dataesr/scientific-paragraphs-categorization huggingface dataset:
The concentrated casein retentate is used to standardize cheese milk. The permeate, which contains whey proteins, is ultrafiltered to provide proteinrich concentrates with high nutritional and functional properties. The growing interest in these milk protein fractions explains the development of milk microfiltration equipment. However, this process remains difficult to understand as a whole, in particular, because the existing models  25  only evaluate a precise range of operating conditions and do not take into account the different types of membrane technologies that can be used  26. To meet this challenge, Baudrit et al. 27  designed a method combining the use of the PO2/TransformON vocabulary and a Bayesian network, to structure heterogeneous data sources, assess the reliability of data sources, and provide relevant recommendations based on deductive and quantitative reasoning.
This workflow allows predictions to be made by a learning system based on two models: i) PO2 core model and ii) probabilistic relational models (PRMs). Combining them in POND allows users to perform a retroactive operation, where each learned model is submitted to the expert who can refine the integrated knowledge or add new knowledge. When applied to a dataset from five different projects, POND has proven to be effective in enabling the formulation of optimal biomass types based on the desired characteristics of the final product, and finding another potentially interesting biomass even if it was not initially tested in the five projects  16.
Use Case 3: Environmental assessment of food, bioproducts, and their processing There is an urgent need to reduce the environmental damage caused by food systems  28  , all the more so the world's population is expected to grow to 9. 7 billion by 2050 29 , which will increase the need for food production. Widely applied to the agri-food sector  30  , Life Cycle Assessment (LCA) is the most scientifically recognized environmental analysis method worldwide and is standardized  31. It allows quantifying the environmental impacts of a product, process, or service during its whole life cycle. However, LCA is a data-intensive method which is often a crucial limit in the quality and quantity of LCA  32. The PO2 BaGaTel RDF graph database is helpful in providing the process flowchart associated with a product and the corresponding data needed for LCA  23.
PO2 core model and TransformON vocabulary are key-elements for retrieving available data: when the exact query could not be found, the hierarchy levels may be explored in order to find alternative data from different projects. For instance, if data on 'Comté cheese' cannot specifically be found, a query can be done on the upper level 'Cheese'. In the same way, with regards to processing, if the electrical consumption of the ripening step cannot directly be found, it can be calculated from the existing data on the equipment (here from the power of the refrigerating system and the duration of the ripening). Complementary, PO2/ TransformON has supported the development of the food and bioproduct engineering part of MEANS InOut tool, a user-friendly web app that helps users to perform LCA  22.
The usefulness of TransformON lies in the structured description of ingredients (inputs), products (outputs), processes, and steps, assisting LCA practitioners to describe their production systems through dropdown menus. A further step could be to align PO2/TransformON with open LCA databases, as well as food composition databases, in order to perform a multicriteria assessment of food. Use Case 4: Relations between food properties and consumer's perception and preference Food choices are difficult to change, and consumers' motivations can be incompatible with the food transition  33. If the reformulations of food products tend towards environmental and nutritional benefits, ensuring consumer adherence to the food transition requires that the reformulated products remain acceptable. Indeed, the taste is the main reason for food purchases in many countries  [34] [35] [36] [37] [38].
Moreover, sharing a common reference system enables us to uniformly query available data and to break out of the information silos that usually constitute individual projects. The approach permits contextualizing experimental data with metadata about material and method in order to be able to either redo experimentations or decide if data acquired in different research projects are comparable for meta-analysis perspectives. This also allows to identify existing knowledge gaps and suggest new experiments to be conducted. When coupled with Bayesian networks, the system can be used for learning and prediction under uncertainty. The ecosystem of tools was designed to assist domain users in building the vocabulary, collecting, querying, and publishing data using Semantic Web technologies, but the price to pay remains high as it takes effort to understand the PO2 model, and manually collecting data is a time-consuming and error-prone task.
We are therefore still working on the development of an automated import template. Finally, the developed software tools enable the publication of data structured in the PO2 RDF format on the INRAE's open data repository hosted on the Research Data Gouv platform (https:// entrepot. recherche. data. gouv. fr/dataverse/inrae) for long term preservation and sharing. The main outcomes of the work presented in this paper consist in the evolution of the PO2 core model, the semantization of FoodEx2, the re-engineering of existing domain ontologies, and the creation of a reference system providing unique resource identifiers (URIs) for food, bioproducts, and biowastes engineering. In addition, the integration of sensory aspects in the vocabulary constitutes an added value compared to the purely "process" and "safety" points of view initially taken into account in FoodEx2.
Knowledge management in the context of semantic web technologies Ontologies seek to formally describe a domain of knowledge by identifying the objects in this domain, their properties, and their relationships. For ontologies representation in the context of the Semantic Web, the OWL2 Web Ontology Language (https:// www. w3. org/OWL/) is standard. However, the Simple Knowledge Organization System (SKOS, https://www. w3. org/TR/skos-reference/) is a less formal data model that allows representing thesauri, classifications, or other types of controlled vocabularies. In SKOS, conceptual resources (concepts) are identified with unique resource identifiers (URIs), labeled with strings in one or more natural languages (multilingual terms), documented with various types of notes, semantically related to each other in informal hierarchies and association networks, and aggregated into concept schemes.
Using SKOS, more human-readable information (labels and documentation) may be added to an existing formal ontology. Therefore, we used SKOS to create the hierarchies of concepts that we defined as OWL classes in the ontology. OWL2 and SKOS use the Resource Description Framework (RDF, https://www. w3. org/RDF/) data model that is readable and interpretable by machines. RDF extends the linking structure of the Web by using URIs to name the relationship between things as well as the two ends of the link, the subject and the object (this is usually referred to as a "triple"). Using this simple model, it allows structured and semi-structured data to be mixed, exposed, and shared across different applications. This linking structure forms a directed, labeled graph, where the edges represent the named link between two resources, represented by the graph nodes.
The first activity defines the ontology requirements associated with the desired outcome, namely the PO2/Transfor-mOn domain ontology. The second activity represents the selection of semantic and non-semantic resources and the gathering of the required concepts for ontology formalization and conceptualization. The third activity describes the ontological transformations used to integrate the selected resources into PO2/ TransformOn in order to fulfill the requirements. The publication and maintenance strategy of the ontology is detailed in the section presenting the ecosytem of tools. Procedure for PO2/TransformON ontology creation PO2/TransformON ontology creation was done in three steps: (1) specification step, which consisted in identifying the scope and the objectives, competence issues, and relevant use cases; (2) formalization and conceptualization step, which consisted in identifying the most generic concepts and distinguishing the most specific concepts, in order to establish the hierarchy of specialization of the PO2 core model; and (3) implementation, publication and maintenance step, which consisted in encoding, providing URIs and mapping to external semantic resources or the domain ontologies previously used for the annotation of the datasets.
PO2/TransformOn requirements specification The ontology requirements specification activity refers to collecting the requirements that the ontology should fulfill. The ontology development team worked in collaboration with users and domain experts to define the purpose and scope of the ontology to be developed. Communication between domain experts, users, and the ontology development team occurred through monthly online and physical meetings between January 2021 and June 2022. Experts were engineers and researchers working in INRAE (France's National Research Institute for Agriculture, Food and Environment, https://www. inrae. fr/en). First, we defined the purpose and scope of the new domain ontology aimed to structure and build a federated graph-oriented database thanks to a harmonized vocabulary aligned with international reference systems, to allow knowledge integration and further modeling and reasoning.
During this requirements specification phase, new ontology requirements were identified, which implied considering some evolutions of the PO2 core model. The newly defined requirements were the following: to model a global process of biomass transformation by being able to distinguish between food and non-food products (OR1), to be able to distinguish between what comes from primary production, secondary processing, and waste (OR 2), to represent the experimental observations throughout the process by being able to distinguish the object of interest in the observation (OR3), to represent the list of equipments of a given platform (OR4), to be able to retrieve the replications of a process with respect to an experimental design (OR5) to identify metadata allowing traceability and harvesting of the ontology and corresponding datasets once published on the Web (OR6) A list of competency questions (CQs) was also collected.
Collection of terms and concepts for ontology formalization and conceptualization To collect the terms and concepts required for knowledge representation in PO2/TransformON, we combined two complementary approaches: first, we followed a bottom-up approach with the existing use cases and datasets (data-driven approach), and second, we followed a top-down approach for selecting knowledge resources. From the list of extracted terms and the CQs collected during the specification phase, we searched for appropriate existing resources. This activity consisted in looking for existing ontologies or thesauri that best fit the previously extracted terms (and their synonyms). We used specific services for ontology retrieval such as the Ontology Lookup Service (OLS, https://www. ebi. ac. uk/ols/ index), a repository for biomedical ontologies, and AgroPortal (https://agroportal.
Publication and maintenance of the domain ontology are managed by specific tools as detailed in the next section. An ecosystem of tools developed for ontology publication and maintenance and data stewardship An ecosystem of tools has been designed and implemented in accordance with the PO2 core model to create and manage domain ontologies and to annotate the data which are then published into a triple store (i. e. , a graph database in RDF format). Figure  10  shows the ecosystem of tools developed for ontology publication and maintenance and data stewardship. The first tool, PO2 Manager, is a standalone application developed in Java specifically designed to assist domain experts in vocabulary management and data annotation  47. The editor offers a graphical interface that allows a user to create processes and itineraries composed of steps (or unit operations) and input/ output compositions.
Observations are linked to a feature of interest (either a process, step, or composition) through this interface, and are collected either by manual entries or thanks to files in CSV format structured with specific templates. Once the dataset has been collected, PO2 Manager generates the RDF triples, and the data are published into a graph-oriented database, PO2 BaGaTel. PO2 Manager also allows domain experts to create and edit the vocabulary and publish the domain ontology with appropriate versioning into the triple store. Another tool named "simple PO2 Query" (SPO2Q) is a web service developed to facilitate data querying from the PO2 BaGaTel RDF graph database. User forms allow end-users who do not know SPARQL, the semantic web query language, to make queries easily, by selecting appropriate fields.
In an advanced usage mode, complex SPARQL queries may be defined. The queries allow data retrieval from different projects, and to reassemble data into new datasets. The datasets can then be saved or exported as CSV or JSON files. The queries can be saved and replayed when new data are available. Both queries and datasets can be published in open-access data repositories to make them publicly available. The PO2 Manager tool and SPOQ web service are publicly available at https://quantum. mia-ps. inrae. fr/PO2/ and https://quantum. miaps. inrae. fr/spoq/form, respectively. The core ontology is available at https://doi. org/10. 15454/XSVVBW with an Etalab Open License.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  INRAE's research Division Transform  ( AIC TransformON 2021-2022 ). This work is also part of the  ANRfunded project DataSusFood  ( ANR-19-DATA-0016 ). We are very grateful to the members of the team  Flaveur, Food Oral Processing et Perception from CSGA  for their contribution in the building of the sensory descriptors hierarchy in PO2/ TransformON. We also thank the members of the MEANS platform for the fruitful discussion leading to the interoperability between PO2 BaGaTel database and the Means InOut tool.
DATA AVAILABILITY The PO2 core model and PO2/TransformON have been made publicly available in the repository Recherche Data Gouv, https://recherche. data. gouv. fr (accessed on 26 May 2023). The PO2 core model is available at https://doi. org/10. 15454/XSVVBW with an Etalab Open License 2. 0, compatible CC-BY 2. 0. PO2/TransformON domain ontology is available at https://doi. org/10. 57745/DWX7W6 with an Etalab Open License 2. 0, compatible CC-BY 2. 0.
, i n } ⊂ {1,. , n + 1}. Clearly, the curves with classes h 1 , h 2e i , e i cover divisors in X 1,n n+1. Now, consider the projections in P n of the blown-up points p i 1 ,. , p i n and let H ⊂ P n be the hyperplane spanned by them. Let X 1,n-1 n ⊂ X 1,n n+1 be the strict transform of P 1 × H ⊂ P 1 × P n. To conclude it is enough to note that the curves of class h 1 + (n -1)h 2e i 1 -• • •e i n cover X 1,n-1 n. Remark 4. 5  Let X be a Q-factorial projective variety. In general Mov 1 (X ) Mov(X ) *. However, by Proposition 4. 4 we have that Mov 1 (X 1,n n+1 ) = Mov(X 1,n n+1 ) *. Remark 4. 6  We developed Magma  [4]  scripts computing the nef cones of X 1,n n+1 , X 1,n n+2 and of X 1,n  n+3 for n ≤ 4, as well as the Mori chamber decomposition of X 1,n n+1. A Magma library containing the scripts can be downloaded at the following link: https://github.
Bioinformatics Analysis of Rhinoviral Genomes for Identification of Putative GQs The QGRS mapping software was kindly made available by Paramjeet S. Bagga from Ramapo College, New Jersey. The underlying algorithm  [43]  predicts the likelihood of a sequence containing G-repeats to form a stable GQ, the stability score (G-score), based on published biophysical data. We performed our search using the default search options: maximal length = 45, minimum G-group = 2, and loop size ≥0 and ≤36. Allowing a loop size of 0 (zero loop) includes unconventional QGRS, as reported in yeast microsatellite DNA and mRNA involved in polyamine biosynthesis  [44, 45]. Complete RV-A (75), RV-B  (26)  and RV-C (  19 ) sequences (120 sequences in total available at the time of writing and accessible through The Pirbright Institute) were used.
Non-overlapping sequences with a G-score ≥10 within a sliding window of 20 residues were rendered as a 2D-line plot representing the QGRS length; the color bar refers to the individual G-scores. We wish to point out that the G-scores provided by the downloadable C++ implementation of the QGRS mapping algorithm used here occasionally differ from those obtained with the web-version https://bioinformatics. ramapo. edu/QGRS/analyze. php (accessed on 18 March 2023) of the QGRS Mapper (see note on Github, https://github. com/freezer333/qgrs-cpp; accessed on 18 March 2023).
Forward scattering (FSC) v/s VP2 (FITC-A) plots were generated by Tree Star FlowJo X v10. 0. 7 software (Ashland, OR, USA). Cells were cultured until ~80% confluent and infected with RV-A2 pretreated +/-200 µM PDS (as for flow cytometry, see above); at 30 min pi, the medium was replaced with PBS. Cells were gently detached and subjected to three freeze-thaw cycles. Cell debris was removed by low-speed centrifugation, and half the sample was used to analyze viral uncoating intermediates, i. e. , subviral A-particles plus (empty) B-particles. Particles were immunoprecipitated using mAb 2G2  [52]  bound to protein G magnetic beads (Dynabeads-Protein G; Life Technologies). The rest of the sample was processed identically, but mAb 2G2 was omitted as a negative control. As a positive control, ~1 µg of heated RV-A2 (10 min at 56 • C), resulting in almost ~100% conversion into subviral B-particles, was processed identically.
Time-of-Drug-Addition Experiments HeLa cells grown in 6-well tissue culture plates to roughly 80% confluency were challenged with RV-A2 at MOI = 10 for 30 min in infection medium at 4 • C with steady rocking, allowing the virus to attach to its receptor while preventing its internalization, achieving synchronized infection. The inoculum was removed, cells were washed three times with PBS, a fresh infection medium was added, and incubation continued at 34 • C to allow for virus internalization. Immediately (=0 min) and 180 or 300 min pi, PDS was added to a final concentration of 20 µM. At 9 h pi, cells were processed for flow cytometry and immunocytochemistry as above. Using Tree Star FlowJo X v10. 0. 7 software, a FITC-A histogram showing the mean fluorescence intensity (MFI) from more than 10 4 events corresponding to de novo produced VP2 was generated upon gating based on FSC and SSC properties.
A Kinetex C18 column (100 Å, 150 × 2. 1 mm) was used beforehand to separate PDS from other components of the extract, employing a flow rate of 80 µL/min. A 10-min linear gradient was used from 95% A (1% acetonitrile, 0. 1% formic acid in water) to 80% B (0. 1% formic acid in acetonitrile). LC-MS/MS was performed by employing the selected reaction monitoring (SRM) mode of the instrument and using the transitions 597. 4 m/z → 511. 3 m/z (CE 20) and 597. 4 m/z → 468. 3 m/z (CE 25). Data were interpreted manually, and the absolute amount of PDS was quantified by using a calibration curve obtained with pyridostatin standard solutions.
QGRS Mapper Identifies Potential GQ-Forming Sequences in All RV Genomes Using the C++ implementation (https://github. com/freezer333/qgrs-cpp; accessed on 18 March 2023) of the QGRS Mapper algorithm  [43] , we investigated all RV complete genome sequences available in GenBank. As RNA GQs are generally more stable than the corresponding DNA GQs  [56] , we adopted the search motif G ≥ 2L0-36G ≥ 2L0-36G ≥ 2L0-36G ≥ 2L0-36 (maximum length 45 bp), allowing long and zero loop GQs and two-layer GQs, as both features are common  [44] , and plotted the G-score (threshold ≥10) against the position of the first nucleotide of the respective putative QGRS in the genomic RNA sequences (Figure  1 ).
The mock-treated virus was used as a control. After one infection cycle (9 h pi), RV-A2-positive cells were determined by fluorescence-activated cell sorting (FACS) using intracellular VP2 as a readout for replicated RV-A2. Upon pre-incubation with PDS (20 µM and 200 µM) as well as with PhenDC3 (5 µM and 1 µM), we observed a concentration-dependent decrease in the fraction of cells containing de-novo synthesized RV-A2 (Figure  3E ; note that an effect of PhenDC3 was only apparent at 5 µM). These data further strengthen our hypothesis that GQ stabilization triggers structural changes in the encapsidated RNA that reduce genome release in vivo and consequently virus production.
Acknowledgments: We express our gratitude to  Irene Goesler  for virus production and purification. Our special thanks go to  Arthur Sedivy ,  Thomas Koecher , and  Marlene Brandstetter  from the  Vienna BioCenter Core Facilities (VBCF)  for their valuable help and assistance. Mass spectrometry ( Metabolomics Facility ), electron microscopy ( Electron Microscopy Facility ), and circular dichroism (Protein Technologies Facility) were performed at  VBCF. Open Access Funding by the  Austrian Science Fund (FWF).
/github. com/freezer333/qgrs-cpp; accessed on 18 April 2023). Viruses 2023, 15, x FOR PEER REVIEW 4 of 2 o 18 April 2023). of the QGRS Mapper (see note on Github https:/ tion of the QGRS mapping algorithm used here occasionally differ from those obtained with the web-version https://bioinformatics. ramapo. edu/QGRS/analyze. php (accessed.
7. We focused our analysis on the continental shelves and excluded both the deep ocean (here defined as all grid points deeper than -250 m) and polar (>60 °latitude) regions since they are not well represented in paleontological databases  20, 32, 33. Analysis of the spatial patterns reveals that the maximum of simulated biodiversity (or equivalently, maximum number of niches available) shifts from high latitudes (60°S) between 490 Ma and 470 Ma, to middle latitudes at 460 Ma, and finally to low latitudes (from 450 Ma onward) (Fig. 1 ). This spatial-temporal pattern arises from the interaction between the modeled pseudo-species and their environment, with lethally high ocean temperatures making the lowlatitude shelves hostile to marine life in the late Cambrian and gradually more suitable as global climate cools.
2 ). Differences between these curves arise from the variable temporal resolution, with the seminal curve of Sepkoski et al. (ref. 5) exhibiting the lowest resolution (>5 Myrs). Differences also reflect the use of different datasets. Rasmussen et al. (ref. 41) based their compilation on the Paleobiology Database (PBDB), which mostly includes data from Laurentia and Baltica (>50% in 2019), while the Geobiodiversity Database 3 (GBDB) mainly includes Chinese data. In our simulations, species richness monotonically increases in response to global climate cooling from the late Cambrian (490 Ma) to the Katian (Late Ordovician, 450 Ma) and subsequently stabilizes between 450 Ma and the early Silurian (430 Ma), in first-order agreement with the reference biodiversity curves (Fig. 2 ). Biological evolution may have played a role during the GOBE as well  14, 42  , but it is not necessary to invoke this process to simulate a first-order rise in marine biodiversity during the Ordovician in our model.