dataesr/scientific-paragraphs-categorization huggingface dataset:
We used Akaike information criteria (AIC) and likelihood ratio tests (LRTs) to select the best random effect structure of the model for each ant species  (Bolker et al. , 2009). After using the GLMMs to determine the best random structure for each species, we selected the best model by removing nonsignificant fixed-effect parameters in a backwards-stepwise process using LRTs. The selection procedure was continued until a model was found in which all effects were significant  (Zuur et al. , 2009). All LMs and GLMMs were estimated using the "glmer" function in the "lme4" package  (Bates, Maechler, & Bolker, 2012) , in which the maximum likelihood of parameters is approximated by the Laplace method  (Bolker et al. , 2009). All statistical analyses were performed with R 2. 15. 0 (R Development Core Team, 2014) and with an alpha level of 0.
Acknowledgements We apologize to colleagues whose work was not discussed or cited due to space constraints. SCJ is supported by the Fundação Portuguesa para a  Ciência e Tecnologia Fellowship  ( SFRH/BPD/87479/2012 ). MBD laboratory is supported by an  EMBO  installation grant, an  ERC   Starting Grant  ( PFE-GIUE-ERC-2010-StG-261344 ) and  Instituto Gulbenkian de Ciência, Portugal. BD laboratory  is funded by  Fondation pour la recherche Médicale  ( FRM DEQ 20131029168 ) Grant.
Statistical analysis and Xoo resistance QTLs mapping A linkage map comprising 226 SSR markers and constructed from the RIL population was used for mapping resistance QTL to Xoo. An analysis of variance, using marker genotypes as the groups, was carried out using MapDisto  (Lorieux 2007). Data files were prepared using the Export map and data function of MapDisto. Analyses of distribution of the phenotypic traits as well as QTL detection were mainly performed using the Qgene v. 4. 3. 0 program  (Nelson 2005 , http://www. qgene. org) and Windows QTL cartographer 2. 5  (Wang et al. 2006b ). Different methods were compared such as Single-marker regression (SMR), Simple interval mapping (SIM), and Composite interval mapping (CIM). The Forward cofactor selection option was used in CIM. The LOD score statistic was used for all methods in order to make the results comparable.
Acknowledgments We thank  USAID  which partly supported this work through the project " Marker-Assisted Selection for improvement of rice varieties resistance to RYMV for West Africa " conducted by the second author (AfricaRice) and the  Generation Challenge Program  (subprogram  SP5 ), which partly funded this study. We are grateful to  Africa Rice  and  IRD  for their support and  Thierry Mathieu  ( IRD ) for his help during greenhouse experiments and screening. We thank also  Daniel Fonséca  and  Nour Ahmadi  for providing information about the SSR markers used in this study.
Flow cytometry and fluorescence microscopy Flow cytometry-based analysis involved the use of a FACS Calibur cytometer (Becton Dickinson) and Flowjo software (Treestar). For wide-field microscopy, nuclei were stained with Hoechst 33258 (1 µg. mL -1 , Life Technologies) and cells imaged with an Olympus IX81 microscope. Confocal microscopy in fixed samples was performed with either a Zeiss LSM700 microscope or a Leica TCS SP2 microscope whilst live cells were imaged with a Nikon TiE Eclipse microscope equipped with a PerkinElmer UltraVIEW VoX 3D module. The day after, infected cells were immuno-stained for the intracellular UUKV nucleoprotein N using the anti-N primary mAb 8B11A3 and an AF568-coupled anti-mouse secondary mAb (red). Nuclei were stained with Hoechst (blue) and samples analyzed by wide-field microscopy.
Acknowledgments This work was supported by grants from  CellNetworks Research Group funds , the  Natural Sciences and Engineering Research Council of Canada  # 419538-2012 , and the  Banting Research Foundation  to P. Y. L. We thank  Amanda Chase  for proof reading this paper. The excellent technical assistance of  Jessy Tremblay ,  Vibor Laketa , and  Niko Tsopoulidis  in our microscopy-based approaches was highly appreciated. The  ZMBH Flow Cytometry and FACS Core Facility of the University of Heidelberg  as well as  Jacques Thibodeau  and  Serge Senechal  are gratefully acknowledged for their precious help in the sorting of higher expressor cells.
Riboswitches Riboswitches consist in short RNA sequences that change conformation upon binding of a dedicated ligand, and adjust the expression of certain operons according to the availability of specific metabolites. Typical riboswitches are parts of the 5 0 -UTRs of genes, where they can for instance act as transcription terminators or anti-terminators, or control the access of the small ribosomal subunit to the SD sequence. 73 The structure of these motifs is highly conserved across species, allowing their rather accurate prediction throughout genomes and their annotation in databases like Rfam (http://rfam. xfam. org). 81 he genome of L. monocytogenes encompasses at least 55 validated or candidate riboswitches, responding to 13 distinct ligands. 12 The identification of the 'on' and 'off' states of 40 of these riboswitches by transcriptome analysis using tiling arrays has pinpointed that functional riboswitches can also be located in 3 0 -UTRs.
Funding Work in the laboratory of P. C. received financial support from  European Research Council  advanced grant  BacCellEpi-670823 ,  ERA-NET Infect-ERA ProAntiLis, Investissements d'Avenir BACNET  ( ANR-10-BINF-0201 ) and  IBEID  ( ANR-10-LABX-62-01 ), and  Fondation le Roch Les Mousquetaires. The laboratory of A. L. is supported by  Fondation pour la Recherche M edicale  ( FRM-AJE20131128944 ),  Inserm ATIP-Avenir, Mairie de Paris (Program Emergences -Recherche m edicale) , and Investissements d' Avenir MemoLife  ( ANR-10-LABX-54 ). P. C. is a  Senior International Research Scholar  of the  Howard Hughes Medical Institute.
Experimental site 1 Phytoseiidae on citrus trees. Three Phytoseiidae species were found on citrus trees: Euseius stipulatus (Athias-Henriot), Neoseiulus californicus (McGregor) and Typhlodromus (Anthoseius) rhenanoides Athias-Henriot (Table  1 ); the most abundant was E. stipulatus whatever the weeding management. Considering the overall dataset, mean densities were significantly lower in the Mod. 2 (herbicide treatment) than in the three others (H (3, 4800) = 15. 30, P = 0. 001) (Figure  3a ). These differences were observed on 09-VI (H (3, 4800) = 23. 12, P = 0. 00) and 23-VI (H (3, 4800) = 11. 27, P = 0. 01). After this latter date, Phytoseiidae densities decreased in all the modalities, and no more significant difference was observed. Phytoseiidae on weeds. Eight Phytoseiidae species were collected on weeds; two are new for the Tunisian fauna  (Kreiter et al.
ACKNOWLEDGEMENTS We thank  Nesrine Tersim  from the  INAT  for her support during the field experiment. We thank also  Sabine Guichou  and  Martial Douin  from  UMR CBGP  for their help during mountings of Phytoseiidae. This study was realised during a PhD project of the senior author with a PhD grant Averroès financed by the  European Community  and a co-registration in the two institutions ( INA Tunis  and  Montpellier SupAgro ) and the co-supervision by  M. -S. Tixier ,  K. Lebdi Grissa  and  S. Kreiter. Finally we are grateful to the two anonymous reviewers whose comments have allowed manuscript improvement.
Data analysis For the statistical analysis of the abundance of captures by the two sampling methods we performed a simple Analysis of Variance (ANOVA) of all the data in all habitats (8 species and 29177 data). For the comparative between D biodiversity indices and between species richness we calculated the montly diversity index and the number of species by summing up the total individuals captured monthly in each agroecosystem followed by ANOVA Multiple Range Test. The comparative between abundance in organic and sprayed agroecosystems was done by simple ANOVA of all data. When we compared fruit trees, ground cover and shelterbelts we performed multifactor ANOVA considering year and habitat as factors for each species. In order to evaluate the number of species and diversity indices, data were grouped monthly as number of insects or number of species/month for each year and sampling methodology.
Seasonal population dynamics We analyzed seasonal dynamics of the most abundant Neuroptera species from data collected with the aspirator device within the organic agroecosystems. Yellow sticky traps were not considered for this purpose since they remain in the field one month during the cold period. The analysis of the three habitats evaluated allowed separating insect species in several groups (Fig 4  ):  -Species that showed seasonal migration from fruit trees to shelterbelts to overwinter: Ch. septempunctata, M. angulatus and W. subnebulosus. Adult populations of these species associated to citrus trees start to decline at the end of summer and beginning of autumn (September-October) at the same time that populations associated with shelterbelts increased. For these species captures of adults during the winter period (equivalent in the case of Valencia to the "non-growing season" from January toMarch) were very scarce or null which mean these species spent winter as egg or larval forms.
Acknowledgements We thank two anonymous reviewers and the editors for constructive comments on the manuscript. The authors are very grateful to  Rosa Guaita ,  Pili Mañó ,  Anna Marco ,  Altea Calabuig ,  Bernadette Csőke  and  Adela Cuesta  for their help and collaboration with laboratory and field work and to citrus orchards owners who permitted access for this study. Joan van Baaren  and  Ferran Garcia-Marí  provided valuable comments that improved the manuscript. We thank  Lucy Alford  and  Adrien Bonvin  for language corrections.
Therefore, the sequencing was considered completed. BUSCO was also used to calculate a training set for the gene predictor Augustus  (11) , and this method predicted 11,695 genes, whose translated sequences were submitted to a BLASTp search against the nr database (NCBI) or CAZydb  (12). The Carbohydrate-Active enZYmes (CAZy) Database search revealed 236 glycoside hydrolases defined as enzymes for hydrolysis and/or rearrangement of glycosidic bonds, 258 glycosyltransferases (formation of glycosidic bonds), 24 polysaccharide lyases (nonhydrolytic cleavage of glycosidic bonds), 111 carbohydrate esterases (hydrolysis of carbohydrate esters), 95 redox enzymes that act in conjunction with CAZymes, and 85 enzymes with carbohydratebinding modules that act in adhesion to carbohydrates. Among all predicted protein-coding genes, genes related to the elicitin-like proteins oligandrin and the cell wall proteins were found in the genome of strain Po37, explaining its ability to act as an inducer of plant systemic resistance.
Model Animals to Deepen Understanding of Beef Quality. Relevant information in beef is often lacking due to the incompleteness of annotation of the genome. Alternative strategies are to mine genome-wide sets of data from international databases (in silico approach) thanks to online and interactive workflows and databases  [42, 43]  or to use model species (in vivo and in vitro approaches). This is useful to reveal gene networks involved in the construction of the quality phenotype, for example, regarding the development of muscle and adipose tissues that determine the lean to fat ratio  [44]. The knowledge gained from the studies in nonruminant and ruminant species can foster our understanding of biological mechanisms. For example, recent studies including ours have identified the heat shock protein Hsp27 as a beef tenderness biomarker  [9, 14, 16, 19, 45]  with differential expression in the muscle of animals giving high versus low meat tenderness.
Measurements Sap flow data were collected between 30 and 31 June for model evaluation and between 06 and 07 July 2014 for model validation on a fully expanded leaf of a well-watered cucumber plant. The leaf was located at the upper canopy, positioned toward southeast and not shaded by other leaves. A heat field deformation sensor (HFD) was installed on the petiole of the leaf to monitor the relative water flow through the petiole  (Hanssens et al. , 2013 (Hanssens et al. , , 2014) ).
Parameter Fitting The stomata model was parameterized using measurements with the Li6400-XT portable photosynthesis system (Li-Cor, Lincoln, USA). The leaf was left in darkness for about 30 min, until stomatal conductance was close to zero. Then, PAR was set to 1300 µmol photon m -2 s -1 and values of stomatal conductance were logged every 5 s until reaching stability. With these data, the time constant α g and the initial lag r 0 were estimated using Equation (  15 ). Using data of 30-31 July 2014, the limit light intensity for conductivity PPFD lim = 1000 µmol photon m -2 s -1 and the reference mesophyll water potential ψ m,r = -0. 9 MPa were estimated to agree with the observations. The ideal light and temperature model parameters representing the average and the amplitude of light and temperature per oscillation period were fitted using recorded light and temperature data in Excel.
However, the range of stomatal conductance from their measured data is 0. 05-0. 13 mol m -2 s -1 , a range where g sw limitation would be strongest. In contrast, our g sw data ranged from 0. 15-0. 30 mol m -2 s -1 , FIGURE 5 | Effect of varying time constant for stomata response (α g ) on net assimilation rate (B), stomatal conductance (C), transpiration rate (D), water use efficiency (E) for different photosynthetic active radiation (PAR; A) during the sunny day. Simulations were performed in steps of 1 s over 2 h (from 10:00 a. m. to 12:00 p. m. ) and are extracted from measurements over 2 days data. Solid lines represent the fastest stomata opening/closing (α g = 4. 052 • 10 -3 ) and dotted lines represent the simulation result for α g = 1. 01 • 10 -2. Highlighted areas correspond to time courses where slower reacting stomata have a better water use efficiency.
Other climatic data were extracted from the 2 days measurement data presented in Figure  1 , and all other parameter values are defined in Tables  1, 2. i. e. , g sw limitation was much less than in the dataset of  Lawson and Blatt (2014). The model should be calibrated to plants with lower maximum g sw , to quantify the combined effect of α g and g 1 on WUE. Effect of g 1  Medlyn et al. (2011)  demonstrated mathematically that the biological interpretation of g 1 is WUE. This interpretation has been further proved by a global dataset showing that g 1 reduces with available water in the soil  (Lin et al. , 2015). Therefore, it is not surprising that increase of g 1 decreased WUE (Figure  7A ). It is interesting to identify the traits determining g 1. According to Equation (  16 ), g 0 and g 1 are the physiological parameters which could increase g sw.
ACKNOWLEDGMENTS The publication of this article was funded by the  Open Access Fund of the Leibniz Universität Hannover. TC thanks  Prof. Kathy Steppe  and  Dr. Jochen Hanssens  at  Ghent University, Belgium , for their instructions in using HFD sensors and  Landgard Stiftung  and  TASPO Stiftung  for their financial support of his research stay at  Ghent University.
qRT-PCR analyses Total RNA was extracted from sub-samples of frozen root powder (100 mg) using the Nucleomag 96 RNA kit (Macherey-Nagel, Düren, Germany) on the Biosprint 96 (Qiagen, Hilden, Germany). Total RNA was subsequently purified with the NucleoSpin 96 RNA kit (Macherey-Nagel). This purification step allowed a complete elimination of both protein residues and residual genomic DNA contamination, with another DNase digestion step. Reverse transcription was carried out on 1 μg of RNA with the iScript Select cDNA synthesis kit (Bio-Rad, Richmond, CA, USA). Steps of extraction, purification and reverse transcription were carried out according to the manufacturer's instructions. The primer pairs used to quantify expression levels of genes coding for two root nitrate transporters TaNRT1 (GenBank AY587264) and TaNRT2.
1 (GenBank AF332214. 1), the nitrate reductase TaNR (whose partial sequence comes from Boisson et al. [48] ) and the glutamine synthetase 2 TaGS2 (GenBank DQ124212. 1) have been described in Taulemesse et al. [27]. The specificities and the efficiencies of the primer pairs had been validated previously. Quantitative real-time experiments were carried on the Light Cycler 380 (Roche, Indianapolis, IN, USA) with the LightCycler 480 SYBR Green 1 Master Kit. Reactions were made with amounts of 12. 5 ng cDNA for TaNR and TaGS2, or 31. 25 ng cDNA for TaNRT1 and TaNRT2. 1 because of lower expression levels observed for the two latter genes. PCR reactions were cycled for 10 min at 95°C followed by 45 cycles of 95°C for 10 s, 60°C for 15 s and 72°C for 15s. A melting curve was analysed at the end of each assay to ensure that single products were amplified.
This could be the reason for the stronger effect of total PANU on GPC observed under field conditions than in the present study. Taken together, these results plead for strong effect of early PANU on GPC, accounting both for existing genetic and environmental variabilities. Datasets that allow us to temporally dissect the relationship established over the entire postflowering period are scarce. Under non-limiting conditions for N, in hydroponic culture, Taulemesse et al. [27]  highlighted that PANU exhibits a marked dynamic during the post-flowering period. Further, these authors showed that PANU occurring during the early postflowering phase (grain cell division) was strongly impacted by plant N status at flowering, whereas that occurring during the later post-flowering phase (active phase of grain N filling) was hypothesised to be mainly controlled by N demand exerted by grain growth.
Acknowledgments The authors thank  Joelle Messaoud ,  Antoine Bordes  and  Julien Mazuel  ( INRA Clermont-Ferrand ) for their help with experiments. The authors are also grateful to  Patricia Ballias  and  Duyen Prodhomme  ( INRA Villenave d'Ornon ) for their help in nitrate assays. Sandy Lang  (rescript. co. nz) proofread and provided language assistance.
Funding This work was funded by a fellowship from  AgreenSkills  ( CESETAB  project) and  Institut National de la Recherche Agronomique  (to C. F. ); a 'Chaire d'excellence' from l' Agence Nationale de la Recherche, France  [ ANR-10-CHEX-0011 : mećanograine to G. I. ]; and a  European Research Council   Starting Grant  [Phymorph # 307387  to A. B. ].
g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance 50 in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets 55 taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking.
EPIC-BOKU EPIC-BOKU was initially developed to provide yield estimates at contrasting management intensities for land use change and agro-economic models  (Havlík et al. 2011 , Schneider et al. 2011; Nelson et al. , 2014; Frank et 160 al. , 2015)  at the European and global scales  (Stolbovoy et al. 2007; Skalský et al. , 2008; Elshout et al. , 2015). The spatial structure of its input data is based on a regular 5 arcmin grid, which is first aggregated to homogenous response units (HRUs) based on a classification of physical characteristics (elevation, slope, soil). The HRUs are subsequently intersected with administrative units (national borders at the global scale) that determine specific crop management parameters, which are derived from databases or socio-economic data. The 165 field-scale model is run for each of the resulting simulation units (SimU).
, 2012). 190 EPIC-TAMU has primarily been used for field-scale and regional-scale simulations  (Gelfand et al. , 2013; Zhang et al. , 2015). It has been adapted with minimal changes for use as part of the AgMIP GGCMI project, and has Biogeosciences Discuss. , doi:10. 5194/bg-2016-527, 2016 Manuscript under review for journal Biogeosciences Published: 20 December 2016 c Author(s) 2016. CC-BY 3. 0 License. otherwise not been previously used for global simulations. As a result, no default simulations (see Section 2. 4) were produced. To keep the number of EPIC-MFWs in evaluations across management scenarios constant, the fully harmonized setup was also used as default. 195.
GEPIC The GEPIC MFW was originally developed for studies of global crop-water relations  (Liu et al. , 2007). In its present version, it uses input data for planting dates, growing season length, P fertilizer application rates, and cultivar distributions besides the original input data elevation, slope, country/region, N fertilizer application rates, and irrigation water management  (Folberth et al. , 2012). Default fertilizer inputs are mainly based on the 200 FertiStat database  (FAO, 2007)  and have been extrapolated for maize based on the human development index (HDI) for countries lacking data. More recently, the MFW has been setup for applications in sub-Saharan Africa based on a regional calibration  (Gaiser et al. , 2010)  and thoroughly evaluated in various studies (e. g. Folberth et al.
PEPIC PEPIC is a global EPIC-MFW developed at the Swiss Federal Institute of Aquatic Science and Technology (Eawag) initially based on GEPIC, which had been developed at the same institute. Hence, the two MFWs have similar features in software design and default input data. However, one of the main purposes of PEPIC was to 215 develop a fully free tool, which can be used without any software license. Therefore, unlike GEPIC, PEPIC was compiled by a free computer language, Python. In addition, the parameterization and setup has been adjusted in large parts (Table  1 ) to match focus research purposes. It was initially developed to investigate the impacts of different PET methods on crop-water relations  (Liu et al. , 2016a). Presently, applications of PEPIC focus on assessing trade-offs between crop yields and nutrient losses, e.
Common input data Climate forcing data based on the WFDEI GPCC dataset  (Weedon et al. , 2014)   solar radiation from ERA-interim  (Dee et al. , 2011)  and precipitation from GPCC  (Schneider et al. , 2013). All 230 EPIC-based MFWs used soil data from the ISRIC-WISE database  (Batjes, 2006)  mapped to the Digital Soil Map of the World  (FAO, 1995). For EPIC-BOKU and EPIC-IIASA, the originally approx. 5000 soil profiles had been reduced to 120 based on a classification of key variables  (Skalský et al. , 2008)  and soil hydraulic parameters not provided in the WISE databases were complemented according to  Schaap and Bouten (1996)  and  Wösten and Van Genuchten (1988). 235 For the harmonized runs, nutrient application rates for N and P were based on crop-specific data from  Mueller et al. (2012)  to which manure application rates had been added proportionally.
Separate planting dates and growing season lengths for rainfed and irrigated management were based on  Sacks et al. (2010) , complemented by gap filling with data from the MIRCA2000 dataset  (Portmann et al. , 2010)  and the LPJmL model  (Waha et al. , 2012). Both datasets were provided by the GGCMI project  (Elliott et al. , 2015). Default runs (see Sect. 2. 4; 240 Table  2 ) were carried out using individual fertilizer and growing season databases within each MFW.