, gene mRNA expression levels (log 2 ) for 29 breast cancer cell lines were retrieved.
Miscellaneous methods MTT-based cell viability assay was performed as previously described  [28]. For colony formation assays, cells were plated in 6-well plates in a density of 1000/well and treated under defined conditions and then further cultured for 14 days. Afterwards, colonies were fixed by ethanol, stained with crystal violet solution and counted using Im-ageJ software. For siRNA transfection, cells were transfected with 20 nM siRNA (ON-TARGET plus SMART pool siRNA or control siRNA, Dharmacon) using INTERFERin reagents (Polyplus Transfection). Further experiments were performed 48 h after transfection. Classical western blots and redox western blots were performed as described previously  [24]. Total cellular glutathione levels (GSH + GSSG), GSSG (oxidized form) and GSH/GSSG ratios were assessed using GSH/GSSG-Glo™ Assay (Promega).
Statistical analysis Statistical significance of each data set was analyzed using adequate statistical tests available in the GraphPad Prism 7 software and specified in the body of manuscript and figure legends. Data were presented as the mean ± standard deviation (SD). Dose-response modeling, halfmaximal inhibitory concentration (IC50) determination from MTT cell viability assays and Spearman's correlation analyses were also performed using the GraphPad Prism 7 software (GraphPad Software, Inc). Wherever possible, exact P values are given and P < 0. 05 were considered statistically significant.