dataesr/scientific-paragraphs-categorization huggingface dataset:
ET was calculated according to  [33]. For soil data, combinations of soil types at the scale of 1:50,000  [34]  and physical parameters were joined to mapping units  [16, 35]. Further soil parameters were obtained as follows: 1) Topsoil organic carbon and pH were taken from the database FIS StoBo  [36] ; 2) Organic carbon and C:N-ratio of deeper soil layer was approximated using pedotransfer functions  [16, 35] ; 3) Top soil layer C:N-ratio was set to 10; 4) Volumetric gravel content and gravel content corrected bulk density were approximated following  [37] [38] ; 5) Soil surface albedo was estimated from soil organic carbon (Equation A in S1 File). Climate and soil data were spatially aggregated in order to obtain grids of 1, 10, 25, 50 and 100 km resolution. For this purpose climate data of spatial resolution of 1 km were spatially averaged.
Based on these findings (see result section), further analysis was refined for plant available water capacity awc, mean growing season temperature Tav and growing season climatic water balance cwb (Fig  2 ). Therefore, yields and aggregation effects were analysed separately for low and high values of the cwb and awc or Tav. High and low values were extracted by selecting values below the 25 th or above the 75 th percentile. The cwb was computed according to  [33]  from 1 km resolution climate data and validated by visual comparison with published maps  [74]  and comparison with regional mean time series of annual values (R 2 > 0. 99, Bias = -8. 4 mm) with  [75].
Acknowledgments This work was financially supported by the  German Federal Ministry of Food and Agriculture (BMEL)  through the  Federal Office for Agriculture and Food (BLE) , ( 2851ERA01J ). FT and RPR were supported by  FACCE MACSUR (3200009600)  through the  Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  ( 220-2007-1218 ) and by the strategic funding ' Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences  ( Swedish University of Agricultural Sciences ) and thank professor  P-E Jansson  ( Royal Institute of Technology, Stockholm ) for support. JC, HR and DW thank the  INRA ACCAF  metaprogramm for funding and  Eric Casellas  from  UR MIAT INRA  for support.
ACKNOWLEDGMENTS The authors would like to acknowledge  Claudine Boyer  and  Thomas Chesneau  ( CIRAD UMR PVBMT ) for providing Rssc strains and genomic DNA, and  Annett Milling  and  Caitilyn Allen  ( University of Wisconsin-Madison ) for providing Rssc genomic DNA. The authors would further like to acknowledge  Dr. Kenneth W. Berendzen  of the  ZMBP Central Facilities (University of Tübingen) Flow cytometry unit , for providing the plasmid pCF205 (35S::mCherryNLS) used for microscopy and further acknowledge  Catherina Brancato  for maintaining the A. thaliana root cell culture.
Data collection, selection, and Preparation Data on pig movements and premises characteristics were obtained from national databases, through Bulgarian Food Safety Agency (BFSA) in Bulgaria, the professional database of swine (La Base de Données Professionnelle Porcine -BDPORC) in France, the Istituto Zooprofilattico Sperimentale dell'Umbria e Marche (IZS-UM) in Italy, and the Ministry of Agriculture, Food and Environment (MAGRAMA) in Spain, under the appropriate confidential data transfer agreements. Registration of pig movements is mandatory in these countries since, at least, 2009. The year 2011, which was common in all databases, was retained for the analysis. Because of the dead-end characteristics of slaughterhouses, these premises were excluded from the analysis. The premises characteristics available were the type of production, the premise size, the type of housing system (except for Bulgaria), the geographical coordinates, as well as the pig company number (only for France).
In Bulgaria, pig farms were classified as East Balkan pigs (EBPs), SP (pigs kept for own consumption), Type B farms (TB = medium-size, low biosecurity level), Type A farms (TA = medium-size, high biosecurity level), or industrial farms (IND = large size, high biosecurity level)  (28, 29). For France, Italy, and Spain, pig premises were categorized into seven distinct types: multipliers (MU: premises that produce breeding stocks and semen), farrowing farms (FA), farrow-tofinishing farms (FF), finishing farms (FI), SP, trade operators (TR), and unknown premise type (UP). FA included farms which produce piglets until 3 or 25 kg. FI included farms which buy piglets (at 3 or 25 kg) and produce either 25 kg piglets or fattening pigs. For Italy, farrowers and farrow-to-finishers could not be distinguished in the database and were thus both typed as FA.
SP were defined as those who produce pigs for self-consumption in Spain, those who have no more than four fattening pigs and produce pigs for self-consumption in Italy (34), and farms with no more than four pigs in France. All farms that were not SP   1. Information on trade movements for all countries included the date of the movement, the unique identifier (ID) of the source and destination premises, and the number of pigs moved. For each country, directed and weighted yearly networks were built, the nodes being all pig premises of the study areas, even those that were not trading pigs during the study period. Movement data were aggregated over the study period and a direct link was drawn whenever a shipment of pigs occurred between the corresponding premises. Two weights w ij A and w ij B were attributed to the link according to the number of pig batches and the number of pigs moved from premise i to premise j during the study period, respectively.
In several networks, the degree distribution displays a power-law tail P(k) ~ k -γ , where the exponent γ is a constant. The tail of this distribution reflects the presence of hubs, which are nodes that have much higher contacts than the majority of the other nodes  (38)  Density (D) Proportion of links that are present in the network compared to all possible links, calculated by the equation: All analyses were conducted in R (44) using the "igraph" package for network analysis  (45). D = 2L/N(N -1).
e. , Corsica and Sardinia Island). It is important to note that although authorities are usually not very open to share animal movement records with this level of detail (i. e. , at farm level and without some temporal or spatial aggregation by month/year or county/region) due to confidentiality issues, there is an extraordinary value of accessing and analyzing this information to unravel the complexity of the trade network structure and characteristics and better inform policies. In fact, thanks to the high quality of movement data and the availability of full datasets from four European countries during the same period, several network measures and proxies of pig trade patterns could be computed and compared in detail. Results highlighted that some proxies can be used whatever the systems considered, whereas other are specific to a country or even a production system (Tables  1 2 3 4 5 ).
acKnOWleDgMenTs Authors acknowledge the provision of data of the professional database of swine (La Base de Données Professionnelle Porcine -BDPORC), the Ministry of Agriculture, Food and Environment (MAGRAMA), the Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche (IZS-UM), and Bulgarian Food Safety Agency (BFSA) as well as Jean-Charles Sicard for his support in data cleaning and validation.
A simulation model's ability to capture historical grain yield variability has shown it can serve as a sensible basis on which to demonstrate the utility of crop models among stakeholders and decision-makers (e. g. ,  Dobermann et al. , 2000). Considering the effort required in collecting data and calibrating a crop model for a particular application, previous studies have often relied upon only a single crop model and limited sets of observational data. This approach overlooks differences in plausible calibration methodologies as well as biases introduced in the selection of a single crop model and its parameterization sets; all of which may affect climate sensitivities  (Pirttioja et al. , 2015). The final decision-supporting information may therefore be biased depending on the amount of calibration data available and the crop model selected for simulations.
Here we present an agro-climatic analysis of 27 wheat models that participated in the AgMIP Wheat Model Intercomparison Pilot (described briefly in the next section and more completely in the text and supporting materials of  Asseng et al. , 2013; and Martre et al. , 2015) , with a focus on how interannual climate variability affects yield simulations and uncertainties across models. This is just one of several studies to emerge from the unprecedented Wheat Pilot multi-model intercomparison and it is intended to contribute to the overall effort by highlighting important areas for continuing analysis, model improvement, and data collection. As most climate impacts assessments cannot afford to run all 27 wheat models, for the first time we examine the consistency of agro-climatic responses across locations, models, and the extent of calibration information to determine whether a simpler, smaller multi-model assessment may be a suitable representation of the full AgMIP Wheat Pilot ensemble.
These high-quality seasonal data unfortunately do not correspond to coincident long-term variety trials using the same management, cultivars, and soils that would be ideal to calibrate interannual variability (corresponding crop growth observations and long-term variety trials are quite rare, particularly in developing countries). Even where long-term variety trial data exist (and are publically available), considerable analysis is needed to attempt a direct comparison with multi-season crop model simulation given shifts in cultivars every 3-5 years  (Piper et al. , 1998; Dobermann et al. , 2000; Mavromatis et al. , 2001; Singh et al. , 2014; Boote et al. , 2015). As a result, analysis here follows many crop modeling studies in utilizing a single-year or short-period (~5 years or less) field dataset for calibration and then relying on soil properties, plant genetics, and established model biophysics to determine interannual variability rather than specifically calibrating internal parameters of response.
Palosuo et al. (2011)  examined the potential of a smaller multi-model ensemble to reproduce interannual yield variability of variety trial for wheat having only two sites with a longer yield series (14+ seasons) but limited data for calibration, finding errors in each model but much improved statistics for the multi-model ensemble mean. Rötter et al. (2012)  came up with similar results for barley model simulations. Daily climate data (maximum and minimum temperatures, solar radiation, precipitation, wind speed, vapor pressure, dew-point temperature, and relative humidity) were compiled from local observations with missing data filled using the NASA Modern Era Retrospective-analysis for Research and Applications (MERRA;  Rienecker et al. , 2011)  and the NASA/GEWEX Solar Radiation Budget  (Stackhouse et al.
Types of simulation exercises Wheat Pilot protocols were designed to investigate whether limitations in data (which hamper the calibration of crop models in many locations) substantially affect the accuracy of yield simulation and/or alter the simulated sensitivity to climate variability and climate changes. Participants were therefore instructed to perform simulations in two steps: 1) Low-information simulations: Weather data, planting, crop emergence, flowering, and physiological maturity dates, field management information, and soil characteristics and initial conditions were provided but no information was provided on end-of-season yields or in-season crop growth and soil water and nitrogen (N) dynamics. This subset of field experiment data was referred to as "blind test" simulations by  Asseng et al.
Effects of calibration strategy on simulations of climate change impact were also examined by  Challinor et al. (2014b)  and for simulations of crops across Europe  (Angulo et al. , 2013). The relative lack of different sensitivities between the Low-and High-information simulations could also be explained by the fact that each was simulated by the same model experts for a given model, and that additional data provided for the High-information simulations were mostly limited to details on the crop itself. Additional information about the soil environment, in particular, would have potentially altered the sensitivity to interannual rainfall anomalies. A comparison between the LL and HH correlations indicates that most models have the same relationship with the full ensemble regardless of the level of calibration information.
Where LH and HH correlations are similar for a given model there is little benefit from additional calibration in terms of interannual climate response, as the Low-information results perform just as well as the High-information results against the High-information ensemble standard. HH correlations are at least higher than LL correlations in the majority of cases, suggesting that additional calibration information does tighten the spread of models around the ensemble mean and thus improve the performance of several models. This benefit is blurred by the likelihood that the fully-calibrated set of models would be expected to have closer agreement among members; however, it is important to note that calibration data at each site were only provided for a single year, making it impossible to directly calibrate the interannual variability examined here.
This is a typical limitation for crop model simulations, as there are few long-term field trials that would allow full calibration of interannual variability. Also calibration in many cases focuses on minimizing error between modelled and observed results for the calibration dataset, which may have little influence on model responses to variation in environmental conditions that may be controlled by model structure and parameters other than those in focus for the calibration. The remainder of this study will focus on the High-information simulation sets, as these are likely to be of highest fidelity. Agro-climatological mechanisms at the root of these correlations are explored in Section 3. 4 below. sites, the benefit of adding a second model to a climate variability analysis is therefore an increase of +0.
Such long-term agricultural research datasets are rare, unfortunately, so in typical applications such as those done here it is likely that any biases in calibration are amplified when a single-year's calibration is used for multiple seasons. It is therefore useful to take advantage of the tendency of multi-model ensemble statistics to reduce overall errors beyond the calibration period. The AgMIP Wheat Pilot offers a far larger multi-model sample than would be expected in the applications for which each of the participating models was designed; however several of the interannual response results help guide the formation of practical subsets and application protocols. Although calibration information has been shown to reduce errors in mean yields and details in crop growth  (Asseng et al. , 2013) , the results presented here suggest that interannual yield variability for most models is not strongly affected by the availability of more detailed field observations (e.
The analysis presented here focuses on mean growing season climate anomalies at four locations; however consideration of intra-seasonal variability and extremes (e. g. , heat waves, dry spells, frosts, floods, waterlogging, monsoon dynamics) require further study. Comparing multi-model simulation experiments against long-term field trials (e. g. ,  Dobermann et al. , 2000)  would also be desirable in order to provide true observations upon which to evaluate simulated outputs (rather than assuming the value of the ensemble average as done here). The effects of interannual temperature variability and mean climate warming were shown to be only weakly related among the 27 wheat models, indicating that a temporal proxy for climate change is likely oversimplified. State-of-the-art empirical models use far more than interannual temperature for climate impacts projection, however these findings underscore the importance of considering complex interactions between variables and non-linear responses that may not be present in the historical period datasets to which models are fit.
Acknowledgements We acknowledge the efforts of  AgMIP Leaders Cynthia Rosenzweig ,  Jim Jones ,  John Antle , and  Jerry Hatfield  in their efforts to initiate the AgMIP Wheat Pilot and encourage explorations such as these. Support for many European participants in the AgMIP Wheat Pilot was provided by the  Modelling European Agriculture with Climate Change for Food Security (MACSUR)  knowledge hub within the  Joint Research Programming Initiative on Agriculture ,  Food Security and Climate Change (FACCE-JPI ). Alex Ruane's contributions were supported by the  NASA   Earth Sciences Research Program  (# 281945. 02. 03. 03. 96 ) and the  NASA Modeling, Analysis , and Prediction Program (# 509496. 02. 08. 04. 24 ). Participation of  Claudio Stöckle  and CropSyst simulations were supported by the project  Regional Approaches to Climate Change for Pacific Northwest Agriculture (REACCH-PNA ), funded through award # 2011-68002-30191  from the  US National Institute for Food and Agriculture.
Microbiota composition: diversity index The qPCR analysis allows estimating absolute bacterial counts in the stool for a defined bacterium but does not give an untargeted view on the faecal microbiota. Therefore, the microbiota analysis was complemented by 16S rRNA gene pyrosequencing. After analysis and exclusion of subjects with incomplete data sets, we obtained faecal microbiota compositions for all three time points from at least 13 infants per group. Figure  3  compares the α-diversity for the three groups at baseline and after 6 and 12 weeks of feeding. At baseline the three feeding groups did not differ significantly in microbial diversity (Shannon index). At 6 weeks of age, infants in the C group showed a higher diversity index, due to an increase in both richness and evenness when compared with infants in the T group and the B group.
Trial design and population This study was registered at ClinicalTrials. gov under the number NCT01983072 where eligibility criteria and locations, among other information, are referred. The two study formulae contained proteins, carbohydrates, fats, vitamins and minerals in amounts intended for full nutritional support of infants from birth to 4 months of age. Both were whey-predominant (whey/casein ratio of 70/30) low-protein (1. 8 g/100 kcal) formulas. The study formulas were manufactured at the Nestlé Product Technology Center (Konolfingen, Switzerland) and were packaged in identical cans. Neither parents/caregivers nor investiga-tors and clinical project managers knew the identity of the formulas. Infants were exclusively fed their assigned formula or were fully breastfed ad libitum from enrolment until 3 months of age.
Pyrosequencing and analysis of 16S genes The 16S variable regions V1-V3 was PCR amplified and sequenced on Roche 454 GS-FLX-Titanium Sequencer as previously described  (Sanchez et al. , 2014). Raw sequence data were deposited in the GenBank Short Read Archive (Accession number: SRP064888) and analysed using MOTHUR V. 1. 33. 0  (Schloss et al. , 2009)  and QIIME V. 1. 8 (Caporaso et al. , 2010b)  software packages. Pyrosequencing reads were denoised with the MOTHUR implementation of PyroNoise  (Quince et al. , 2009)  according to the 454 SOP described in  Schloss and Westcott (2011). Chimeras were identified using usearch61 in QIIME  (Edgar et al. , 2011). The sequences were then trimmed as described in the MOTHUR 454 SOP to keep sequences overlapping the same 16S region. Subsequent analytical steps were performed in QIIME.
Operational taxonomic units (OTUs) de novo picking at 97% identity was performed using UCLUST  (Edgar, 2010). Taxonomy assignment of OTU representative sequences used the RDP Classifier with confidence threshold of 0. 6  (Wang et al. , 2007)  on the Greengenes reference database v. 13. 8 (McDonald et al. , 2012). The same sequences were aligned using PYNAST  (Caporaso et al. , 2010a)  on the Greengenes core reference alignment  (DeSantis et al. , 2006). The resulting multiple alignments was then filtered and used to build a phylogenetic tree with FASTTREE  (Price et al. , 2010). After quality filtering  (Bokulich et al. , 2013) , phylogenetic distances between all samples were computed as UniFrac distances  (Lozupone and Knight, 2005). Diversity analyses based on the Unifrac distances were performed in QIIME, whereas the analyses based on taxonomy assignments used the websites Calypso at http:// bioinfo.
Acknowledgements We would like to thank  N. de Groot ,  J. Tanguy ,  L. Krause ,  C. Ngom-Bru  and  T. Neville  for their contribution to this work. This study was funded by  Nestlé Nutrition. Bernard Berger ,  Sophie Pecquet ,  Enea Rezzonico ,  Dominik Grathwohl ,  Norbert Sprenger  and  Harald Brüssow  are employees of  Nestec, Ltd.
Selection of candidate genes Sequences of cytochrome P450 genes in the Vitis vinifera L. PN40024 genome were retrieved from the Grape genome database (http://genomes. cribi. unipd. it/grape/) and their translated sequences were aligned to the P450 sequences from the heterozygous Pinot noir genome from the cytochrome P450 website (http://drnelson. uthsc. edu/CytochromeP450. html). In cases of discrepancy, the genes were manually re-annotated. RNA sequencing data from Riesling and Gewurztraminer grape berries from two time points (green and mid-ripe) were aligned to these gene models. The RNA sequencing experiment will be reported elsewhere. For further investigations, we chose those genes with increased expression at the later time point (mid-ripe): CYP71AT7 (gi|731429818|ref|XM_002278264 Total RNA was isolated from 1 g of berry powder according to the described procedure  (Reid et al.
, 2006)  with the following modifications: 7 ml of extraction buffer was used per gram of berry powder and after LiCl precipitation the samples were treated as described above. cDNA was synthesized from 1 lg total RNA using (dT) 23 and Superscript ® III Reverse Transcriptase (Life Technologies, Carlsbad, CA, USA) following the manufacturer's instructions. The resulting cDNA was diluted 59 and used as a template in quantitative PCR (qPCR) experiments. Primers for qPCR were designed using the Primer3 plus website and are listed in Table  S2. Specificity of the primers was confirmed in silico using the Primer BLAST website (NCBI) and by measuring the melting curve of the products. The reaction mixture contained 19 SYBR ® Green Master Mix, 250 nM of forward and reverse primers and 509 diluted cDNA from reverse transcription reaction.
The PN40024 CYP76F14 coding sequence was synthesized de novo by Genecust Europe (Dudelange, Luxembourg) with codon optimization for expression in yeast. CYP76F12, CYP76F14 and CYP76T21 were cloned into the pCAMBIA2300 vector with the USER TM cloning method. All cloning primers sequences are listed in Table  S3. Sequences described in this work have been deposited in GenBank under accession numbers KU955332 to KU955337. cDNA sequences of putative LIS expressed in Gewurztraminer berries were reconstituted based on RNAseq data and on homology with LIS identified in the grapevine reference genome  (Martin et al. , 2010). A full-length cDNA corresponding to a putative TPS56 (HM807392 PN55M1) was then amplified from RNAs prepared from mid-ripe Gewurztraminer berries and cloned into vector pMDC32.