dataesr/scientific-paragraphs-categorization huggingface dataset:
The known glycogen degradation pathway on MetaCyc 23. 0 was detected with the presence of the reactions EC 2. 4. 1. 1 (Maltotetraose glucosidase malP FSU_RS06195) and EC 2. 4. 1. 25 (4-alpha-glucanotransferase malQ FISUC_RS04305; FSU_RS06200) identified by annotation (Fig. 4 ). Then, we completed this pathway by adding manually the two reactions EC 3. 2. 1. 196 (limit dextrin α-1,6-glucohydrolase glgX) and EC 3. 2. 1. 20 alpha-glucosidase (malZ FSU_RS06195). The maltotetraose formation reaction present in E. coli model was added to our model, and no gene was linked because of no significant BLAST similarity with the F. succinogenes genome (Data set S1 in supplementary material B).
Dynamic metabolic model Table  4  shows the selected EFMs and the model parameter estimates for each substrate, including the conversion factors between the biomass concentration (moles per liter) and OD for glucose and cellobiose. The metabolism of glucose and cellulose is represented by four macroscopic reactions. For cellobiose, the metabolism is represented by five macroscopic reactions. All the EFMs are from the polygon vertices of the yield spaces. The models were implemented in MATLAB and are available at https://doi. org/10. 5281/ zenodo. 7228115. Fig. 9A  through C show the comparison of the variables predicted by the model against the dynamic experimental data obtained from the culture of F. succinogenes on glucose, cellobiose, and cellulose, respectively. Table  5  shows the accuracy of the model.
DISCUSSION The mathematical modeling of the rumen ecosystem is a useful endeavor to provide tools for improving rumen function. Current kinetic rumen models do not consider genomic information  (31, 65, 73, 74). GEMs are a promising tool to fill this lacking gap and allow a better understanding of the rumen systemic functionality  (75)  and the individual bacteria metabolism  (76). Many independent methods have been developed to generate genome-scale models, including some toolboxes and workspaces, such as Pathway Tools  (39) , RAVEN  (77) , merlin  (78) , KBase  (79) , The SEED  (80) , AuReMe  (38) , AutoKEGGRec  (67) , CarVeMe  (81) , and gapseq  (82). They rely on one or several metabolic databases such as MetaCyc  (48) , KEGG  (83) , ModelSEED  (84) , or BiGG  (68). However, the output of a main platform for a GEM requires adjustments assisted by a choice of specialized tools, especially when the network reconstruction requires to take advantage of information spread in different models, formats, and organisms, leading to issues in the standardization of metadata and reproducibility of the reconstruction procedure.
Some of the glycogen pathway reactions were identified from the databases as generic reactions. F. succinogenes is known to synthesize glycogen during all its growth phases  (19). We focused on the validation of its production/degradation pathway including generic reactions not only for its specificity in this organism but also for its importance in the carbon cycle equilibrium that needs to be held for the network, the EFM computation step, and the DMM. As a solution, we have set for glycogen a number of monomers equal to 6 to be able to complete the carbon balance and therefore stoichiometrically balance the reactions of this metabolic pathway. The reduced network helped to calculate the EFM whose reduction was performed by yield analysis  (71). A 2-D representation of yields was used to compute the convex hull that surrounds the EFMs.
ACKNOWLEDGMENTS We thank the  INRAE PHASE department  and the  INRAE MEM  metaprogram for financial support. The Ph. D. of Ibrahim Fakih is supported by grants from the  INRAE Holoflux metaprogram  and funding from  Lallemand Animal Nutrition (Blagnac, France). We thank  Emile Dumont  and  Méziane Aite  for their preliminary work on network reconstruction and  Melanie Brunel  for the cultivation experiments with F. succinogenes. The authors declare that they have no competing interests.
Since the considered HTs are foodborne pathogens, the investigation followed the 'farm-to-fork' chain approach. The results of this work are detailed in a document published on Zenodo  (15) , the open repository developed under the European OpenAIRE programme. However, the mapping exercise allowed the identification of both opportunities and challenges of this investigation approach of what is already in place in different countries. In this paper, we therefore will describe our methodological approach, and be presenting two reallife scenarios as case studies. The two scenarios chosen as case studies are the surveillance of L. monocytogenes in dairy products in Norway, and the Salmonella surveillance in pig meat in France. The scenarios concern pathogens that are of importance for human health based on the severity (L.
Online questionnaires Within the activities of the project MATRIX, a multiple-choice questionnaire was created for each of the four hazards (Salmonella, Campylobacter, L. monocytogenes, and Hepatitis E virus), to gather the necessary information for the mapping of the existing food chain surveillance activities from national experts in the field. As an adaptation of the approach from 'farm-to-fork' to 'farm-to-patient' , each questionnaire was divided into three different sections: (I) focusing on the animal health aspects (AH), (II) on the food safety aspects (FS), and (III) on public health (PH). In each section, the surveillance was assessed by gathering information on actors, sampling context, collected sample types, laboratory methods for diagnosis, available data sources, and cross-sectoral collaboration in place.
Mapping template A questionnaire was considered completed when answers from the three involved sectors (AH, FS, PH) were obtained. Upon the reception of the three compiled sections, a preliminary evaluation of the results was carried out. Where missing or unclear information emerged, we requested clarifications by re-sending the questionnaire to the reference expert (or to a different one). For this reason, the questionnaires were open for completion for a period of about six months. In order to evaluate and display the collected information, a categorisation was put in place: information was classified as part of 'data' , 'metadata' , 'events' , 'event producing data (EPD)' , and/or 'identified data source (IDS)'  (15). The subsequent step was then the identification of the most relevant information, for their graphic representation on a map.
Foods The sampling plans in the official national programmes are designed to cover imported foods and local small-scale dairy products. Large-scale dairies usually have their own sampling programmes. The surveillance of small-scale producers includes the sampling of summer products. In some programmes, '24 h samples' (which means sampling the day after the start of the maturation process) are implemented in farms and small-scale dairies, as several pathogens can be found at the highest concentration at this stage. This kind of sampling allows for the rapid detection of anomalies and allows for sampling without the loss of the entire cheese. Sampling is also performed at the retail level, in compliance with the microbial criteria in the food legislation. In addition, metadata like production date, shelf-life date, animal species, whether the product is made of pasteurised or unpasteurised milk, producer, sampling place (address and kind of shop), and sampler can be recorded.
Animals In the animal sector, many activities for Salmonella surveillance are implemented at the farm level in France (Figure  3 ), which are carried out by official control authorities, laboratories, farmers, the industry, private veterinarians or technicians, and eventually research centers or institutions like universities. In the framework of monitoring programmes, outbreak investigations, or research projects, these actors collect environmental samples, including fecal material, water, and feed to detect and identify the bacteria by phenotypic or molecular methods. Laboratories implement official methods to serotype all isolates and, among this  The monitoring and control of the application of biosecurity measures are particularly important, for both breeding and fattening pig farms. For this reason, additional data including personnel movement, and records of cleaning and sanitation procedures, is collected.
Humans In France, sporadic cases of salmonellosis are not notifiable diseases. Several actors, from local health authorities to hospitals/ clinical/reference/local laboratories, monitor for human salmonellosis. In general, consistent data related to case detection are collected on a routine basis, while additional epidemiological data are collected mainly during outbreak investigations. A research unit from Institut Pasteur hosts the French mandate of NRC for Salmonella. This reference laboratory collects strains and data related to human cases confirmed by contaminated blood or faecal material. NRC shares confidential data related to each case with SpF, including the severity of symptoms, and spatial and temporal data. WGS is systematically performed, and results are centralised. Algorithms using this database produce weekly alerts when clusters based on microbiological data occur, and then the NRC informs SpF of these situations.
Currently, there is no automatic tool or shared database in place at the national level to allow prompt interaction between human and non-human sectors. To date, the ability to share data mainly depends on the interpersonal connections between scientists working at the reference laboratories (NRC and NRL). In conclusion, the collaboration between sectors exists mostly for foodborne outbreak surveillance and investigation. The exchange of information issued from investigation frameworks is in place between the Regional sanitary authorities in charge of human surveillance ('Regional health agency') and of food safety, animal health, and welfare ('Departmental Directorate for Social Cohesion and Population Protection'). Additionally, information is shared with the national competent authorities to implement adjusted control measures.
Conclusion During the MATRIX project, we proved that it is possible to map surveillance chains of foodborne pathogens of One Health relevance across the human health, animal health, and food safety sectors in various European countries, and the methodological approach described in this manuscript is replicable in several contexts. Although many efforts are implemented to remove barriers to a better application of the One Health, the importance of shifting from silo thinking should not be underestimated. The methodological approach that we presented can support identifying new opportunities for integrating OHS, while lifting our heads and looking further than we normally do, as it happens during research projects.
This catalogue, based uniquely on Gaia data, also has long-term legacy value as a rich database for target selection for future follow-up studies and missions. In this work, we demonstrate the potential of the new data products in Gaia DR3 by producing very high quality samples of astrophysical parameters of stars throughout the HR diagram. We aim to make clean samples of stars based on severe quality cuts. We consider these sources to have the most accurate and precise stellar properties in this catalogue, and advocate their use on a star-by-star basis. However, these quality cuts have an important impact on the selection function and completeness. Our selection criteria will not be optimal for the specific scientific cases of many users, and we fully encourage the exploration of the full catalogue of APs in Gaia DR3.
This paper is laid out as follows. In Sect. 2 we describe the data products that are used in this work. Subsequently, in the first part of this analysis, we focus on three main stellar regimes and produce large high-quality samples of stars covering the hot O-, B-, and A-type stars (OBA, Sect. 3), the cooler F-, G-, K, and Mtype stars  (FGKM, Sect. 4) , and the substellar ultracool dwarfs (UCDs, Sect. 5). We then focus on specific objects of interest: carbon stars (Sect. 6), solar analogues (Sect. 7), and finally the Gaia spectrophotometric standard stars  (SPSS, Sect. 8, Pancino et al. 2021). Sections 3-8 are entirely independent sections and readers can choose to focus on their section of choice without missing important information for the rest of the paper. In Sect. 9 we describe the six tables from this work that are made available in Gaia DR3, and then in Sect.
2013; Creevey et al. 2023 ) and a series of three papers describe the methodologies and content of the APs in Gaia DR3. Creevey et al. (2023)  present an overview of the processing, the architecture, and the modules of Apsis, along with a summary of the data products. Fouesneau et al. (2023)  focus on the stellar content, its description, and quality assessments, and  Delchambre et al. (2023)  detail the non-stellar content, in particular object classification, extinction, and extra-galactic objects. The DPAC data processing chain also uses these APs, for example, to identify the best template spectrum for the extraction of the radial velocities from the RVS spectra, the identification of quasars used to fix the astrometric reference frame, and the optimisation of the BP and RP calibration.
In this work, we focus on the data products provided by six modules of the Apsis chain; the General Stellar Parametrizer from Photometry (GSP-Phot), the General Stel-lar Parametrizer from Spectroscopy (GSP-Spec), the Extended Stellar Parametrizer for Emission-Line Stars (ESP-ELS), the Extended Stellar Parametrizer for Hot Stars (ESP-HS), the Extended Stellar Parametrizer for Ultra-Cool Dwarfs (ESP-UCD), and the Final Luminosity Age Mass Estimator (FLAME). These are described in detail in  Creevey et al. (2023)  and in the online documentation. Further details on GSP-Phot and GSP-Spec are also found in the dedicated module papers  (Andrae et al. 2023; Recio-Blanco et al. 2023). Briefly, GSP-Phot processes all sources with mean BP and RP spectra  (De Angeli et al. 2023; Montegriffo et al. 2023 ) to produce spectroscopic parameters and extinction estimates; it also uses parallaxes and photometry.
Within the Apsis software, the parallaxes are corrected for the known zero-point biases as a function of latitude, magnitude, and colour; see  Lindegren et al. (2021b). It processes the sources considering four stellar libraries, and the individual results for each of these libraries are found in the astrophysical_parameters_supp table. The results from the library responsible for the highest log posterior for that source (see libname_gspphot) are those that appear in the main astrophysical_parameters table. GSP-Spec processes sources with mean RVS spectra  (Seabroke et al. , in prep. )  and produces not only atmospheric parameters but also chemical abundances and the diffuse interstellar band characterisation. These latter products are not the focus of this work, and we refer readers to Gaia  Collaboration (2023a,b) , respectively, for further information on those.
Sample selection GSP-Phot and ESP-HS are the two main Apsis modules that derive the APs of OBA stars. While GSP-Phot processed all targets with G ≤ 19, ESP-HS only processed OBA stars brighter than G = 17. 65  1  , and additionally it only processed those stars that received a spectraltype_esphs tag of ∈ ['A','B','O']. This tag is derived from a Random Forest classification of the BP and RP spectra; see  Sect. 11. 3. 7  of the online documentation for details. In terms of effective temperature, this is equivalent to selecting targets hotter than 7500 K. The same lower T eff limit is applied to the GSP-Phot stellar sample. Because GSP-Phot processes targets down to G = 19, the corresponding sample initially contains more (11 156 449 stars) candidate-OBA targets than ESP-HS  (2 344 484). The GSP-Phot parametrisation partly relies on the use of parallax, and more outliers (e.
g. Helmi et al. 2018). The colour coding in Fig. 4  suggests that the halo contaminants in the OBA sample can be filtered out by demanding v tan < 180 km s -1 , although clearly there will be stars left at low tangential velocities which have a total velocity that puts them in the halo. Figure  5  shows the observational Hertzsprung-Russell (HR) and the Kiel diagrams for the sample of OBA stars with /σ > 10. Extinction corrections using A G and E(G BP -G RP ) from the ESP-HS module were applied. The contours show the distribution of the full sample, while the colour coded density images show the distribution of stars selected according to A39, page 5 of 34   marily located at high Galactic latitude, as expected for a halo population. A search in SIMBAD  (Wenger et al. 2000)  results in 8124 matches for which there is information on stellar type, of which 5770 are incompatible with stellar types corresponding to hot young stars, including 5499 sources classified as HB stars.
Sample selection The identification of candidate carbon stars by ESP-ELS is based on a Random Forest classifier trained on the synthetic BP and RP spectra as well as on the observed Gaia data obtained for a sample of Galactic carbon stars  (Abia et al. 2020 ). This identification is saved in the spectraltype_esphs field of the astrophysical_parameters table. In total, 386 936 targets received the 'CSTAR' tag. While most of these stars are M stars, only a smaller fraction of the sample exhibit significant C 2 and CN molecular bands. To identify these cases, we measured the band head strength as follows: R λ 2 = f (λ 2 ) g λ 1 ,λ 3 (λ 2 ) , (2) where f (λ 2 ) is the flux measured at the top of the band head of the molecular band, and g λ 1 ,λ 3 the value linearly interpolated  between wavelengths λ 1 and λ 3.
SPSS The Gaia SPSS 8 are a grid of flux tables specifically designed to calibrate Gaia photometry and BP and RP spectra. They are the result of a dedicated set of ground-based observing campaigns to collect spectrophotometry  (Altavilla et al. 2015) , light curves for constancy monitoring  (Marinoni et al. 2016) , and absolute photometry for validation  (Altavilla et al. 2021 ) over more than ten years. The latest version of the grid, SPSS V2, was used to calibrate the Gaia photometry in EDR3 and the BP and RP spectra in DR3 and contains 111 stars 9 based on 1500 spectra, and is calibrated on the 2013 version of the CALSPEC 10 grid  (Bohlin et al. 1995 (Bohlin et al. , 2019;; Bohlin 2014 ) with a zero-point accuracy of better than 1%. The SPSS grid is designed to cover those areas of the stellar parameter space that are not well sampled by CALSPEC, in particular the FGKM star types, and to cover the entire Gaia wavelength range (330-1050 nm).
The final release, SPSS V3, 7 We also inspected the variation of these colours with the GSP-Spec DIB measurements (Gaia Collaboration 2023b) and find qualitatively similar results. Unfortunately, only very few DIB measurements are available for our candidates. will be used to calibrate Gaia DR4, it will contain about 200 stars and will make full use of all the 6500 spectra collected in the observing campaigns. It will be calibrated on the latest version of the CALSPEC grid  (Bohlin 2014; Bohlin et al. 2019) , which differs by about <0. 5% from the 2013 one in the grid zero point. The S/N of the ground-based SPSS spectra is generally well above 100, with the exception of the blue and red extremes of the Gaia wavelength range. The SPSS flux tables were therefore extended with theoretical spectra and adjusted to match the central high-S/N region of the observed spectra (see  Pancino et al.
2021, for details). It is therefore of the utmost importance for the next SPSS release to have a robust estimate of the spectral type, atmospheric parameters (T eff , logg, [Fe/H], and [α/Fe]), and of the interstellar absorption for as many of the SPSS as possible. To this aim, we explored and selected relevant information from the astrophysical_parameters table of Gaia DR3 for the SPSS V2 stars. In particular, whenever available, we selected the GSP-Spec parameters over the GSP-Phot ones for T eff , logg, and [Fe/H]. To obtain [Fe/H] from the GSP-Phot [M/H] estimates, we used the formula by  Salaris et al. (1993)  and assumed an αenhancement of +0. 35 for metal-poor stars, +0. 15 for intermediate metallicities, and zero for solar or higher metallicity. We note that we did not apply any re-calibration to the log g and [Fe/H] GSP-Phot and GSP-Spec values.
Similarly, for the choice of the FLAME parameters, that is, mass, age, luminosity, and radius, we always selected the corresponding FLAME-spec determinations when available (in the astrophysical_parameters_supp table). Parameters were available for all the SPSS in the sample, except for the 56 white dwarfs. For hot stars, a handful of parameters from ESP-HS were available that were not parametrised by GSP-Phot or GSP-Spec. The two binarity estimators available (specmod and combmod) agreed on indicating SPSS 028 (SA105-663) as a binary, while 15 different SPSS were indicated as photometrically variable (phot_variable_flag) and will be carefully re-evaluated in the preparation of the SPSS V3 release. To explore the quality of the results, we compared them with the two sets of parameters presented by  Pancino et al.
(2021) : (1) a collection of literature estimates and (  2 ) the best-fit parameters obtained by extending the SPSS V2 flux tables with model libraries. First, we compared the spectral type determinations and find that only 16 SPSS out of 111 have discrepant spectral types, and in all cases the discrepancies never span more than one spectral class (e. g. an F star classified as G). For one star, SPSS 313 (M5-S1490), discordant previous literature spectral type determination (from A to F) was available, and we find it to be a K giant, about 500 K cooler than the coolest literature determination. We then compared the three main atmospheric parameters (Fig. 31 ) with both reference sets. As can be seen, apart from a very small number of outliers, the agreement with the two sets of reference parameters appears good, especially when considering the heterogeneity of the literature estimates.
As well as source_id, the table for the UCDs contains the newly derived radii and luminosities from the analysis of the Gaia and infrared data, and the bolometric flux correction. The SPSS sample table contains all 111 SPSS sources along with information such as binary and variability flags, radial velocity, and v sin i. The stellar parameters and extinction are given for the non-white dwarf stars, some of which are based on GSP-Spec parameters and others on GSP-Phot or on ESP-HS; this is indicated by the notes in that table. Finally, for the FGKM sample, a table with source_id, atmospheric parameters, evolutionary parameters, and spectral type is provided, where specific parameters for some sources have been removed (compared to the astrophysical_parameters table).
405 ± 0. 065) mag, ( 13 ) where we restrict the AllWISE comparison to cases with W 1 > 8 mag in order to avoid saturation. These colours are in excellent agreement with the values (G BP -G RP ) = 0. 82 mag, (G BP -G) = 0. 33 mag, and (G -G RP ) = 0. 49 mag obtained by  Casagrande & VandenBerg (2018)  from Gaia DR2 passbands and synthetic as well as observed spectra for the Sun. The absolute magnitude of M G, = 4. 67 mag obtained by these latter authors is also consistent with our estimate. In order to make this comparison with Gaia DR3 passbands and also include near-infrared photometry, we take the Kurucz model sun_mod_001. fits from the CALSPEC library 11   (Bohlin et al. 1995 (Bohlin et al. , 2014 (Bohlin et al. , 2020) )  and simulate its photometry using the pyphot package 12. We obtain synthetic colours of A, 2MASSI J0025036+475919 22.