dataesr/scientific-paragraphs-categorization huggingface dataset:
The relative abundance of the Fibrobacteres phylum decreased from 2. 83% at D0 to 1. 78% at D2 (P = 0. 0242) and returned to its basal state at D7. The relative abundance of the Tenericutes phylum increased from D0 (0. 07%) to D2 (0. 24%; P = 0. 0434) and returned to its basal range at D7. Modifications were also  highlighted in the feces of horses at the family (Supplementary Table  2 ) and genus (Supplementary Table  3 ) levels according to day. Among the 39 families and the 99 genera observed, 20 and 39 (representing 74. 95% and 46. 18% of the total family and genus relative abundances, respectively) showed differences related to the day. Using PLS-DA treatments of the genomic dataset of fecal samples, differences between the relative abundances of fecal bacterial populations were reported for each day at the genus level (Figure  2 ).
Regardless of supplementation, the PLS-DA score plots revealed clear disparities in the genomic datasets and highlighted clustering based on D2 in bacterial community structure along the first and second X-variate axes. [Eubacterium] ruminantium group, Christensenellaceae R-7 group, Lachnoclostridium, Lachnospiraceae UCG-003, Mailhella, Noviherbaspirillum, and Ruminococcaceae UCG-010 were identified as discriminant genera (VIP > 0. 2 and P ≤ 0. 05). Awe found a day × supplementation interaction for the relative abundance of Treponema 2 genus and for upper taxonomic levels (Spirochetes > Spirochetes > Spirochaetales > Spirochaetaceae; P = 0. 0058) at D7 and D14 (Supplementary Figure  3A ). The relative abundances of the Anaerovibrio genus and the higher taxonomic levels (Negativicutes > Selenomonadales > Veillonellaceae) were greater in the feces of horses supplemented with Dose 5 (P = 0.
ACKNOWLEDGMENTS We would like to thank the technical staff of  Agrosup Dijon  for taking care of all horses during the experimental trial ( Claire Sivry ,  Manon Guy-Coquille , and  Bernard Chateau ), contributing to the sampling and laboratory analyses ( Alicia Froidurot ,  Sylviane Mahe ,  Marie-Claire De Vos Franzin , and  Emmanuel Jacotot ). We also thank  Agathe Martin  ( Lab To Field ) for the technical and logistical support during all the trials. We are grateful to  Caroline Peltier ,  Chloé Roullier-Gall , and  Cedric Grangeteau  ( University Bourgogne Franche-Comté, AgroSup Dijon, PAM UMR ) for their helpful statistical skills. We gratefully acknowledge  Serge Monier  for supporting the development of cytokine analysis (Flow Cytometry Core Facility, INSERM,  UMR 1231 ). We also thank the  INRAE GenoToul Platform (Toulouse, France ; http://bioinfo.
Statistical analysis Data were analysed using JMP Pro 14 (SAS Institute Inc. , Cary, NC). Univariable analysis was performed to evaluate the success of the randomisation process. The control and treatment groups were compared to detect differences in days in milk at enrolment, parity, mobility score at enrolment, mobility score assessor, average milk yield at enrolment, presence of sole ulceration, presence of white line disease, presence of sole haemorrhage and presence of DD stage M2. Analysis of variance was used for continuous outcomes. Chi-square tests were employed for binary or categorical outcomes. Univariable analyses were undertaken between variables and the main outcome variables of interest before multivariable regression models were constructed. Parity was fitted in all models as a categorical variable with two levels (1 for primiparous and 2 for multiparous animals).
RESULTS One hundred and fifty-eight cows were enrolled in the study, 90 cows were enrolled in the treatment group and 68 were in the control group. Randomisation lists were prepared for each farm and for the approximate number of animals we were expecting to enrol in each farm. We ended up enrolling fewer animals due to COVID-19 restrictions and this led to unbalanced T and C groups in two farms. Had we been able to continue enrolling animals as originally planned, our groups would have been more balanced. Table  1  shows summary data for the study population. A total of 310 mobility scoring assessments were performed. Out of those assessments, 285 were performed by the main assessor of mobility in the project (C. Bedford) and 25 were performed by substitute assessors (M. Barden, n = 7; B.
A U T H O R C O N T R I B U T I O N S KK collected field data, assisted data analysis and co-wrote the first draft of the manuscript, AA collected field data and co-wrote the first draft of the manuscript, CB collected field data, TM assisted field data collection, MB assisted field data collection, BEG assisted field data collection, DA offered advised regarding study design, KT offered advised regarding study design, VSM performed statistical analyses, AC offered advised regarding study design and GO (corresponding author) designed and supervised the study and assisted with the statistical analysis. All authors critically evaluated the manuscript and approved the submitted version.
Mathematical description by compartment Each compartment is characterised by variables dependent on parameters of the compartment itself, variables of other compartments, and exogenous variables. These compartments also serve to calculate output variables. Unless otherwise stated, mass flows are nitrogen and annual values. The model has been coded in R language and solved using the "deSolve" package. The code and the list of data to be collected as input are available in table format upon request. More explanations and equations can be found in the Supplementary Material.
Parameters We collected from the literature the most recent parameter values for the considered SAR. A list of the parameters with their sources and nominal values is available in the Supplementary Material. We performed a global sensitivity analysis using the R "FME" package. We considered an interval of ±20% of the nominal values for the repartition coefficients, humification factors, and C:N ratios as well as an interval of ±10% of the nominal values for nitrogen content, yield, harvest index, shoot-to-root ratio, livestock diet, and livestock production coefficients. We did not apply uncertainties to the initial livestock numbers and plant areas. More details regarding the estimation of parameters, estimation methods and data for the initialisation of state variables, the year of parameter values, nominal values of parameters, parameter uncertainty intervals are available in the Supplementary Material.
Acknowledgments The authors are thankful to the anonymous reviewers who helped to improve the quality of the manuscript. The authors are also grateful to  Pytrik Reidsma  for her very useful comments on earlier versions of the manuscript. CP was supported by the  European Commission   Horizon 2020 Research & Innovation Programme  under grant agreement number  727520  (project  SURE-Farm ). FA was supported by the  French  state aid managed by the  National Research Agency (ANR)  under the " Investissements d'avenir" Programme  with the reference number  ANR-16-CONV-0003. The funders are not accountable for the content of this research and had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2 ) using a weather station (Cimel, 516i, INRAE) located on the experimental site. The experimental dataset was divided into two successive 3-year periods: the first period  (2011) (2012) (2013)  from October 2010 to September 2013 and the second period  (2014) (2015) (2016)  from October 2013 to September 2016. Subsequently, a cropping season was defined as the period from the burial of residues   (2014) (2015) (2016). In addition, the second period had higher mean temperature (14. 3 °C) than the first one (13. 7 °C).
Production factors for monitoring and meeting cropping system objectives Several production factors (Table  2 ) were applied to meet the objectives of the cropping systems. The treatment frequency index (TFI) was used to assess pesticide pressure. To characterize the main type of pesticide pressure accurately, TFI was separated into three components according to the taxon (weed, fungal disease, or insect) targeted: TFI Herbicide , TFI Fungicide , and TFI Insecticide. For each cropping system, toxicity of pesticides-the total number of applications of molecules registered as toxic to humans (i. e. , corrosive, acutely toxic, harmful, an irritant, hazardous to health, carcinogenic, or mutagenic)-was quantified. Toxicity was defined based on the information provided by the material safety data sheet for each product.
e. , protein content for durum wheat and oil content for sunflower). For a given species, we distinguished (i) the grain yield at the crop scale, corresponding to the quantity produced on 1 ha cultivated with this species (or mixture), and (ii) the quantity of grain produced by this species on 1 ha of the cropping system, since each species represents only 33% of the surface area for the prototypes and 50% for the COM. Similarly, we distinguished (i) the protein and oil content of the grain and (ii) the amount of protein and oil produced on 1 ha of the cropping system. For the COM, because of sample losses, the protein content of durum wheat grain was not measured in 2011, 2012, and 2014, nor was the oil content of sunflower grain in 2011 and 2012. Consequently, we used the mean contents of all prototypes for these missing data in order to consider the year effect on grain protein content and oil content.
The procedural limits of detection (LOD) and quantification (LOQ) of the whole extraction method were evaluated from the analysis of a blank sample. The non-spiked model sediment was extracted with the optimised MSPD process to determine the sample noise near the peaks of interest. LODs and LOQs were then estimated as the average response (n=3) for each spiked analyte multiplied by three times or ten times the standard deviation of the blank sample noise, respectively. Table  1  gives the procedural LODs and LOQs. LODs were compared with literature data obtained from similar environmental matrices for PAHs, but from biological or food samples for PCBs. LODs were ranging from 0. 08 to 0. 22 ng g -1 (dw) for PAHs, that was better than the values found in the study using also a MS detector (Table  1 )  [20] ; LODs were lower in the studies that analysed PAHs with a fluorescence detector, more sensitive than MS  [18, 27].
However, for the CRM104 certified reference material, MAE gave significantly better results for Naph (Table  S3 ). The discrepancy with the CNS391 certified value might be explained observing the date of the CNS391 certificate, where data were listed in 2013, and the certificate has not been reconsidered for many years, whereas the CRM104 certificate was regularly updated. It is well known that the lower molecular weight PAHs can be volatilized or degraded, even in cold and dry storage conditions, and it was probably the case for Naph in CNS391 material. The total amount of the Σ15PAHs (3415 ng g -1 ) obtained from MSPD was lower than that mentioned on the CNS391 sediment certificate (3985 ng g -1 ) (Table  2 ). Some values of individual PAHs were not in the intervals of prediction of the certificate (8/15) and had mean extracted amounts lower than the certified values.
As the advantages of CA and gridded crop model approaches are complementary, we integrate the CA and the global crop model results in a common statistical framework to assess relative differences between irrigated and rainfed yield (ΔY, see Methods section). The CA attainable yield dataset comes from updated Mueller et al. 10  (see Methods section) and crop model results from the Global Gridded Crop Model (GGCM) inter-comparison project  30  (see Methods section), with 10 global gridded crop models. A proper integration of different approaches could lead to better estimates outperform either approach  31. However, traditional regression-based method relies on strong assumptions of known driving factors  31. Here, with Bayesian Model Averaging (BMA) method  32  (see Methods section), we reanalyze ΔY for wheat and maize, in which we do not assume any driver to its spatial variations in prior.
Results and discussion Reanalyzed ΔY. First, we contested ΔY from CA, GGCMs, and BMA reanalysis against an independent dataset of irrigated and rainfed yield over US based on county yield surveys from the US Department of Agriculture (referred to as gridded-US hereafter; Schauberger et al. 12  ). As Fig. 1  shows, ΔY from CA (squared bias of 0. 2% for both wheat and maize) is less biased than that from GGCMs (squared bias of 4. 1% for wheat and 3. 8% for maize, respectively), being distributed on both side of the 1:1 line with gridded-US (Fig. 1a, d ). Although ΔY from GGCMs shows a systematic overestimation with respect to the gridded-US data (Fig. 1b, e ), its spatial pattern is better correlated with observations than the CA results (r = 0. 67 for wheat, r = 0. 72 for maize in Fig. 1b , e compared to r = 0.
See Supplementary Fig. 5  for spatial pattern of different thresholds of maximum runoff usage. MIRCA2000 irrigation dataset  46. The rainfed and irrigated attainable yield estimates used for this analysis are grid cell averages derived from replicating this sampling procedure for varying numbers of climate zones, from 100 to 400 (10 × 10 to 20 × 20 growing degree day and precipitation increments, see Supplementary Fig. 8 ). A limitation of this dataset is that the irrigated attainable yields may not be different from the rainfed attainable yield estimates if little area is actually irrigated within a climate zone. Further, we note that for the comparison with gridded-US data, the underlying yield dataset upon which these estimates are based  33  does include county-level USDA yield data (although these data are a combination of rainfed and irrigated observations).
9. A full list of models, their characteristics and their references can be found in Supplementary Table  2. However, all models follow the same simulation protocol  30  with the same forcing of gridded climate (AgMERRA) and management (planting date and fertilization rate) in order to minimize the impacts of difference in model drivers. Irrigation simulated by GGCMs follow the protocol with automatic irrigation that do not limit irrigation water supply  30  , though different models may slightly vary in the setting, which were detailed in Muller et al. 47. All models provided "harmnon" simulations, which simulate historical crop yield forced by historical climate dataset but assuming unlimited nutrients supply to the croplands  30  are used in the analysis. We use "harmnon" simulations with unlimited fertilizer supply because (1) it helps to avoid interactive effects of fertilization and irrigation, and (2) it is closer to the assumption used in the CA approach, making the two datasets more directly comparable.
Crop rotations were not considered in current phase of GGCMI, largely due to lack of global gridded dataset of the crop rotation practices. Different GGCMs were allowed to use their own soil parameters. On the one hand, this comprises a source of uncertainties in simulated yield (e. g. Folberth et al. 29  ); On the other hand, the ensemble of GGCMs with a diversity of soil parameters represents a good range of soil impacts on the crop simulations. We noted that some GGCMs also simulated other crops, including, for example, rice, rapseed, and sugarcane. However, we did not consider these due to limited number of models simulating these crops and validation datasets to evaluate model performance. In addition, flooding irrigation practices for rice over Asia has several functional features, such as facilitating transplanting.
These features have not yet been well represented by GGCMs. Gridded US dataset. The gridded rainfed and irrigated crop yield over US (gridded-US) dataset is based on county rainfed and irrigated yield statistics provided by the US Department of Agriculture (USDA), post-processed by Elliott et al. The statistics were gridded to 0. 25°according to the weighted crop area over each county. The dataset covers 1980-2010, and we use the average across this time period, which is the common period between the field data used by CA and gridded crop model simulations. Further details of the dataset can be found in Schauberger et al. 12. The gridded-US dataset is suitable for evaluating our reanalysis because (1) it is largely independent from both products we used in this study, and (2) unlike in many less-developed countries where rainfed farming is often paired with a significantly lower management intensity (e.
g. less fertilizer input and pest control measures), the rainfed and irrigated management over the same county in US is more often associated with access to water resources. It may thus more closely approximate spatial variations in the contribution of irrigation alone to crop yield instead of the contribution of co-varying factors. To compare results from CA and GGCMs with gridded-US dataset, we decomposed mean squared deviation (MSD ¼ P n i¼1 ðx i À y i Þ 2 =n, Eq. (  1 )) of ΔY between CA/GGCM/the reanalysis and gridded-US dataset into the squared bias (SB ¼ ð x À yÞ 2 , Eq. (  2 )) and mean squared variation (MSV ¼ P n i¼1 x i À x ð ÞÀ y i À y ð Þ ½ 2 =n, Eq. (  3 )), where x and y were the means of x i and y i (i = 1,2…n) respectively  48  , and x i and y i in this study represented gridded ΔY estimates from different data sources.
River discharge. The global river discharge dataset (UNH-GRDC) used in this study is a reanalyzed product combining observed river discharge collected by Global Runoff Data Centre (GRDC) and river discharge simulations by a hydrology model (Water Balance Model). This dataset has composite river discharge fields, which preserve the accuracy of discharge measurements as well as give the spatial and temporal patterns of it. Since the dataset has already accounted contemporary utilization of river discharge for all purposes, including irrigation, it serves in this study as an estimate of available water resources that can potentially be used to irrigate the rainfed croplands. UNH-GRDC dataset has a spatial resolution of 0. 5°a nd temporal resolution of 1 month over the entire global land surface  34.
The idea of BMA is to derive the posterior probability of each model given a target dataset. The posterior probability of each model is then used to calculate model weights (W i for the ith model) in the model ensemble and results in reanalyzed estimates that "best" combine information from different datasets. The derivation of W i follows the Bayesian equation (Eq. (  5 )) W i ¼ P M i jO ð Þ/P OjM i ð ÞP M i ð Þ ð5Þ where M i is the ith model estimates on ΔY and O is the ΔY estimated from the CA approach. In the prior (P(M i )), we assumed each model is equally skillful in projecting ΔY. The conditional probability P(O|M i ) is therefore proportional to the misfits between the ith model simulation and climate analog estimates. With W i , the posterior probability for the best estimate of ΔY in the reanalysis will follows Eq.
(  6 ), P ΔYjM 1 ; M 2 ; ; M 11 ; O ð Þ ¼ X 11 i¼1 W i P ΔYjM i ; O ð Þ ð6Þ where P(ΔY|M i ,O) is conditional probability density function of ΔY based on M i and O. A Monte-carlo Markov chain method is used to derive the optimal W i for each model  32. Potential evapotranspiration. In addition to temperature, we use PET as a surrogate to estimate climatic demand of water from croplands. We follow the modified Haude equation  49  to derive PET, which has been proven effective in building statistical models for regional crop yield  50. The climatic variables used in calculating PET comes from AgMERRA dataset, which was also the climate forcing for GGCM simulations  30. PET can also be calculated with other algorithms, such as widely used Penman-Monteith equations. However, AgMERRA does not provide the surface air pressure and downward long-wave radiation, which make deriving vapor pressure deficit and net radiation required in Penman-Monteith equations difficult.
We therefore used another PET dataset, which apply Penman-Monteith equations on CRU dataset  51  , as another sensitivity test to climatic water demand. Balance between irrigation requirements and runoff supply. We first obtain crop water demand calculated by each GGCM, i. e. , water demand without constrained by actual water availability  15, 30. To ensure consistency between reanalyzed ΔY and water demand, we use the same model weights (see Bayesian model average) to calculate reanalyzed water demands used in the main text. The water demands estimated by GGCMs assume 100% irrigation efficiency (no conveyance or application losses), which is not ideal when comparing with available water resources. Therefore, at each grid, we divide the reanalyzed crop water demand by crop-specific irrigation efficiencies  52  to attain requested irrigation demand from surface water bodies.
The crop-specific irrigation efficiency  52  is obtained from LPJmL simulation constrained by AQUASTAT data on irrigation system distribution. On the water supply side, the UNH-GRDC dataset considers today's human water withdrawal for industrial, domestic, and agricultural usage from river runoff. River ecosystems provide life-supporting functions that depend on maintaining minimum river discharge, i. e. , environmental flow requirements (EFRs)  53, 54. However, the quantification of EFRs is not trivial as estimation methods vary, leading to a broad range of uncertainty (e. g. EFRs estimated by different methods ranges from 12 to 48% for Nile and 30 to 67% for the Amazon). A detailed account of such is beyond the scope of this study and can be found in Jägermeyr et al. 11. The large uncertainties in EFRs and lack of data for other potential water usages make it difficult to estimate the overall sustainable water resources available for irrigating wheat and maize.
Data availability GGCMI model data can be accessed under open data license CC BY 4. 0 in https:// zenodo. org (Supplementary Table  4 ). Attainable irrigated and rainfed yield by Climate Analog approach can be accessed upon request to Prof. N Mueller (nathan. mueller@rams. colostate. edu). Global gridded river discharge can be downloaded from http://www. grdc. sr. unh. edu/.
Production and Purification of GII. 4 VLPs The experiments were performed using GII. 4 VLPs (Cairo 4 strain, a 2007 Osaka variant, GenBank: EU876884. 1), a GII. 4 epidemic variant that have circulated in recent years  (Kamel et al. , 2009). The GII. 4 VLPs were produced using the baculovirusexpressed VP1 system and purified using cesium chloride gradient to a final concentration of 1. 36 g. ml -1 followed by a centrifugation step at 38,000 rpm for 22 h at 10°C, as previously described  (Belliot et al. , 2001; de Rougemont et al. , 2011). The concentration of GII. 4 VLPs was determined using a NanoDrop 2000 spectrophotometer (Nanodrop, Thermo Fisher Scientific, Waltham, MA, United States) at 280 nm (applicable to proteins that contain tryptophan, tyrosine, and phenylalanine) in accordance with the Beer-Lambert equation (i.
TEM Observations TEM was used to observe 10 μl of untreated/oxidation-treated GII. 4 VLPs following negative staining with phosphotungstic acid, as previously described  (Loison et al. , 2016). The number of GII. 4 VLPs was estimated for each condition using eight representative pictures per field at a magnification of X 15,000. The size of the GII. 4 VLPs was quantified using ImageJ software to produce eight representative pictures per field for each condition at a magnification of X 27,500  (Schindelin et al. , 2012). This procedure was applied to each oxidant at each concentration in two independent experiments conducted on different days.