dataesr/scientific-paragraphs-categorization huggingface dataset:
This principle is well-known in genetics and genomics. For instance, the Animal Quantitative Trait Loci Database is a repository for quantitative trait loci (https://www. animalgenome. org/cgi-bin/QTLdb/ index). Gene Expression Omnibus is a public functional genomics data repository (https://www. ncbi. nlm. nih. gov/geo/). In contrast, in animal phenotyping (including meat science), this is less common. One main reason is historical: the characterization of animals and of their products has been the subject of research for centuries and therefore started in each country with poor communication between research organizations, resulting in low standardization of methods. On the contrary, when genomics exploded, scientists were already more or less communicating with a common wish to share resources.
The lack of standardization also impairs the third FAIR principle, namely Interoperability, as described subsequently. To better share phenotypic data, some institutions, such as the French National Research Institute for Agriculture, Food and the Environment (INRAE), have created their own repository: the Data INRAE portal offers new services to facilitate the management, sharing, and retrieval of research data from INRAE (https://data. inra. fr/dataverse/root). The INRAE portal attributes a unique and relevant identifier to each set of data deposited in this repository. For instance, a dataset related to carcass yield has been recently deposited to compare carcass yield across breeds  (Hocquette et al. , 2019). The second FAIR principle, Accessibility, means that data are retrievable, that the protocol is open and free, and that at least metadata-and sometimes even raw data-are accessible.
Again, geneticists are in the habit of making their data accessible because they really need large amounts of data for genetic analyses. On the contrary, the meat industry is very conservative  (Troy and Kerry, 2010) , often dominated by a few retailers who are focused on the competition between each other. Consequently, they are not open to sharing data, which weakens product-science-based development and innovation in the meat sector. Again, any initiative such as the Data INRAE portal aims to share research data funded by public grants.
, 2008). To achieve the goals, the Animal Trait Ontology of Livestock (ATOL) was developed (http://www. atol-ontology. com/atol/) and should be strengthened in meat science. The last FAIR principle is Reusability. This means that the data should be richly described, be released with a clear and accessible data-using license, be associated with detailed provenance, and meet domainrelevant community standards. As mentioned earlier, to better share phenotypic data, INRAE has created its own repository: the Data INRAE portal (https://data. inra. fr/dataverse/root) is a tool to promote open science. It uses Dataverse, which is an open-source data repository software to support institutional research. The use of interoperable standards is also a key issue to make data reusable. To achieve this goal, a group of academics from different countries have developed AgroPortal, which is a vocabulary and ontology repository for agronomy, food, plant sciences, and biodiversity.
, 2008a (Watson et al. , , 2008b)). Then, these 4 scores are combined together to generate the 4-variable meat quality score (MQ4), which is a global eating-quality score (Figure  2 ). It was shown using the MSA approach that eating quality is poorly related to carcass fatness and conformation, which are the major traits considered in Europe for trading  (Bonny et al. , 2016). Secondly, the MSA grading scheme is based on large amounts of data obtained with the same standard protocols  (Watson et al. , 2008a (Watson et al. , , 2008b)). Indeed, almost 100,000 untrained consumers were used over years to score around 700,000 beef samples from different sources in terms of breeds, animal types, cuts, etc. , allowing robust modelling approaches, since the data are always comparable. By using this large database, the MSA grading model predicts MQ4, which is the palatability score of each individual cut based on 4 sensory descriptors from untrained consumer taste panels, namely tenderness, juiciness, flavor liking, and overall liking.
Critical steps were the development of rigorous test protocols and expression of consumer sensory standards such as the MQ4, leading to a realization that accurate prediction of an individual beef meal result needed to assign an outcome to cooked individualmeal-sized portions rather than a generic carcass description. As described earlier, a further fundamental decision was to accumulate data from all related experiments and commercial product evaluation no matter the country. Data are stored in a common database utilizing consistent description to enable issues to be evaluated over multiple disparate base studies. Formal MSA international collaboration began with consumer studies in South Korea  (Thompson et al. , 2008)  and Northern Ireland  (Chong et al. , 2019)  and were followed by further projects in the US  (Polkinghorne, 2007) , Japan  (Polkinghorne et al.
, 2011 ), Ireland (McCarthy et al. , 2017) , France  (Legrand et al. , 2013) , and South Africa  (Strydom et al. , 2019). Independent extensive projects utilizing identical protocols were conducted in New Zealand  (Garmyn et al. , 2019) , the US  (O'Quinn et al. , 2018) , and Poland  (Guzek et al. , 2015; Pogorzelski et al. , 2020) , with other countries also extending earlier programs or conducting new studies (such as those in the UK, Wales, and France in the past year), the more recent projects being under the auspices of the Foundation. Many of the research teams and individuals have collaborated closely over this period and concluded that, for maximum global beef industry benefit and research efficiency, it would be highly beneficial to pool data for analysis and to develop further industry applications, which is the aim of the International Meat Research 3G Foundation.
Organization and missions of the International Meat Research 3G Foundation The not-for-profit Foundation, called the International Meat Research 3G Foundation (https:// imr3gfoundation. org/), was incorporated under Polish law to provide a practical structure to facilitate scientific collaboration, data storage, and utilization and to provide a platform for commercial application under the auspices of UNECE. Formal structures include a Management Council charged with legal responsibility for governance, business functions, and delivery of commercial activity and a Scientific Reference Group with responsibility for scientific standards, collaboration, and peer review. The Scientific Reference Group decisions are strictly evidence based and relate to data, whereas commercial and industry-related activity is the responsibility of the Council (Figure  3 ).
It is anticipated that the structure will provide a useful forum for ongoing scientific collaboration across countries and organizations. A major project is DATABank, which is establishing a cloud-based data storage and management system that can provide secure and confidential data storage for members and facility to pool data for agreed purposes as desired. Supporting largely open-source software is being expanded to provide easy access for researchers or students to assist with trial design and application aligned to standard protocols to ensure compatibility. Considerable effort is being made through the ontology working party to ensure standardized description and linkage to other international standards such as ATOL or the International Committee for Animal Recording. A data analysis technical group is foreshadowed as data are accumulated and made available for scientific investigation and potential development of eating-quality prediction models, with the capacity to relate consumer populations to alternative production systems and regions.
Methods Study population. We examined 1990 subjects from the MetaCardis cohort for whom a serum sample was available but excluded 32 patients due to non-metabolic etiology of cardiovascular diseases (n = 25), clear outliers for ImP levels according to Grubb's test (n = 1), non-complete biochemistry data (n = 6). Patients with a history of abdominal surgery (other than appendicitis or cholecystectomy), abdominal radiotherapy, digestive cancer or that had received a recent antibiotic treatment (<2 months) were not included. Patients that had participated in the previous cohort-based study were contacted for potential inclusion. A subgroup of healthy control individuals with no signs of obesity or metabolic syndrome were recruited through advertisement and through existing population cohorts.
All subjects provided written informed consent and the study was conducted in accordance with the Helsinki Declaration and is registered in clinical trial https://clinicaltrials. gov/show/NCT02059538. The Ethics Committee of each participating country approved the clinical investigation. The study was approved by the Comite de Protection des Personnes (CPP) Ile de France III no. IDRCB2013-A00189-36. A detailed list of prescribed medications, anthropometric data, clinical history, fecal sample, and a fasting blood sample was obtained at enrollment. Subjects were classified as healthy, prediabetes, or type 2 diabetes. Type 2 diabetes was defined as fasting glycemia ≥7. 0 mmol/l and/or 2 h values during the oral glucose tolerance test >11. 1 mmol/l and/or hemoglobin A1c (HbA1c, glycated hemoglobin) ≥6.
5% (≥48 mmol/mol) and/or use of any anti-diabetic treatment; prediabetes was defined for subjects without type 2 diabetes as fasting glycemia ≥5. 6 mmol/l and/or 2 h values in the oral glucose tolerance test ≥7. 8 mmol/l and/or hemoglobin A1c (HbA1c, glycated hemoglobin) ≥5. 7% (≥39 mmol/mol) according to the American Diabetes Association (ADA) definitions  44. Dietary intake data and diet quality assessment. Dietary data were collected via a food-frequency questionnaire that was adapted to the cultural habits of each of the countries of recruitment. A validation study against repeated 24 h-dietary records among 324 French MetaCardis participants has indicated an acceptable validity  45. Specifically for this study, histidine intake was calculated based on values concerning the histidine content of selected foods published online in the United States Department of Agriculture (USDA) food composition databases https://ndb.
Serum glucose, insulin, and C-peptide were measured at baseline and 120 min after the glucose load. 8. Source data are provided as a Source Data file. Imidazole propionate serum measurements. ImP was quantified using ultraperformance liquid chromatography coupled to tandem mass spectrometry according to previous work. Briefly, serum samples were extracted with 3 volumes of ice-cold acetonitrile containing internal standards (13C3-labeled ImP and urocanate). After derivatization to butyl esters using 5% hydrochloric acid in butanol, the samples were separated on a C18 column using a gradient consisting of water and acetonitrile. Quantification was made using an external calibration curve  16. Flow cytometry analysis. To characterize and quantify immune cells, 100 µl of whole blood was freshly obtained in a subgroup of subjects (n = 439) belonging to one of the centers involved in the MetaCardis consortium (France, Pitié-Salpêtrière Hospital, Institute of Cardiometabolism And Nutrition cytometry platform)  34.
Briefly, blood was incubated with FcR blocking reagent (Miltenyi Biotec, Bergisch Gladbach, Germany), red blood cell lysated, and then white blood cells were stained with the following antibodies: Vioblue-anti-human CD3 (clone BW264/56), PE-Vio770-anti-human CD4 (VIT4), PerCP-Vio700-anti-human CD8 (BW135/80), APC-anti-human CD25 (4E3), FITC-anti-human CD127 (MB15-18C9), PE-antihuman CD161 (191B8) from Miltenyi Biotec, and APC-Cy7-anti-human TCR Vα7. 2 (3C10) from BioLegend (San Diego, CA, USA). Data acquisition was performed with a MacsQuant Analyzer using MacsQuantify software (Miltenyi Biotec). Data obtained were analyzed with FlowJo 10. 1r5 software (Tree Star, Ashland, OR, USA). Extraction of fecal genomic DNA and whole-genome shotgun sequencing. Participants collected fecal samples within 24 h before each visit.
In order to reduce technical bias due to variable sequencing depth, Ion-Proton samples were downsized to 10 million reads, and downsized gene abundances were normalized according to Fragments Per Kilobase per Million mapped reads (FPKM) strategy. Assessment of gut microbiota characteristics. Metagenomic data were available for 1852 subjects. Abundance for each MGS (metagenomic species) was computed as the mean value of the 50 genes defining a robust centroid of the cluster (if more than 10% of these genes gave positive signals) as proposed  53  for MGS with >500 genes using momr R package. MGS taxonomical annotation was performed using all genes by sequence similarity using NCBI blastN; a species-level assignment was given if >50% of the genes matched the same reference genome of the NCBI database (November 2016 version) at a threshold of 95% of identity and 90% of gene length coverage.
05). Correlation analysis of gene richness and functional features (urdA, hutH) were assessed by Spearman's correlations. The most important mOTUs for the prediction of ImP levels were identified with cross-validated random forest models using ImP residuals adjusted for age, gender, BMI, diabetes status, ethnicity, and creatinine clearance with the randomForest R package. An optimal mtry and max node of 50 for the trees was determined using the mean squared error of ImP residuals in test samples as the outcome. The mean variable importance (using an increase in node purity) was determined with 100-fold cross-validation. Statistical analyses were carried out using R statistical analysis software version 3. 3. 2 (http://www. R-project. org/). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This study was supported by the  FP7 sponsored program  MetaCardis (305312),  JPI  (A healthy diet for a healthy life;  2017-01996_3 ), as well as  Transatlantic Networks of Excellence Award  from the  Leducq Foundation  ( 17CVD01 ),  Swedish Research Council  ( 2019-01599 ),  Swedish Heart Lung Foundation  ( 20180600 ),  Knut and Alice Wallenberg Foundation  ( 2017. 0026 ), the  Novo Nordisk Foundation  ( NNF19OC0057271 ,  NNF17OC0028232 , and  NNF15OC0016798 ), grants from the  Swedish state  under the.
Data availability Gender and center data were removed from source data files in order to maintain participant confidentiality. These data can accessed by a request to Professor Karine Clement. Raw sequencing data used in this study have been deposited in the EMBL-EBI European Nucleotide Archive (ENA) under accession numbers PRJEB37249 and PRJEB38742. For clinical cohort-related questions, contact K. C. Source data are provided with this paper.
As the horizontal resolution ranges between the different models (from 0. 95 o x 1. 25 o to 2. 8 o x 2. 8 o ), all the data were brought to a common 2. 8 o x 2. 8 o spatial grid using bilinear interpolation prior to processing. Moreover, as the minimum time period covered by the simulations is 30 years, for all simulations the first 30 years were selected for consistency. Results from the ensemble of all the models are presented within the manuscript on an annual and seasonal basis (winter vs summer) while results for each model separately are given in the supplement. The statistical significance of the results at the 95% confidence level is checked using a paired sample two-sided t-test. To decompose the effect of different present-day aerosol types on early industrial climate, supplementary data from other 3 experiments, namely piClim-SO2 (all forcings set to 1850 but using SO2 precursor emissions of the year 2014), piClim-BC (all forcings set to 1850 using BC precursor emissions of the year 2014) and piClim-OC (all forcings set to 1850 using OC precursor emissions of the year 2014), were used.
At the time this manuscript was written, there were available data from 3 models only (CNRM-ESM2-1, MRI-ESM2-0 and NorESM2-LM). Similarly, by subtracting the piClim-control simulation from these 3 simulations, the fast responses of pre-industrial climate to present-day sulfates, BC and OC aerosols are calculated. Supporting information for each model, the corresponding experiments and the model basic references/dois are shown in Table  1. Taking into consideration that the perturbation experiments to the control simulation are based on emissions for aerosol precursors set to present-day (2014), Figure  1  shows the annual SO2 and BC emissions for 2014 used in piClim-aer simulations as well as the differences from their respective emissions for year 1850 used in piClim-control simulations. Figure  1  is based on the emissions used in CNRM-ESM2-1, but the emissions are similar for the rest of the models used here, indicating that the largest present-day sources of SO2 are over East Asia, India, North America and Europe, while for BC over https://doi.
Acknowledgements This research was funded by the project " PANhellenic infrastructure for Atmospheric Composition and climatE change " ( MIS 5021516 ) which is implemented under the Action " Reinforcement of the Research and Innovation Infrastructure ", funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and cofinanced by Greece and the  European Union  ( European Regional Development Fund). D. O. and M. S. were supported by the  Research Council of Norway  (grant nos. 229771 ,  285003 , and  285013 ), by  Notur/NorStore  ( NN2345K  and  NS2345K ), and through  EU H2020  grant no. 280060. https://doi. org/10. 5194/acp-2019-1201Preprint. Discussion started: 3 February 2020 c Author(s)  2020. CC BY 4. 0 License.
Between-group comparisons were performed using Fisher's exact test for categorical data or Mann-Whitney test for quantitative data. Bonferroni or false discovery rate correction methods were applied for multiple comparisons (P. adj). All P-values corresponded to two-tailed P-values. A P-value < 0. 05 was considered to be statistically significant. Statistical analyses and graphical representations were performed using either R v3. 6. 3 or GraphPad Prism v7. 0a software (GraphPad Software, Inc).
rRNA 2 O-methylation evolution and mapping To further characterize the two classes of human rRNA 2 O-methylated sites, their conservation during evolution was compared using box C/D snoRNA guides as surrogates of rRNA 2 O-methylated sites, since to date availability of rRNA 2 O-methylation profiles is limited to only few organisms. Three evolution groups were established using the snoRNA-LBME-db database (www-snorna. biotoul. fr). Using Fisher's exact test, enrichment analysis was performed to compare the proportion of stable versus variable sites amongst the three groups of rRNA 2 O-methylated sites. The rRNA 2 O-methylated sites were mapped on the structure of the HeLa cancer cell human ribosome determined by cryo-EM  (16) (17) (18)  and images were drawn using the PyMol software. Observations were based on previous reported three-dimensional molecular docking analysis of tRNAs or ribosome-associated factors, as already discussed in (ref  [16] [17] [18].
Similarly, these two classes of most stable and most variable sites derived from the 195 breast tumours were observed in a series of 7 mammoplasties derived from healthy female donors, most of the sites being classified in the same classes with the except of few ones (Supplementary Figure  S5 ). The difference might come either from the small size of this series or from the fact that variability in rRNA 2 O-methylation might be tissue-specific, mammoplasties mostly corresponding to adipocyte cells. These data show that rRNA 2 O-methylation varies between human samples and demonstrate the co-existence of stable and variable classes of rRNA 2 O-methylated sites within human samples.
Evaluation of the structural impact of the two classes of rRNA 2 O-methylation sites To further characterize the two classes of human rRNA 2 O-methylated positions identified in the 195 breast tumours, we compared their conservation during evolution. Amongst the 106 rRNA 2 O-methylation sites described in humans, some sites have also been identified in yeast and bacteria (snoRNA-LBME-db database, www-snorna. biotoul. fr). We thus compared the proportion of either 'stable' or 'variable' sites classified on the basis of our data, amongst three groups: the group of rRNA 2 O-methylated sites only detected in humans ('humans', n = 68); the group of rRNA 2 O-methylated sites detected both in humans and yeast ('eukaryotes', n = 35); and the group of rRNA 2 Omethylated sites conserved in humans, yeast and bacteria ('universal', n = 3; see also annotation on the human ribosome structure in ref 17).
ACKNOWLEDGEMENTS We thank  Brigitte Manship  for English editing. Author contributions:  V. Marcel ,  J. K. ,  V. Marchand , H. P. , F. N. V. L. , L. A. , V. B. I. , P. L. M. , D. M. and L. T. performed and analysed experiments. V. Marcel , J. K. , H. P. , L. T. , Y. M. and A. V. performed statistical analyses. F. N. V. L. , V. S. , S. E. B. , A. D. , L. B. J. , A. M. T. , J. C. B. , T. D. and F. A. provide human samples, clinical data and cellular models. V. Marcel , J. K. , S. K. N. , F. C. , A. P. , B. O. K. and J. J. D. analysed and interpreted the data. V. Marcel  and  J. J. D. shaped the research question and coordinated the project. V. Marcel , J. K. , F. C. and A. V. supervised the daily experiments. V. Marcel , A. P. and J. J. D. raised funding. V. Marcel , J. K. , S. K. N. , B. P.
2C ), which allowed a large field of view (≈ 700 µm × 700 µm). Movies were analyzed by using particle image velocimetry (PIV). We used a fluorescent motility wild-type strain of E. coli, AD21, dispersed in a minimal medium that prevented growth, but enabled motility for the time of the experiment (typically 0. cell counting. The corresponding volume fraction was calculated by using a measured average cell body volume of VB = 1. 4 µm 3  (34). Size Dependence of Viscosity Reduction. Fig. 3  displays the viscosity of E. coli suspensions as a function of the shear rate γ for three different φ, each at three gap sizes, H = 240, 500, and 730 µm. The η( γ) data at the two higher bacterial densities were similar to those reported before (  13 ) at H = 500 µm. At the lowest density, our apparatus could not reach the low-shear plateau.
4 B-G. At φ 0. 75%, velocity gradients were linear within experimental uncertainties. Above this density, pronounced nonlinearities developed in the confinement gap. To quantify this transition, we calculated the SDs σ of ∆Vx (z ) = Vx (z ) -γappz over the entire z range, where the applied shear rate was obtained from γapp = Vx (zcone)/zcone (Fig. 4H ). Taken together, the data from five different experiments suggest that σ stays at a noise floor of ≈ 0. 43 at low cell densities until φ ≈ 0. 75% and then rises, consistent with where the density at which the low-η data extrapolate to 0. Importantly, visual inspection revealed large-scale correlated motions above this cell concentration. However, the small field of view in our setup (≈ 180 µm × 90 µm) ruled out reliable PIV on our rheoimaging datasets.
3 ) It was, however, not possible to decrease the gap size below 240 µm in this device. Observations at smaller gaps were, however, possible in our rheoimaging setup. Indeed, because we utilized a cone-plate geometry, shearing at a continuum of gap heights could be studied in a single experiment simply by moving the monitoring position radially. For φ ≈ 1. 5%, we observed strongly nonlinear velocity gradients at H ≈ 200 µm and H ≈ 170 µm, but linearity at H ≈ 100 µm. In Fig. 1 , we compare the predicted CKT stability boundaries against our measurements. The threshold for τ = 2 ± 0. 5 s credibly accounts for three datasets: our observation of the onset of collective motion in quiescent ( γ = 0) cell suspensions sealed in capillaries, our observation of the onset of nonlinear flow profiles in cone-plate rheoimaging at γ = 0.