dataesr/scientific-paragraphs-categorization huggingface dataset:
For image acquisition, an average of 15,000 frames was recorded at a rate of 10 ms per frame. To induce photoswitching, samples were imaged in a freshly prepared Smart Kit buffer (Abbelight). Continuous excitation and activation was used with output laser powers of 600 mW at 642 nm (for AF647 excitation) and 0 to 2. 5 mW at 405 nm (for activation). The intensity of activation was progressively increased through-out the acquisition to ensure a constant amount of simultaneously emitting fluorophores within the labeled structures. These excitation powers were optimized to ensure single-molecule detection. Imaging data are available upon request. Image analysis 3D image analysis was performed using Imaris software and its XT module. For all images analyzed in 3D, a background subtraction filter was applied.
6-pixel initial sigma). Particle coordinates and statistical properties were exported, and further analysis was conducted using MATLAB (MathWorks). Clustering of localizations was performed using an algorithm that was previously described by Cattoni et al. (41). A particle density and area threshold was set to define and quantify the number of objects per imaged probe. Automatic detection and quantification were further verified by visual inspection and comparison of conventional fluorescence and super-resolution rendered images using variable intensity thresholds.
Determination of cell cycle stage We used the method published by Roukos et al. (29)  to determine cell cycle stages of S2R+ cells. This method is based on DAPI fluorescence intensity after microscopy image acquisition (fig. S5 ). Briefly, DAPI channels of images were separated and projected along the z axis using average intensity, and nuclei were identified with CellProfiler software (http://cellprofiler. org/). The DAPI-integrated intensity was calculated for each nucleus, and the distribution for the whole population was plotted. The nuclei were then classified as G 1 or G 2 according to their DAPI-integrated intensity profile relative to the profile obtained for the population (fig. S5 ). To classify nuclei positive for CycB (fig. S5C ), the CycB signal was segmented using CellProfiler software, and overlapped nuclei were counted.
5061200 alongside with ChIP-seq of H3K4me3 (modENCODE5096). The embryo [16 to 18 hours, merge of two replicates published by Schuettengruber et al. (42) ] Hi-C map (Fig. 5A ) is plotted for the region 3R:500000. 14000000 and aligned with ChIP-seq of Pc (modENCODE3957) and H3K4me3 (modENCODE5096). The side-by-side Hi-C comparison for the 3R:500000. 14000000 region (Fig. 5G ) shows the contact map in WT male embryos (merge of two replicates) on the top left and ph 505 male embryos (merge of two replicates) on the bottom right. Data sets are available at GSE99107.
S8B ). We repeated this operation several times by varying also the values of R and took the realization with the lowest local minimum (Fig. 4A  and fig. S8B  for R = 1. 4a). The length unit in the simulation (a = 102 ± 4 nm) was then fixed by comparing the predicted distances between the three loci investigated by FISH to the corresponding experimental data accounting for a random experimental error of ~100 nm (fig. S8C ; see the "Image analysis" section for the calculation of the experimental error). For a given simulated configuration, the overlap fraction between TAD 1 and TAD 2 was computed by estimating the 3D convex envelope of each TAD using the MATLAB function convexhull and by computing the fraction of TAD 1 and TAD 2 monomers belonging to both envelopes (an illustration in 2D is given in fig.
Acknowledgments: We thank  H. Hoang ,  B. Beliveau ,  M. Hannan , and  C. -T. Wu  for help in the design of Oligopaints and for the 3-Mb Oligopaint probe. We thank the  Montpellier Resources Imagerie facility  ( BioCampus Montpellier ,  CNRS ,  INSERM ,  Université de Montpellier)  for microscopy support, especially  J. Mateos-Langerak  for help in SIM and  J. Cau  and  A. Sarrazin  for image analysis. We thank MGX sequencing and  Drosophila  facilities ( BioCampus Montpellier ,  CNRS ,  INSERM ,  Université de Montpellier ). We thank the  Genotoul bioinformatics facility Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing and storage resources. We thank  B. de Massy  for critical reading of the manuscript. Funding: Q. S. was supported by the  French Ministry of Higher Education and Research.
J. -M. C. was supported by the  Taiwan Ministry of Science and Technology (MOST   105-2218-E-004-003  and  MOST 106-2221-E-004-011-MY2 ). B. B. was supported by  Sir Henry Wellcome  Postdoctoral Fellowship WT100136MA. Research in the laboratory of M. N. is funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 724429 ) and by  France-BioImaging  ( ANR-10-INBS-04-01 ). Research in the laboratory of D. J. was supported by the  CIMENT infrastructure  (supported by the  Rhône-Alpes region , grant  CPER07 13 CIRA ) for computing resources, the  Agence Nationale de la Recherche  ( ANR-15-CE12-0006  EpiDevoMath), the  Fondation pour la Recherche Médicale  ( DEI20151234396 ), and the  CNRS. F. B. was supported by  CNRS  and the Fulbright Visiting.
Scanning was performed using an X-ray Computerized Tomography (CT) scanner (Siemens Somatom Definition AS). The X-ray source was set at 100 kV and 120 mA. Five hundred images were acquired every 0. 6 mm, with a pitch of 0. 45. The images were reconstructed using a reconstruction filter Safire I26. Phantoms of known fat tissue were used to calibrate the scanning parameters for adipose tissue measurement and phantoms were reconstructed under the same parameters as the animals. Analyses of all data were carried out using an Acquisition Sinogram Image Processing IDL's virtual machine (ASIPro VM, Siemens Medical Solutions).
Statistical analysis SAS software (version 9. 3) was used for all analyses. A two-way ANOVA test in repeated measurements was used to compare the mean values for body weight, feed intake, fat content and plasma concentrations of biochemical parameters between different treatment groups from the beginning of the experiment to week 39. The model used was: Y ijkl ¼ m þ Diet i þ Supp j þ Wk k þ Diet i Ã Supp j þ Diet i Ã Wk k þ Diet i Ã Wk k þ e ijkl where Y ijkl is the dependent variable (body weight, feed intake, fat [ultrasound and BIA] and plasma biochemical parameters), μ is the overall mean, Diet i is the fixed effect of diet i (I = Rt, Ad), Supp j is the fixed effect of fish oil j (j = supplemented, unsupplemented), Wk k is the fixed effect of week k (k = 1, 2), Diet i Ã Supp j is the interaction between Diet i and Supp j , Diet i Ã Wk k is the interaction between Diet i and Wk k , and e ijkl is the residual error.
(  2010 ) found an increasing effect of food restriction on ADIPOQ mRNA expression in AT  [36]. These may be explained by a transitory effect which depends on the duration of the food restriction. In the present study, we also observed that plasma RARRES2 was negatively correlated with the percentage of fat determined by BIA or ultrasound. These data are in contrast to those reported in humans. Indeed, plasma RARRES2 levels are increased in obese patients and reduce after bariatric surgery  [37]. All of these results indicate that adipokines, especially RARRES2 and ADIPOQ, may be potential markers of fat mass in broiler breeder hens. In the current study, we observed no fish oil-induced effects on growth, performance or fattening, whereas fish oil supplementation significantly increased the levels of omega 3 PUFAs in the adipose tissue and muscles.
Acknowledgements This work has received the support of the  French National Research Agency  ( ANR-11-CEPL-0009  -Project  PRIGOUE: Privatizing Global Environmental Governance?The Roles of Multi-stakeholders Standards Initiatives for Global Environmental Change Mitigation ), the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Marie Skłodowska-Curie grant agreement 691053  (Project  ODYSSEA ) and the  Brazilian research foundation of the State of Maranhão, FAPEMA  (Edital no  008/2015 BPV Exterior ).
The food enzyme is an a-amylase (4-a-D-glucan glucanohydrolase; EC 3. 2. 1. 1), produced with the genetically modified Aspergillus niger strain NZYM-SB by Novozymes A/S. The food enzyme does not contain the production organism or its DNA; therefore, there is no safety concern for the environment. The a-amylase is intended for use in starch processing, beverage alcohol (distilling) processes and baking processes. Residual amounts of total organic solids (TOS) are removed by distillation and by the purification steps applied during the production of glucose syrups (by > 99%). Consequently, dietary exposure was not calculated for these two uses. Based on the maximum use levels recommended for the baking processes and individual consumption data from the EFSA Comprehensive European Food Consumption Database, dietary exposure to the food enzyme-TOS was estimated to be up to 3.
Reaction and fate in food The a-amylase catalyses the hydrolysis of 1,4-a-glycosidic linkages in starch (amylose and amylopectin) resulting in the generation of dextrins and other malto-oligosaccharides. Experimental data have been provided on the removal (> 99%) of protein in the course of distilled alcohol production and starch processing for the production of glucose syrups (Documentation provided to EFSA No. 5). The Panel considered the evidence as sufficient to conclude that residual amounts of TOS (including substances other than proteins) are removed by distillation. In addition, when taking into account the purification steps applied to the production of glucose syrups i. e. filtration, ion exchange chromatography, treatment with active carbon, the Panel also considers that the amount of TOS in the final glucose syrup is removed to a similar degree.
Dietary exposure As residual amounts of TOS are removed by distillation and by the purification steps applied during the production of glucose syrups (by > 99%), a dietary exposure to the food enzyme resulting from these intended uses was not calculated. For baking processes, dietary exposure estimates were calculated using the methodology described in the CEF Panel statement on the exposure assessment of food enzymes (EFSA CEF Panel, 2016). The assessment of the food processes covered in this opinion involved the selection of relevant food groups and application of process and technical conversion factors (Appendix B). These input data were subject to a stakeholder consultation through open calls  8  and adjusted in accordance with feedback received. The description provided by the applicant has been harmonised by EFSA according to the 'EC working document describing the food processes in which food enzymes are intended to be used'not yet published at the adoption of this opinion.
EFSA Comprehensive European Food Consumption Database Since 2010, the EFSA Comprehensive European Food Consumption Database (hereafter the EFSA Comprehensive Database 9  ) has been populated with detailed national data on food consumption. Competent authorities in European countries provide EFSA with data on the level of food consumption by individual consumers, as taken from the most recent national dietary survey in their country  (EFSA, 2011a). The food consumption data gathered by EFSA were collected using different methodologies, and thus, direct country-to-country comparisons should be made with caution. Depending on the food category and the level of detail used in exposure calculations, uncertainties might be introduced owing to subjects underreporting and/or misreporting of consumption amounts.
Nevertheless, the EFSA Comprehensive Database is the best available source of food consumption data across Europe. Food consumption data from the population groups, infants, toddlers, children, adolescents, adults and the elderly, were used for the exposure assessment. For the present assessment, food consumption data were available from 33 different dietary surveys carried out in 19 European countries (Appendix A). Consumption records were codified according to the standardised food classification and description system (FoodEx) (EFSA, 2011b).
Exposure to food enzyme-TOS according to the intended use proposed by the applicant Exposure to the food enzyme-TOS was based on intended use and the recommended maximum use levels of the food enzyme-TOS provided by the applicant (Section 3. 1. 7). Foods/ingredients derived through distilled alcohol production and starch processing were excluded from the analysis as the Panel considered the presence of residual amounts of TOS in distilled alcohol and glucose syrups as negligible (see  Section 3. 1. 8). Therefore, dietary exposure to food enzyme-TOS was calculated from foods produced involving a baking process only. Relevant food groups and/or individual foods were selected from the Comprehensive Database and were assumed to always contain the food enzyme-TOS at the maximum recommended use level.
This will result in an overestimation of exposure to food enzyme-TOS. To facilitate matching of the reported use levels for baking processes with foods identified in the Comprehensive Database, the selected foods were disaggregated to ingredient level as appropriate and converted into the corresponding raw material i. e. flour and malted barley/starch, via the application of conversion factors (Appendix B). For example, consumption of 100 g of bread was converted into an intake of 70 g flour (recipe fraction of 0. 7) and then multiplied by 274 mg TOS/kg flour (Table  2 ) to arrive at an exposure of 19. 18 mg TOS/100 g bread. Exposure to the food enzyme-TOS was calculated by multiplying values reported for each food category by their respective consumption amount per kilogram of body weight (kg bw) separately for each individual in the database.
Briefly, the complete 2. 3-kb capsid region (encoding the VP1-major capsid and VP2-minor capsid regions) of Bo/BNeV/MA415/04/KR was amplified from the above-described fecal sample by reverse transcription-PCR (RT-PCR) with forward (5=-AAACATGAGTGACAACAAAAGCATCCCAGA-3=, nucleotide position 5055 to 5084 of VP1 region) and reverse (5=-TCAAACACTCGTGGTCGAGAACACTGAC-3=, nucleotide position 7360 to 7387 of VP2 region) primers designed from the full-length sequence of Newbury agent 1 strain in the GenBank database (accession number NC_007916). The amplicon was ligated into the pCR2. 1-TOPO vector (Invitrogen, CA) and then transformed to DH5␣ competent cells (Enzynomics, Daejeon, South Korea). Plasmids were purified using GeneAll Hybrid-Q Plasmid Rapidpre (GeneAll, Seoul, South Korea), and the sequence (GenBank accession numbers EF528565 and MG009451) was verified using an ABI system 3700 automated DNA sequencer (Applied Biosystems, Foster City, CA).
03 (GraphPad Software Inc. , La Jolla, CA, USA), and a one-way analysis of variance test. P values of less than 0. 05 were considered statistically significant. Figures were generated using Adobe Photoshop CS3 and Prism 5, version 5. 03. Accession number(s). The sequence for the VP2-minor capsid region of Korean BNeV strain MA415 (Bo/BNeV/MA415/04/KR) has been deposited in GenBank under accession number MG009451. 1.
ACKNOWLEDGMENTS We thank  Ulrich Desselberger  ( Department of Medicine, University of Cambridge, United Kingdom ) for insightful comments on the manuscript. K. -O. C. was supported by a grant ( 2017R1A2B3002971 ) from the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  that is funded by the  Ministry of Science, ICT, and Future Planning, Republic of Korea. J. L. P. was supported by the  ARMINA  project, a grant from the  Région des Pays de la Loire, France.
Relative humidity (RH) and temperature (T) were measured using an RHT probe (HMP60, Vaisala, Finland) placed inside the chamber and photosynthetically active radiation (PAR) was measured using a quantum sensor (PAR-SA 190®, LI-COR, USA) placed outside the chamber. Measurements with the RHT probe and the PAR sensor were recorded every minute using a data acquisition system (data logger, Li-1400-04®, LI-COR, USA). Once CO 2 and H 2 O exchange had been measured, leaves from enclosed branches were collected in order to calculate the surface area of each leaf with a leaf area meter (AM350, ADC Bioscientific Ltd. , UK) and to report gas exchange of the whole branch leaf surface. The foliage used for the water potential and biochemical analyses was taken from branches adjacent to those enclosed and used for the gas exchange measurements.
In order to check for the occurrence of compensatory mechanisms (increasing the production or emission of some antioxidants as others decrease), Spearman correlations were performed between antioxidants. Spearman correlations were also used to assess the relationship between antioxidants and physiological parameters (Pn and Ψ pd ). Correlations were always carried out for each season with years and treatments pooled together. Principal component analyses (PCA) were performed on antioxidant data for each season, (previously centered and standardized) in order to highlight whether some compounds were produced more under AD in 2014 and 2015 (ade4 package in R) followed by two-way PERMANOVA to check for significance of the results (vegan package in R).
Acknowledgments This work was supported by the  French National Research Agency (ANR)  through the  SecPriMe²  project ( ANR-12-BSV7-0016-01 ),  Europe (FEDER)  and  ADEME/PACA  for PhD funding. We are grateful to FR3098 ECCOREV for the O3HP facilities (https://o3hp. obshp. fr/index. php/fr/). The authors thank the  MASSALYA instrumental platform (Aix Marseille Université , lce. univ-amu. fr)for the analysis and measurements of BVOC used in this publication. We are very grateful to  J. -P. Orts  (IMBE) and  I. Reiter  ( ECCOREV ) for their help at the O3HP platform and  S. Tourrette  ( BVME ) for some biochemical analyses. We also thank all members of the DFME team from IMBE and, particularly,  S. Greff  and S. Dupouyet, for their help during measurements and analyses.
Internal infection data was collected on 269 DH lines, the two parental lines and 6 Australian canola varieties: Crusher, CB-Telfer, Hyola444, ThumperTT, ATR-Marlin, ATR-Stingray which were used as resistant and susceptible controls. The treatment and blocking structure for the 2014 tub test trial can be written using the symbolic notation of  Wilkinson and Rogers (1973)  as Blocking structure: GC + GC. Tray + GC. GCCol. GCRow + SHBay + SHBay. SHBlock Treatment structure: Geno where GC indexes growth chambers and is a factor with 2 levels, Tray is a unique identifier for trays within growth chambers, GGCol and GCRow indexes the spatial arrangement of tray experimental units within each growth chamber, SHBay is a factor which indexes the two sides of the shade house and SHBlock indexes non-rectangular blocks of 5 pots within each shadehouse bay.
Genetic Mapping After the assessment of disease severity of the parental lines, 'Darmor-bzh' and 'Yudal' and their DH progenies, fresh leaf samples were obtained in 96 well formatted tubes. Total genomic DNA was isolated following a high throughout DNA protocol, essentially based on chloroform and phenol. A framework map of the DYDH population, based on 2,094 DArTseq markers was constructed as described previously  (Raman et al. , 2014 (Raman et al. , , 2017)). PCR based SCAR (sequence characterized amplified region) marker specific to Bzh gene for dwarfness was analyzed using the primer-pairs: SCM07UP1 and LP2 as described in  Barret et al. (1998). The quality of marker data was reassessed for its suitability for QTL identification by considering missing data, duplicate markers, markers with excessive segregation distortion, and DH clones that may arise during the microspore culture process.
A total of 249 markers with more than 70% missing data, were removed. Two DH lines (DY043 and DY044) which had more than 70% of missing marker scores were also removed from the analysis. Thirteen DH individuals were removed from the marker data as the proportion of matching genotypes with another marker was more than 99%. The DH individual with the most missing data was the DH individual removed when considering clones. The final set of marker data containing 1842 markers scored as -1, or 1 or NA (missing) over 262 DH lines was used for QTL identification. Missing data for a DH line was imputed using the mean of the 4 nearest neighbors with non-missing data for that DH line using the ASMap  (Taylor and Butler, 2016)  and pedicure  (Butler, 2016)  software packages within the R (R Core Team, 2014) computing environment.
Resistance to L. maculans Is Genetically Controlled in DYDH Population The mean logit-transformed disease scores for plant survival, cotyledon infection and internal infection (canker scores) were calculated for each phenotypic value. There was a significant genotypic variation for plant survival and internal infection scores among the DH lines. Frequency distributions for plant survival and internal infection showed continuous variation, confirming the quantitative inheritance of resistance to L. maculans. The H 2 varied from 43 to 95%. The accuracy was also generally high for each experiment (Table  1 ). For example, the values for accuracy of 'ascospore' shower test carried-out under shade house were 66, 72 to 84% for field experiments and 88-98% for glasshouse experiments. High values for H 2 and accuracy suggested that blackleg resistance was controlled by genetic factors, and the phenotypic data could be confidently used for detecting reliable QTL.
Introduction Models are increasingly used in impact assessments of climate change on crop production and food security  (Ruane et al. , 2017). Models intended for these applications require suitable datasets to minimize the error in the projections  (Wallach, 2011). The crop modelling community has repeatedly addressed and improved the definition of suitable datasets  (Nix, 1983; Boote et al. , 1999; Hunt et al. , 2001; White et al. , 2013). The latest efforts have been made in the context of AgMIP  (Rosenzweig et al, 2013)  and MACSUR  (Rötter et al. , 2013)  projects. Boote et al. , (2016)  developed a generic qualitative method that ranks datasets based on the presence or absence of input and state variables. Kersebaum et al. , (2015)  designed a numerical classification approach where rules based on expert opinion provide scores for several desirable features.
The total quality score of a dataset is the summation of scores from each feature. Further contributions to the definition of suitable datasets go through replacing expert opinion by empirically based rules. Hence, further research is needed assessing the impacts of dataset features on simulations and model performance. Confalonieri et al. , (2016)  worked in this direction by introducing a method for assessing changes in model performance depending on measurement errors. He et al. , (2017)  quantified the repercussions of the number of seasons and state variables on their effectiveness to calibrate a crop model. The results of these studies are key to elucidate the interactions between data and crop model but their comparison with the rules in  Kersebaum et al. , (2015)  is not straightforward.
In order to favour this comparison, features of datasets should be changed and assessed in a progressive and comprehensive manner. The number of observations and the measurement error (as a proxy for number of replicates) are two essential features of datasets in the scoring system by  Kersebaum et al. , (2015). This is due to their critical role in estimating model parameters and their uncertainty  (Wallach et al. , 2011; Confalonieri et al. , 2016)  and the relevance of parameter uncertainty in impact assessments of climate change  (Wallach et al. , 2011; Wallach et al. , 2017). Large and accurate datasets could reduce parameter uncertainty but the crop modelling community has suffered from chronic data scarcity exacerbated by ensemble modelling  (Rötter et al. , 2011; Jones et al. , 2017).