dataesr/scientific-paragraphs-categorization huggingface dataset:
The Delphi process was conducted and managed using DelphiManager software developed by the COMET Initiative (http://www. comet-initiative. org/delphimanager/). The anonymised data from both rounds of the Delphi process, itemised by stakeholder group, are available in S1 Delphi Data. The consensus meeting was a one-day event held in London, United Kingdom, in January 2016, with 17 international participants, including COS developers (n = 6), medical journal editors (n = 4), COS users (n = 5) [trialists (n = 1), Cochrane systematic review co-editors (n = 2), and clinical guideline developers (n = 2)], and patient representatives (n = 2). Individuals were selected to be invited to the meeting using the following broad principles: (i) Delphi participants who completed both rounds; (ii) a balance across the four stakeholder groups; (iii) a balance amongst COS developers of those using Delphi surveys and those using other methods; (iv) a balance across COS users of trialists, systematic reviewers, and guideline developers; and (v) a reasonable geographic spread.
Acknowledgments The study team would like to thank all those members who contributed to the consensus meeting, to those who took part in the Delphi survey exercise, and those who tested the guideline. Those acknowledged provided permission to be mentioned as participants in the development of the COS-STAR reporting guideline. Consensus meeting attendees:  Sara Brookes ,  School of Social and Community Medicine, University of Bristol (Bristol, UK );  Sally Crowe ,  Crowe Associates (Oxen, UK) ;  Mandy Daly ,  Irish Neonatal Health Alliance (Wicklow, Ireland) ;  Christopher Eccleston ,  Department for Health, University of Bath (Bath, UK ); Trish Groves, The BMJ, ( London, UK );  Kirstie Haywood ,  Royal College of Nursing Research Institute, University of Warwick (Warwick, UK );  Angelos G Kolias ,  Cambridge Neuroscience ,  University of Cambridge (Cambridge, UK) ;  Monika Nothacker ,  Guidelines International Network (Berlin, Germany) ;  Larry Peiperl ,  PLOS Medicine  ( San Francisco ,  US) ;  Daniel Shanahan ,  BioMed Central (London, UK) ;  Toni Tan , The  National Institute for Health and Care Excellence (Manchester, UK) ;  Jilda Vargus-Adams ,  Cincinnati Children's Hospital Medical Center  ( Cincinnati, US );  Jos Verbeek ,  Finnish Institute of Occupational Health (Helsinki, Finland).
Capillary Electrophoresis-Single Strand Conformation Polymorphism (CE-SSCP) is based on the electrophoretic mobility of single-stranded DNA fragments. This mobility is different according to their three-dimensional conformation. The samples were then allowed to co-migrate with the fluorescent size standard (GeneScan 400 ROX) to enable comparison of migration profiles between samples. Patterns were aligned with the Stat Fingerprints program  [26]  and studied by principal component analysis (PCA) using R software (version 2. 15. 2).
Structure and diversity analysis of microbial community: The characterization of the structure and the diversity of rhizosphere microbial community (fungi and bacteria) was performed based on profiles obtained using the CE-SSCP method according to Kimsé et al. [27]  and Michelland et al. [26]. All readable molecular fingerprint profiles were aligned with the internal ROX ladder and normalized, to produce relative abundance data with the R package Stat fingerprints v1. 3 software. This yielded a matrix in which root samples were indicated in rows, and fluorescence values (4866 scans) in columns. A fluorescence profile may be seen as a quantitative descriptor of the microbial assemblage of a sample. Bigger differences in fluorescence scans between profiles indicate a greater dissimilarity in composition between samples  [26, 27].
Diversity of rhizosphere microbial (fungi and bacteria) community was evaluated using Fingerprint molecular profiles studied using PCA in relation to environmental factors with R software (version 2. 15. 2, including FACTOMINER packages). In total, 72 samples were analyzed, i. e. , 36 fungal amplicons (obtained using ML1 and ML2 primers) and 36 bacterial amplicons (obtained using primers 799f and 1115r).
Statistical Analysis Data were analyzed using one-way analysis of variance and means separations were carried out according to Duncan's Multiple Range test at (P ≤ 0. 05). ANOVA analysis was performed using SPSS version 16. 0 for all disease severity and plant growth parameters. The tests were conducted according to a completely randomized design where 6 treatments were tested and each individual treatment was replicated 12-15 times (i. e. , 12-15 plants per individual treatment). The whole experiment was repeated twice (in 2012 and 2013 trials) and all data collected was presented in this paper. The relationships between Sclerotina Stem Rot severity and plant growth parameters were compared using Pearson's correlation analysis at P ≤ 0. 05. Analysis of the genetic structure of fungal and bacterial community inhabiting the rhizosphere war perfomed using PCA (R software version 2.
Roots fresh weight Data provided in Table  5  indicated that using rhizobacterial strains either singly or as consortium, root fresh weight (RFW) was significantly (P ≤ 0. 05) augmented relative to S. sclerotiorum-inoculated and untreated control, and that their PGP effect varied upon tomato cultivars grown and year trials. For cv. Marmande, RFW was improved by 57. 45 to 64. 87% in 2012 and by 60. 55 to 67. 21% in 2013 versus 12. 74 and 51. 21%, respectively, noted on fungicide-treated plants. However, on cv. Rio Grande plants, RFW increment obtained using single strains or their consortium varied from 42. 02 to 56. 79% in 2012 trial and from 70. 65 to 78. 12% in 2013 relative to 40. 07 and 67. 05%, respectively, recorded on plants treated with fungicide. Regarding their comparative capacity to increase the RFW of tomato plants already challenged with S.
Genetic structure of microbial community colonizing the rhizosphere of treated tomato plants In total, 72 SSCP profiles (36 for bacteria and 36 for fungi) were generated from root samples collected from tomato cvs. Marmande and Rio Grande plants in 2013 trial. Based on the number of peaks and the relative height of the baseline, the SSCP profiles revealed complex microbial community (data not shown). Principal Component Analyses (PCAs) were performed to compare the genetic structure of bacterial and fungal communities inhabiting the rhizosphere of both tomato cultivars infected with S. sclerotiorum and treated using rhizobacterial strains either singly or as consortium. The distributions of data from the different root samples on the principal plans generated by the PCA analysis for fungi and bacteria communities are provided in Figure  1.
Acknowledgement This work was funded by the  Ministry of Higher Education Scientific Research in Tunisia  through the budget assigned to  UR13AGR09-Integrated Horticultural Production in the Tunisian Centre-East, Regional Centre of research on Horticulture and Organic Agriculture of Chott-Mariem-Tunisia , and  INRA Bordeaux-France  through the budget allocated to SAVE UMR. Authors also thank the whole team  UMR SAVE/INRA Bordeaux-France and CFBP/INRA Anger-Nante team  for their hospitality, their guidance and support.
Experimental details Elemental La, Ca, Mg and Ni powders (with at least 99. 9 % purity) were mixed with the nominal composition LaCaMgNi 9 and charged into carbide vials under controlled atmosphere (purified argon in glove box). MA was performed with a Fritsch Pulverisette P7 planetary ball mill at a rotation speed of 400 rpm with a ball to powder ratio equal to 17:1. The MA duration was varied from 4 to 30 h and the samples were labeled Sd (S for sample and d for duration in hour). Each powdered sample was sieved to a particle size below 40 lm for X-ray powder diffraction (XRD) analysis. XRD patterns were recorded with a (h-2h) Panaltytical X'Pert pro MPD diffractometer with a copper anti-cathode (k CuKa = 0. 15406 nm) in a 2h range of 10°-100°. The XRD data were analyzed by the Rietveld method  [16, 17]  using FULLPROF program  [18].
Acknowledgments The financial support was guaranteed by the  CMCU  project ( CMCU PHC Utique N°10G1208 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Discussion Although selection for fine branch foraging, food properties, and predation may be sufficient to explain the origin of primate grasping  [8] [9] [10] [11]  , it does not explain by itself the further evolution of enhanced hand dexterity and its variations among primates  2, 12. Hence, it is of importance to distinguish the factors associated with these variations in hand dexterity or hand use to identify the possible relevant selective pressures. This study investigated the relationship between infant carrying strategy (in the mouth or clinging to the fur) and manual dexterity (measured by the relative proportion of hand-grip type used to grasp immobile food items) across 21 strepsirrhine species. These data were collected in an effort to test for a possible evolutionary link between these two behaviours.
Methods Sample. We collected data for 77 individuals of 21 different species of strepsirrhines comprising six of the seven strepsirrhine families (Cheirogaleidae, Daubentoniidae, Indriidae, Lemuridae, Galagidae and Lorisidae), excluding Lepilemuridae. Nearly all data were collected at the Duke Lemur Center (Durham, North Carolina, United States) where housing conditions and enrichment are similar for all species. The only species that was not   43. Light grey color represents species that carry their infant orally whereas black color represents species for which infants grasp the fur of their mother. Three independent origins of 'fur-grasping' are present: (a) the independent origin for the loris clade (Nycticebus coucang and N. pygmaeus); (b) the independent origin for the Coquerel's sifaka (Propithecus coquereli) and, (c), the independent origin for the brown lemurs (Eulemur sp.
Behavioural data collection. We videotaped each individual in its home enclosure during five days during their normal feeding period. We used digital video cameras (SONY HDR-PJ790V, full HD 1080, 24. 1MP; SONY HDR-SR11, 10. 2MP; SONY Handycam, HDR-PJ230, 8. 9MP; SONY HDR-CX240E, full HD 1080, 9. 2MP) for the diurnal species and a low light digital video camera (SONY HDR-SR11 10. 2MP) for the nocturnal species. The usual diet constituted of different food items including raw pre-cut pieces of fruits and vegetables as well as monkey chow (Labdiet Monkey Diet Jumbo Constant Nutrition ® and ZuPreem ® Primate Dry Diet). Although insects are part of the diet for some species we did not analyse manual prehension for these items. As a previous study showed that the properties (size and consistency) as well as mobility of the food item influenced the grasping strategy adopted  24  , we decided to control for food size and hardness to account for any potential bias in manipulative activity across species (Table  S1 ).
As small and soft items were grasped mainly with the mouth by all species, they do not constitute a good model to analyse manual prehension. We considered an item as big when bigger than one hand width of the focal species and as hard when it imposed a significant resistance to the teeth (defined as at least as hard as cucumber in our range of items). Here, we thus analyzed only the grasping of large static items. We analysed the videos using Avidemux 2. 6. 8 (Free Software Foundation, Inc. ). We annotated every instance of identifiable big and hard item grasping, leading to a total of 1929 grips for 102. 06 hours of video analysed. The mean number of grips recorded per individual was 25 ± 2 grips (range from 5 to 86). Grip types were.
Varecia variegata Carried orally (16,18,23,34)  Left into the nest and then carried orally and parked 1-4 6 Table  2. Description of the infant carrying pattern and common litter size of the studied species and their sample size in the study. * From DLC pub#1252, Oct 2013. characterised by the body part(s) involved as oral, unimanual, bimanual, combined oral-unimanual, or combined oral-bimanual. We included in the dataset only individuals observed for at least 5 grip events. Infant Carrying behaviour. We used the literature to characterize the infant carrying behaviour of each species (Table  2 ). Some species were observed to perform both types of behaviour, however, one of these patterns is always dominant. Hence, we classified species in two exclusive categories, "oral carriers" or "fur clingers" based on the most commonly observed strategy  [33] [34] [35] [36] [37] [38].
Statistical Analyses. We analysed the data using R3. 0. 2 (R Core Team 39 ). We arcsine transformed the proportions of the different grips used. Descriptive statistics (means ± standard error of the mean) were calculated for each individual and each species. In this study, "N" was used to denote the number of species whereas "N ind " was used to denote the number of individuals and "N grip " the number of grasping events. To assess the effect of infant carrying on the proportions of grip types, we first analysed the behavioural dataset using a MANOVA (Multivariate Analysis of variance). We then performed post-hoc Mann-Whitney U tests in order to determine the types of grip influenced by infant carrying. Strepsirrhines share their phylogenetic history and, therefore, they cannot be considered as independent data points  40.
To address this issue and determine the adequacy of using conventional or phylogenetically informed statistical analyses we tested the presence of a phylogenetic signal 41 using the R "phylosig" function included in the "phytools" package (version 0. 4-45)  42. Pagel's λ is a scaling parameter for the correlations between species traits, relative to the correlation expected under Brownian motion. Values of λ < 1. 0 correspond to traits being less similar among species than expected based on their phylogenetic relationships. This method requires the use of a phylogenetic tree. We used a consensus tree in version 3 of the 10kTrees Project  43  with the species for which we had behavioural data. We performed a phylogenetic Multiple Analysis of Variance (MANOVA) using the R "aov. phylo" function included in the "geiger" package (version 2.
Acknowledgements The authors thank the anonymous reviewers for helpful and constructive comments on an earlier version of this paper. We thank the staff of the  Duke Lemur Centre  and the staff of the  Vincennes Zoo particularly Delphine Roullet , the primate manager, for their assistance during data collection. We thank  Lluís Socías Martinez  for precious discussions and his relevant remarks. We thank  Daniel Schmitt  for comments and for providing access to Kivell et al. (2016). A. -C. Fabre thanks the  Fondation Fyssen  and the  Marie-Skłodowska Curie fellowship  ( EU  project  655694 -GETAGRIP ) for funding;  E. Pouydebat  thanks the " ATM Collections Vivantes, MNHN, Paris, France"  for funding;  C. E. Wall  thanks the  NSF  for funding ( BCS-1062239 ). This is Duke Lemur Center Publication Number # 1335.
Measurements of normalized differential cross-sections of top quark pair (t t) production are presented as a function of the mass, the transverse momentum and the rapidity of the t t system in proton-proton collisions at center-of-mass energies of √ s = 7 TeV and 8 TeV. The dataset corresponds to an integrated luminosity of 4. 6 fb -1 at 7 TeV and 20. 2 fb -1 at 8 TeV, recorded with the ATLAS detector at the Large Hadron Collider. Events with top quark pair signatures are selected in the dilepton final state, requiring exactly two charged leptons and at least two jets with at least one of the jets identified as likely to contain a b-hadron. The measured distributions are corrected for detector effects and selection efficiency to cross-sections at the parton level. The differential cross-sections are compared with different Monte Carlo generators and theoretical calculations of t t production.
The specific decay modes refer to the ee, µµ, and eµ channels. In the 8 TeV measurement, one lepton must be an electron and the other must be a muon (the eµ channel). This channel provides a data sample large enough for the measurement to be limited by systematic uncertainties at 8 TeV. In the 7 TeV analysis, where the integrated luminosity is smaller, events containing same-flavor electron or muon pairs (the ee and µµ channels) are also selected in order to maximize the size of the available dataset.
Data and simulation samples The datasets used in this analysis were collected from LHC pp collisions at √ s = 7 and 8 TeV in 2011 and 2012. The total integrated luminosities are 4. 6 fb -1 with an uncertainty of 1. 8% at √ s = 7 TeV and 20. 2 fb -1 with an uncertainty of 1. 9% at √ s = 8 TeV. The luminosity was measured using techniques described in Refs. [17, 18]. The average number of pp interactions per bunch crossing (pileup) is about 9 for the 7 TeV dataset and increases to about 21 for the 8 TeV dataset. The data sample was collected using single-lepton triggers. The √ s = 7 TeV dataset uses a single-muon trigger requiring at least one muon with transverse momentum p T above 18GeV and a single-electron trigger requiring at least one electron with a p T threshold of either 20 or 22GeV, with the p T threshold being increased during data-taking to cope with increased luminosity.
In the √ s = 8 TeV dataset, the logical OR of two triggers is used in order to increase the efficiency for isolated leptons at low transverse momentum, for each lepton type. For electrons the two p T thresholds are 24 GeV and 60 GeV, and for muons the thresholds are 24 GeV and 36 GeV, where only the lower-p T triggers impose lepton isolation requirements. Samples of Monte Carlo (MC) simulated events are used to characterize the detector response and efficiency for reconstructing t t events, to estimate systematic uncertainties, and to predict the background contributions from various physics processes. The samples were processed through the Geant4  [19]  simulation of the ATLAS detector  [20]  and the ATLAS reconstruction software. For the evaluation of some systematic uncertainties, generated samples are passed through a fast simulation using a parameterization of the performance of the ATLAS electromagnetic and hadronic calorimeters  [21].
The W+jets production is simulated using Alpgen with the CTEQ6L1 PDFs interfaced to Pythia6 with the Perugia P2011C tune and the CTEQ6L1 PDFs, including LO matrix elements for Wb b, Wcc, and Wc processes. The t-channel single-top production is modeled using the AcerMC  [55]  generator, while Powheg is used for the production in the s-channel, and both generators are interfaced to Pythia6 using the Perugia P2011C tune and the CTEQ6L1 PDFs. Different methods are used in the two datasets due to the different trigger conditions and because the 7 TeV analysis uses all 3 dilepton channels. Other backgrounds are negligible after the event selections used in this analysis. Table  1  summarizes the baseline signal and background MC simulated samples used in the 7 TeV and 8 TeV analyses.
2 and the sum of track p T within a cone of fixed size ∆R = 0. 3 around the muon, except the contribution from the muon itself, to be less than 4GeV and 2. 5GeV, respectively. In the 8 TeV analysis, muons are required to satisfy I < 0. 05 where the isolation variable is the ratio of the sum of p T of tracks, excluding the muon, in a cone of variable size ∆R = 10GeV/p T (µ) to the p T of the muon  [57]. Both isolation requirements result in an efficiency of about 97% for prompt muons from Z → µµ decays. Jets are reconstructed by the anti-k t algorithm  [58]  with a radius parameter R = 0. 4 using calorimeter energy clusters  [59] , which are calibrated at the electromagnetic energy scale for the √ s = 7 TeV dataset, or using the local cluster weighting method for √ s = 8 TeV  [60]. The energies of jets are then calibrated using an energy-and η-dependent simulation-based calibration scheme with in situ corrections based on data.
Different calibration procedures were used for the 7 TeV and 8 TeV datasets due to the different pileup conditions. The effects of pileup on the jet energy calibration at 8 TeV are further reduced using the jet area method as described in Ref. [61]. Jets with p T > 25 GeV and |η| < 2. 5 are accepted. To suppress jets from pileup, a requirement on the jet vertex fraction (JVF), the ratio of the sum of the p T of tracks associated with both the jet and the primary vertex to the sum of the p T of all tracks associated with the jet, is imposed based on the different pileup conditions in the √ s = 7 TeV and √ s = 8 TeV  [1]. At 7 TeV, jets are required to satisfy |JVF| > 0. 75 while at 8 TeV, jets with p T < 50 GeV and |η| < 2. 4 are required to satisfy |JVF| > 0. 5. To prevent double-counting of electron energy deposits as jets, the closest jet lying ∆R < 0.
The t t-system observables in consideration (invariant mass, transverse momentum, and rapidity) are obtained from this four-momentum. Figures  1  and 2  show the distributions of the reconstructed m tt , p T,tt , and |y tt | together with the MC predictions at 7 TeV and 8 TeV, respectively. The bottom panel shows the ratio of the data to the total prediction. Overall there is satisfactory agreement between data and prediction.
For the 8 TeV analysis, the determined bin widths are generally finer than the bin widths for the 7 TeV analysis due to the larger dataset available. Possible biases due to the use of the MC generator in the unfolding procedure are assessed by altering the shape of the parton-level spectra in simulation using continuous functions. The altered shapes studied cover the difference observed between the default MC and data for each observable. These studies verify that the altered shapes are recovered by the unfolding based on the nominal migration matrices within statistical uncertainties. A multichannel combination is performed in the 7TeV analysis by summing the background-subtracted observed events corrected by the migration matrix and the event selection efficiency over channels. The results obtained from the combined dilepton channel are consistent with those from the individual channels.
Uncertainties Various sources of systematic uncertainty affect the measurement and are discussed below. The systematic uncertainties due to signal modeling and detector modeling affect the estimation of the detector response and the signal reconstruction efficiency. The systematic uncertainties due to the background estimation and the detector modeling affect the background subtraction. The covariance matrix due to the statistical and systematic uncertainties for each normalized unfolded spectrum is obtained by evaluating the correlations between the bins for each uncertainty contribution. In particular, the correlations due to statistical fluctuations are evaluated from an ensemble of pseudoexperiments, each by varying the data event counts independently in each bin and propagating the variations through the unfolding procedure.
The overall impact of these uncertainties is less than 1% in both the 7 TeV and 8 TeV measurements. The fake-lepton contribution is estimated directly from data, using a matrix method  [51]  in 7 TeV data and the same-sign dilepton events in the 8 TeV data sample  [1]. In the 7 TeV analysis, the uncertainty of the fake-lepton background is evaluated by considering the uncertainties in the real-and fake-lepton efficiency measurements and by comparing results obtained from different matrix methods. In the 8 TeV analysis a conservative uncertainty of 50% is assigned to the fake-lepton background  [1]. The impact of the uncertainty is typically less than 1% in all observables, except in high-m tt and high-p T,tt bins where it is up to 5%.