dataesr/scientific-paragraphs-categorization huggingface dataset:
Weather data included incident solar radiation, maximum (Tmax) and minimum (Tmin) air temperature, humidity, wind speed, and precipitation. Weather data were screened for erroneous and/or missing information using rigorous quality-control protocols available at: http://www. yieldgap. org/web/guest/methods-weather-data. We followed two approaches to derive 10-20 years weather data for those buffer zones for which such long-term weather records were unavailable. In those buffers in which measured weather data were available for < 3 years, we generated longer records following the propagation technique described in  van Wart et al. (2015). Briefly, this technique consists of using ca. 3 years of location-specific measured daily weather to correct for bias in gridded Tmax and Tmin from the Prediction of Worldwide Energy Resource (NASA-POWER) dataset while missing solar radiation and precipitation data are filled using uncorrected data from NASA POWER (NASA, 2017) and Tropical Rainfall Measuring Mission  (Kummerow et al.
, 2000)  databases, respectively. In buffer zones without any weather data, we used uncorrected gridded weather data from NASA-POWER. The first approach (i. e. , propagation) was followed for 8% (maize) and 3% (wheat) of the buffer zones, and the second one (i. e. , uncorrected NASA-POWER data) for 6% (maize) and 2% (wheat) of the buffer zones. Within each buffer zone, dominant soil type x crop sequence combinations were simulated. Yw is sensitive to soil hydraulic properties that govern plant-available water retention characteristics, and landscape and soil properties that influence infiltration rate and runoff. Soil input data used by different crop models to simulate Yw differ to some extent. However, basic soil information required by all models consists of (i) rootable soil depth, (ii) available water holding capacity (difference in water content between field capacity and permanent wilting point), either as direct input or estimated from soil texture using pedotransfer functions, and (iii) terrain slope and drainage class (for calculating surface runoff).
We used high-quality soil maps with functional soil properties where these were available (e. g. north America, Europe, and Oceania). Otherwise, we used the global ISRIC-WISE soil databases such as ISRIC-WISE  (Batjes, 2012)  and AfSIS  (Leenaars et al. , 2018). Dominant soil types were selected to achieve > 50% area coverage per buffer zone  (Grassini et al. , 2015b; van Bussel et al. , 2015). Details on selected soil data sources can be found in http://www. yieldgap. org/ web/guest/methods-soil-series. Agronomic information including crop sequences, commonly used cultivars, crop cycle length, and sowing date window were obtained from local experts. For some regions (e. g. , sub-Saharan Africa), sowing date was simulated dynamically for each buffer-year based on daily precipitation dynamics within the reported sowing window.
Official statistics on Ya were collected for each crop-country combination at the finest spatial resolution for which these data were available (e. g. , county, department, or sub-district depending upon country). Ya was determined by including as many recent years of data as possible to account for weather variability, while avoiding the trend bias due to technology or climate change  (Calviño and Sadras, 2002; Grassini et al. , 2015b; van Ittersum et al. , 2013). In all cases, Ya was calculated with at least 3 recent years of yield data. Details on criteria for selection of data sources can be found elsewhere  (Grassini et al. , 2015b). Water productivity gap (WPg) was calculated as the difference between WPw and WPa and reported as a percentage of WPw. Yield potential (Yp), Yw, and ETw were simulated for each dominant crop sequence and soil type within each buffer zone using 10-20 years of daily weather data.
Comparison with published data and analysis of variation across environments We compared our estimates of WPw against boundary functions reported in the literature  (Connor et al. , 2011; Grassini et al. , 2009b; Sadras et al. , 2015). Selected boundary functions were also compared against a large database of measured data from rainfed and irrigated field-grown crops  (Grassini et al. , 2009b; Sadras and Angus, 2006; Zwart and Bastiaanssen, 2004 and references therein). Irrigated WP data were included to strengthen the comparison, especialy in the upper range of ETw. The boundary function was assumed to have an x-intercept (i. e. , minimum soil evaporation) of 75 mm for maize and 60 mm for wheat, and a slope (i. e. , transpiration-use efficiency) set at 42 kg ha -1 mm -1 for maize and 34 kg ha -1 mm -1 for wheat  (Connor et al.
, 2011; Grassini et al. , 2009b; Sadras and Angus, 2006). Slopes of boundary functions and data from the literature were also expressed at 15. 5% (maize) and 13. 5% (wheat) grain moisture content. Causes for variation in WPw across CZs were investigated for each crop. Linear regression was used to reveal associations between WPw and several possible factors including (i) average reference grass-based evapotranspiration (ETo) and daytime VPD, (ii) fraction of ETw after flowering (ETw POSTF : ETw), (iii) fraction of ETw evaporated from the soil (Es : ETw), and (iv) water stress index around flowering ( ± 10 days), which indicates the degree of crop stress due to water limitation  (Cooper et al. , 1983; Passioura and Angus, 2010; Steduto et al. , 2012). Daily water stress index was calculated as one minus the ratio between simulated water-limited transpiration and non-water limited transpiration.
Acknowledgements This study was supported by the  Bill and Melinda Gates Foundation , the  Daugherty Water for Food Global Institute at University of Nebraska-Lincoln (UNL ),  Wageningen University & Research , and the  CGIAR research program on Climate Change, Agriculture, and Food Security (CCAFS). We thank  Juan P. Monzon ,  Fernando Aramburu Merlos ,  Zvi Hochman ,  Marloes van Loon , and  Haishun Yang  for providing crop simulations results and weather data.
This indicates that a positive adjustment for dairy breed type would be important in a future European grading system. Moreover, as the breed information is available to the industry, it would cost very little to record. It is important to note that the magnitude of the effect varied by muscle in this study, being up to 7 points for some cuts and nil for others. This may be a result of true variation between muscles or a reflection of differences in the amount of data available for certain muscles, either in the European database or the Australian database. It is important to note that the effect of muscle type on eating quality explains far more of the variation in eating quality than breed, or indeed than any of the other factors discussed in this paper  (Jurie et al. , 2007). Therefore, further work in both continents to address the limited data available within some muscles would increase the accuracy of the grading system in both Australia and the European Union.
Acknowledgements This research was supported by  Meat and Livestock Australia and Murdoch University. Data were obtained through the financial contributions of the European research project  ProSafeBeef  (contract no. FOOD- CT-2006-36241 ), the  Polish ProOptiBeef Farm  to Fork project funded by the  EU Innovative  ( POIG. 01. 03. 01-00-204/ 09 ), the  French 'Direction Générale de l'Alimentation' and FranceAgriMer. For the Irish data , the authors acknowledge the financial support of the  Department of Agriculture  and the  Marine (DAFM)  under the  Food Institutional Research Measure (FIRM). Furthermore, this project would not have been possible without the practical support of the  Association Institut du Charolais , the  Syndicat de Défense et du promotion de la Viande de Boeuf de Charolles  and the gourmet restaurants ' Jean Denaud ' and representatives of the beef industry across Europe.
NIRS data Wheat: NIRS data were obtained on flag leaves and harvested grains from the two treatments of the drought trial in Clermont-Ferrand (France) in 2015/2016. For each variety in each treatment, twenty flag leaves were sampled on one plot at 200 degree days after flowering. The samples were oven dried at 60 °C for 48 h. Leaves were milled (Falaise miller, SARL Falaise, France), and the powder was analyzed with a FOSS NIRS 6500 (FOSS NIRSystems, Silver Spring, MD) and its corresponding softwares (ISIscan TM and WINisi  TM 4. 20). For each variety in each treatment, 200 g of grains harvested at one plot were analyzed with a FOSS NIRS XDS (FOSS NIRSystems, Silver Spring, MD) and its corresponding softwares (ISIscan TM and WINisi  TM 4. 20). For leaf powder and grain, absorbance was measured from 400 to 2500 nm with a step of 2 nm.
5varieties were removed from the dataset because their leaf absorbance was abnormal, resulting in a final panel of 223 varieties. The resulting spectra were loaded into R software 5 to be pretreated using custom R code. They were normalized (centered and scaled) and their first derivative was computed using a Savitzky-Golay filter  6  with a window size of 37 data points (74 nm) implemented in the R package signal  7. In the end, each variety in each treatment was characterized by a transformed spectrum of flag leaf powder and a transformed spectrum of grains. Poplar: NIRS was carried out on wood from stem sections collected at 1 m above ground on 2-year-old trees for 1,081 genotypes in three blocks at Orléans (total of 2,860 samples) and 792 genotypes in three blocks at Savigliano (total of 2,254 samples).
After harvest, the wood samples were oven dried at 30°C for several days, cut into small pieces with a big cutter and milled using a Retsch SM2000 cutting mill (Retsch, Haan, Germany) to pass through a 1-mm sieve. The wood samples were not debarked prior to milling. After stabilization, wood powders were placed into quartz cups for NIR collection with a Spectrum 400 spectrometer (Perkin Elmer, Waltham, MA, USA) and its corresponding software (Spectrum TM 6. 3. 5). For each sample, the measurement consisted of an average of 64 scans done while rotating the cups over the 10,000 cm -1 -4,000 cm -1 range with a resolution of 8 cm -1 and a zero-filling factor of 4, resulting in absorbance data every 2 cm -1. The resulting spectra were loaded into R software 5 to be processed using custom R code.
They were first restricted to the 8000 cm -1 -4000 cm -1 range because the most distant part of the spectra (8000 cm -1 -10,000 cm -1 ) appeared to be quite noisy. Then, the restricted spectra were normalized (centered and scaled), and their first derivative was computed using a Savitzky-Golay filter  6  with a window size of 37 data points (74 cm -1 ) implemented in the R package signal  7. Finally, these normalized and derived spectra were averaged by genotype at each site.
SNP data Wheat: The 228 wheat varieties were genotyped with the TaBW280K highthroughput genotyping array described in Rimbert et al. 8. This array was designed to cover both genic and intergenic regions of the three subgenomes. Markers with a minor allele frequency below 1%, or with a heterozygosity or missing rate above 5% were removed. Redundant markers were filtered out. Eventually, we obtained 84,259 SNPs, either polymorphic high resolution or off-target variants, with an average missing data rate of 0. 83%. Missing values were imputed as the marker frequency. Poplar: The poplar association population was genotyped with an Illumina Infinium BeadChip array 3, yielding 7,918 SNPs for 858 genotypes. Missing values were rare (0. 35%) and they were imputed with FImpute 9. The data were restricted to the subset of 562 genotypes with SNP data and NIRS data at both sites.
Phenotypic data Wheat: The 228 wheat varieties were phenotyped for heading date (HD) and grain yield (GY) at the two environments in which the NIRS analysis was conducted (drought experiment in Clermont-Ferrand 2015/2016). The subpanel of 161 varieties was phenotyped for the same traits in six independent environments. In each environment, the phenotypic data were adjusted for micro-environmental effects using the random effect block and when necessary by modeling spatial trends using two-dimensional penalized spline (P-spline) models as implemented in the R package SpATS  10. Poplar: The poplar association population was evaluated at each of the two sites for the following traits on up to six replicates by genotype: height at 2 years at Orléans (HT-ORL), circumference at 1 m above ground at 2 years at both sites (CIRC-ORL and CIRC-SAV), bud flush at both sites (BF-ORL and BF-SAV) and bud set at both sites (BS-ORL and BS-SAV) as discrete scores for a given day of the year (see Dillen et al.
11  and Rohde et al. 12  for details on the scales used) and resistance to rust at Orléans (RUST-ORL) as a discrete score of susceptibility on the most affected leaf of the tree and on a 1 to 8 scale. Within each site, the phenotypic data were adjusted for micro-environmental effects using random effect block and/or spatial position when needed following a visual inspection of spatial effects with a variogram as implemented in the R package breedR  13. Finally, the adjusted phenotypes were restricted to the subset of 562 genotypes with SNP and NIRS data for computing an averaged genotypic value for each trait by genotype within each site for further analyses. Association mapping was carried out along spectra considering the absorbance at a given wavelength as a trait in a bivariate setting and using previous estimates of genetic and residual variances (EMMAX philosophy as previously proposed in the multi-trait mixed-model approach  19  ).
The main difference between S1 and S2 was that in S1, NIRS could potentially capture both genetic and variances, leading to environment-specific predictions. For both scenarios, 5-and 8-fold cross-validation procedures repeated 20 times were used for poplar and wheat, respectively. A larger fold-number was considered for wheat in comparison to poplar because the sample size in the wheat dataset (n = 223 in the panel, and n=161 in the subpanel) was lower than the sample size in the poplar dataset (n = 562). Predictive ability was computed as the Pearson correlation between the predictions and adjusted means. For genomic predictions, we tested two complementary reference models: G-BLUP and Bayesian LASSO  [20] [21]. The underlying assumptions of these two models are that the SNP effects are normally distributed for G-BLUP, whereas Bayesian LASSO allows for departure from normality (i.
Expected genetic progress with genomic and phenomic selection in a simple example We ran simulations to illustrate the expected genetic progress with GS and PS that would be achieved in one cycle of selection for various combinations of costs and reliabilities. Reliability was defined as the squared correlation between true breeding values (TBV) and the genomic or NIRS estimated breeding values (EBV). We considered a situation in which a given budget (200,000 €) was available to predict the performances of selection candidates with NIRS or genotyping. Depending on the costs of the methods (DNA extraction and genotyping for GS or tissue sampling and NIRS acquisition for PS), we computed the number of selection candidates (N) that could be analyzed. The TBV and genomic or NIRS EBV of these N individuals were then sampled from a multivariate normal distribution with means equal to 0, variances equal to 1 and covariance equal to the square root of reliability (R package mvtnorm  23  ).
To provide concrete examples, we applied this simulation process with the reliabilities and costs that we experienced for wheat and poplar. GS costs were between 35 € and 50 € per individual for wheat and poplar, respectively, and PS costs were between 3 € and 2. 5 € per individual for wheat and poplar, respectively. Reliabilities were estimated as the square of predictive abilities estimated by crossvalidation divided by the heritability of the adjusted means. For each combination of trait, scenario, and NIRS data considered (tissue, environment), the increase in genetic progress using PS instead of GS was computed with the best performing GS model as a reference. Table  S3 : Increase of genetic progress (%) using PS instead of GS for poplar. The genetic progress of PS and GS was estimated with the estimated heritabilities, the costs that we experienced (2.
ACKNOWLEDGEMENTS The authors gratefully acknowledge the staff of the  INRA GBFOR experimental unit  for the establishment and management of the poplar experimental design in Orl éans, the collection of wood samples in each site, and their contribution to phenotypic measurements on poplars in Orléans;  Alasia Franco Vivai  staff for management of the poplar experimental plantation in Savigliano, and  M. Sabatti  and  F. Fabbrini  for their contribution to phenotypic measurements on poplars in Savigliano. We acknowledge the staff of the  INRA GenoBois platform  for the preparation of samples and collection of  NIRS  on wood samples and the staff of EPGV and BioForA for their contribution to obtaining SNP data on poplar. We would like to thank  J. Messaoud  for NIRS acquisition on wheat samples,  V.
Allard ,  B. Adam  and  D. Cormier  for implementation of the rain-out shelter experiment ( Phéno3C ,  INRA Clermont-Ferrand) , and  E. Heumez  ( UE GCIE ) for the experiment in Estrées-Mons. We would also like to thank  A. Chateigner ,  L. Sanchez ,  G. Charmet ,  V. Allard ,  P. Martre  and  S. Bouchet  for useful discussions and comments on the manuscript. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing resources. Establishment and management of the poplar experimental sites until harvests were carried out with financial support from the  NOVELTREE project  ( EU-FP7-211868 ). NIRS measurements on poplar wood samples were supported by the  SYBIOPOP  project funded by the  French National Research Agency  ( ANR-13-JSV6-0001 ).
Management of the wheat multi-environment trials was financially supported by the  French National Research National Agency  under Investment for the  Future  ( BreedWheat  project  ANR-10-BTBR-03 ) and by  FranceAgriMer. The Phéno3C platform was financially funded by the  French National Research National Agency  under the  Investment for the Future Phenome project  ( ANR-11-INBS-12 ) and by the  European Regional Development Fund  ( AV0011535 ).
1 ). We added the spectrum from strain SIT8 to our database (Fig. 1 ). Strain SIT8 exhibited a 100% 16S rRNA sequence identity with Fermentimonas caenicola strain ING2-E5B T (GenBank accession KP233810), the phylogenetically closest species with a validly published name in nomenclature (Fig. 2 ). The 16S rRNA sequence of strain SIT8 has been deposited in GenBank under number LN827535. Growth at different temperatures (29, 37 and 55 °C) was tested. Growth of the strain was tested in 5% sheep blood-enriched Columbia agar (bioMérieux) and Tryptic Soy agar (Becton-Dickinson, Le Pont-de-Claix, France) under anaerobic and microaerophilic conditions using the GENbag anaer and GENbag microaer systems, respectively (bioMérieux), and under aerobic conditions, with or without 5% CO 2. Growth was tested for salt tolerance, with 0-5, 50 and 100% (w/v) NaCl.
Chemotaxonomic data Cellular fatty acid methyl ester analysis was performed by GC/MS. Two samples were prepared with approximately 30 mg of bacterial biomass per tube harvested from several culture plates. Fatty acid methyl esters were prepared as described by Sasser  [5]. GC/MS analyses were carried out as described before  [6]. Briefly, fatty acid methyl esters were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500 -SQ 8 S, Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). Hexadecanoic acid is the most abundant fatty acid (45%). 9-Octadecenoic acid and 9,12-Octadecadienoic acid are also abundant unsaturated fatty acids (23 and 20% respectively) (Additional file 1: Table  S1 ).
Genome project history The strain was selected for sequencing on the basis its 16S rRNA similarity, phylogenetic position, and phenotypic differences with the other members of the family Porphyromonadaceae, and is part of a culturomics study of the human microbiome. It is the second published genome from the F. caenicola species. Table  2  shows the project information and its association with MIGS version 2. 0 compliance  [7]. The genome Genbank accession number is CTEJ01000000. The genome consists of 2 scaffolds. Growth conditions and DNA preparation Strain SIT8 (CSUR P1560) was sub-cultured on 5% sheep blood-enriched Columbia agar (bioMérieux) and grew in 24 h at 37 °C in anaerobic atmosphere. Eight Petri dishes were harvested and resuspended in 4x100μl of G2 buffer (EZ1 DNA Tissue kit, Qiagen).
Genome annotation Open Reading Frames were predicted using Prodigal  [8]  with default parameters but the predicted ORFs were excluded if they were spanning a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database [9] and the Clusters of Orthologous Groups databases  [10]  using BLASTP. The tRNAScanSE tool  [11]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [12]  and BLASTn against the GenBank database. Signal peptides and numbers of transmembrane helices were predicted using SignalP  [13]  and TMHMM  [14]  respectively. ORFans were identified if their BLASTP Evalue was lower than 1e-03 for alignment lengths greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05.
Insights from the genome sequence To date, one genome from the Fermentimonas genus has been published. Here, we compared the genome sequence of F. caenicola strains SIT8 (Genbank accession number CTEJ01000000) and ING2-E5B T (Genbank accession number NZ_LN515532). The draft genome of strain SIT8 (2. 87 Mb) has a larger size than that of strain ING2-E5B T (2. 85 Mb). The G + C content of strains SIT8 and ING2-E5B T are comparable (37% vs 37. 3%, respectively). The gene content of strain  SIT8 is lower than that of strain ING2-E5B T (2400 vs 2455, respectively). The ratio of genes per Mb of strain SIT8 is lower than that of strain ING2-E5B T (836 vs 861, respectively). The distribution of genes into COGs functional categories is comparable between strains SIT8 and ING2-E5B T (Table  4 ). The genomic comparison identified a pangenome of 2681 genes and core genome of 2096 genes.
23 121 5. 03 Energy production and conversion G 142 6. 03 143 5. 95 Carbohydrate transport and metabolism E 144 6. 12 143 5. 95 Amino acid transport and metabolism F 54 2. 29 55 2. 29 Nucleotide transport and metabolism H 77 3. 27 76 3. 16 Coenzyme transport and metabolism I 57 2. 42 58 2. 41 Lipid transport and metabolism P 118 5. 01 106 4. 41 Inorganic ion transport and metabolism Q 11 0. 47 13 0. 54 Secondary metabolites biosynthesis, transport and catabolism R 216 9. 18 213 8. 86 General function prediction only S 115 4. 89 126 5. 24 Function unknown - 661 28. 08 700 29. 1 Not in COGs COGs Clusters of Orthologous Groups database.
Image analysis. After the opening of the EUs, pictures of all CCs were taken and analysed using ImageJ software  37  (v. 1. 5, https://imagej. nih. gov/ij) to determine the root length and the orientation of the root apex. The root cap of Brassica napus seedlings contains seven cell layers from the quiescent center to the root apex. Statolith positioning was analysed on the two central cells of layer 4 and layer 5 corresponding to a total of 4 cells; Supplementary Fig. S6 ). The location of these four cells in the central columella make them good candidates for being statocytes  4, [38] [39] [40]. Images at high magnification (100×) were used to determine statolith positioning within the statocytes. Images were first contrasted and converted to binary images. Cell membrane and statoliths were then manually segmented to extract the coordinates of the centroids of these cellular components.