dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR spectroscopy The plasmas were thawed at room temperature. In the 5 mm NMR tubes, 600 mL of plasma was added with 100 mL deuterium oxide for field locking. The proton spectra were acquired at 500 MHz on a Bruker Avance II spectrometer with a 5 mm reversed QXI Z gradient high resolution Bruker probe. The temperature was 297 K. The FIDs were acquired using the noesy1D sequence for water suppression including a pre-acquisition delay of 2 s, a 100 ms mixing time and a 90u pulse. The FIDs were collected on 32 K complex points for a spectral window of 6000 Hz and 64 transients after four silent scans. The use of a noesy1D sequence preserves the lipid resonance integrity. The FIDs were processed with the NMRpipe software. The Fourier transform was performed with an exponential function producing a 0.
PCA and OPLS analyses were performed using an in-house Matlab code (Mathworks, Natick, MA) based on the Trygg and Wold method  [9]  as previously described  [10]. The parameter quality for the OPLS model was assessed by calculation of R2Yand Q2Y. The R2Y represents the explained variance of the Y matrix. The Q2Y, computed with the ''leaveone-out'' method, estimates the predictability of the model. R2Y = 1 indicates perfect description of the data by the model, whereas Q2Y = 1 indicates perfect predictability. For internal validation of the OPLS models, a permutation test (999 permutations) was performed. This evaluated whether the OPLS models, built with the groups, was significantly better than any other OPLS model obtained by randomly permuting the original group attributes. A score plot illustrated the results.
Acknowledgments The authors greatly acknowledge: -All the horse's owners, riders and trainers who participated to the study; -All volunteers,  INRA  and  ENVA  staff who participated to the  GenEndurance  project for collecting the data; -The organizers and judges of the endurance races who gave us the opportunity to do the data collection; - Nadia Bouchemal (UniversiteParis 13 ) and  Florian Messier  ( Universite d'Evry Val d'Essonne ) for NMR experiments and processing.
Data analysis Data were analyzed through ANOVA using the PROC MIXED procedure (SAS Institute Inc. , Cary, NC, USA) with diet as fixed effect in experiments 1 and 2, the Phe : (Phe + Tyr) ratio, the minimum Phe + maximum Tyr level, and their interaction as fixed effects in experiment 3 and block as a random effect. Results are reported as least square means. In experiments 1 and 2, the response to the Phe and Tyr supply was also analyzed by regression with a curvilinearplateau model using the PROC NLIN procedure of SAS (Robbins et al. , 2006). The model was parameterized to include a plateau for each block, the minimum SID AA concentration to reach the plateau (i. e. the requirement estimate) and the response at 90% of the requirement relative to the plateau value (i. e. the response for an AA supply 10% below the requirement).
Data collection and analysis The behaviour of focal birds was analysed from videotapes using an event-recording program (EthoLog 2. 2  [50] ). All videos (n = 180) were analysed at 1X reading speed after testing on a subset of trials that this speed provided correlated results to those obtained by analyzing the videos frame by frame. (Foraging time; Pearson correlation coefficient: r = 0. 781; p,0. 01, n = 90). Both the pecking rate (expressed as the number of pecks during the 5 minutes trial) and the time devoted to foraging and vigilance (in seconds) were separately quantified. Using video recording, the posture of birds was analysed for each trial. Following Whittingham & Markland  [51]  and Powolny et al. [48] , a bird was considered to be vigilant when its head was above the horizontal line made by its body, and not orientated towards the ground.
To test for habituation, GLMMs were performed on pecking and intake rates and foraging time using trial number as continuous covariate. Results did not show any evidence for habituation (pecking rate: F = 1. 633; df = 1, 173; p = 0. 204; intake rate: F = 0. 048; df = 1, 173; p = 0. 827; foraging time: F = 0. 548; df = 1, 173; p = 0. 862). All analyses were performed using SPSS 17. 0 software. Means are expressed 6 SE.
Acknowledgments We thank  V. Rocheteau  for assistance during fieldwork,  N. Guillon ,  X. Duchemin  and  C. Collas  for caring for the birds while in captivity. We also thank  M. Guillemain  and  O. Pays  and anonymous referees for valued comments on experiments and  S. Yeo  and  J. Hennicke  for English language editing.
Data analysis Fragment sizes converted to repeat unit (U) numbers  [25]  were analyzed as a character data set, using BioNumerics v6. 6 (Applied Maths, Belgium). According to the molecular evolution, weights were assigned to the distinct panels (weights of 2, 1 and 0. 1, respectively for the panel 1, panel 2A and panel 2B)  [17]. MLVA genotypes were identified into the database Brucella 2012 (Orsay, France) (http:// mlva. u-psud. fr/mlvav4/genotyping/). The cluster analysis was performed using the UPGMA (Unweighted Pair Group Method Algorithm) algorithm or using a Minimum Spanning Tree (MST) with Euclidean distance matrices. Regarding the wide world circulation of B. melitensis bv 3, especially in Europe and in Maghreb, the cluster analysis was extended to 507 published MLVA patterns, including MLVA genotypes of the Maghreb strains investigated in this study  [17, [25] [26] [27] [28] [29] [30] [31].
1%) (S1 Table ). Interestingly, the genotype 94 seems to be shared only by the Maghreb strains, although in the public database Brucella 2012, some strains identified as non Maghreb strains harbor this genotype 94, e. g. BCCN#03-13, a strain isolated in 2003 in France  [17]. These strains might be epidemiologically misidentified. As previously described, MLVA-11 allows assessing global phylogeographic relationship  [30]. MLVA-11 cluster analysis based on 497 worldwide B. melitensis bv 3 strains, including the Maghreb strains investigated in this study, was performed (Fig. 2 ). The strains were divided into three clusters: West Mediterranean, East Mediterranean and American clusters  [17]. As previously described, the East Mediterranean group essentially contained the strains isolated from Turkey (including M091)  [42] , China  [40, 43] , Germany and Greece, while the West Mediterranean group was constituted by the European strains (Italy, Spain, France, Switzerland)  [26] [27] [30] [31]  and the Maghreb strains (Algeria, Morocco, Tunisia) (this study).
Imaging diagnosis Diagnosis was either based on survey radiographs and myelography or on CT myelography. Radiographs of the thoracolumbar spine were reviewed. Myelograms were obtained by subarachnoid injection of Iohexol (0. 3-0. 4 ml/kg of body weight) (Omnipaque®, Iohexol 300 mg/ml, Sanofi Winthrop Pharmaceutical, New York, NY) between the 5 th and 6 th lumbar vertebrae. Cross-sectional computed tomography scans (CT scans) were obtained with a third generation scanner (GE High Speed NXI 2002, 2 slice CT Scanner, General Electric Healthcare, Chalfront St-Giles, RU). CT myelography (1 ml of Iohexol diluted in 1 ml of sterile saline) (Omnipaque®, Iohexol 240 mg/ml, Sanofi Winthrop Pharmaceutical, New York, NY) was performed by taking contiguous, 1-mm, transverse images of the entire spine. A three dimensional reconstruction was obtained from each CT scan using computer software (Osirix, 64bit, Pixmeo Swiss made, Geneva, Switzerland).
Outcome Follow-up data were obtained during the second examination 4 weeks after surgery. Owners and referring veterinarians were re-contacted by telephone (i. e. , from 7 months to 4 years after surgery) to assess the current disease status, particularly whether the dog was walking or not, and the owner's satisfaction regarding the improvement of neurological status. Due to the small number of dogs in the study, the results could not be subjected to statistical analysis or compared with other treatments.
Uropygial gland composition analyses Samples of uropygial gland secretions were diluted in 500 μl of hexane, evaporated, and then diluted in 200 μl of dichloromethane and vortexed for 1 min in order to extract organic chemical compounds. Samples were analysed using Gaz Chromatography -Mass Spectrometry (GC-MS; TSQ Quantum; ThermoScientific, plateform MetaToul), with a migration program as follows: 50°C for 1 min, 10°C/min from 50°C to 300°C and then 10 min at 300°C (see Additional file 1 for details). Blanks were interspersed between each sample. Resulting profiles were analysed using Xcalibur software to generate composition matrices. Since we cannot standardize the quantity of secretions sampled by the GC-MS, we used matrix of intra-individual relative quantity of compounds in all analyses  [45].
We use ARISA (Automated Ribosomal Intergenic Spacer Analysis) to measure bacterial community composition  [47]. We amplified highly variable regions of the bacterial ribosomal operon, and measured the length of the amplified fragments by sequencing to obtain profiles composed of several peaks (see Additional file 2 for details), each peak corresponding to an operational taxonomic unit (OTU). This method allows to estimate the diversity of bacterial communities, and to compare samples based on their structure (i. e. the presence or absence of the different OTUs,  [47] ). The peak profiles obtained for bacterial communities were analysed using R software with a standardized automatic method  [48]  in order to obtain the presence/absence data of OTUs. Briefly, this method consists in two steps, the first one aiming at estimating the best shift value and window size to maximize between samples OTUs profile similarity (in this study shift value = 0.
Statistical analyses All analyses were performed using R software (version 2. 14. 0, R Development Core Team 2008). Analyses of differences in bacterial community structure between treatments were performed using non-parametric multivariate analysis of variance based on permutation tests (Adonis;  [49] ). For the analyses of treatment effects on nest bacterial density, we used linear models with year and date as covariates, whereas linear mixed models (lme, nlme R package) with nest as a random factor were performed to analyse the effects of treatments on feather microbial densities. Finally, we used Shannon diversity index to test for differences in bacterial diversity between the treatments. We used a principal component analysis (PCA) in order to decompose the variance of chemical composition of secretions into independent components  [45].
We estimated individual body condition through the regression of body mass on tarsus length (body mass = 4. 19 + 5. 89 × tarsus length; r 2 = 0. 49; T = 5. 48; p < 0. 001;  [50] ). We used linear mixed models to analyse the effect of treatments on the volume and composition of the uropygial gland. Date, year, clutch size, body condition and wing length were included as covariates. Year was not included in the analyses of chemical composition since we obtained data only for the second year of the study. Since we expected differences in antimicrobial strategies between sexes, we included a sex by treatment interaction in all models. Treatment nested in the interaction between nest identity and sex was included as a random factor in order to account for the hierarchical structure of our data.
Acknowledgements We thank the families  Bordes, Gers and Labories  who allowed us to install nest-boxes on their site. Carole Sembeille ,  Marion Roullin ,  Marc Etchecopart Echechar  and  Charlotte Perrot  helped during the fieldwork. We thank  Mélanie Roy ,  Jean-Baptiste Ferdy  and  Pascal Le Bourgeois  for discussions. The manuscript has greatly benefited from comments by  Joselito Acosta  and two anonymous referees. This work is part of the  'Laboratoire d'Excellence (LABEX) ' entitled  TULIP  ( ANR-10-LABX-41 ).
Goal definition The goal of this study was to assess potential environmental impacts of different categories of pig farming systems in the EU, defined by the degree to which they differed from conventional systems. System description and data collection A total of 15 EU pig farming systems were chosen among the 84 systems inventory  (Bonneau et al. , 2011). One conventional and two differentiated systems were assessed from each of the five countries: Denmark, The Netherlands, Spain, France and Germany. The systems were classified according to the typology of  Bonneau et al. (2011 Bonneau et al. ( , 2014b) )  into conventional (C, n = 5), adapted conventional (AC, n = 5), and differentiated, composed of organic (O, n = 2, Denmark and Germany) and traditional (T, n = 3, Spain, France and Germany).
The inventory data needed for LCA calculations were obtained from surveys of 5 to 10 farms from each system. This survey collected the information required for global evaluation of multiple themes of sustainability  (Bonneau et al. , 2014b). Different types of farms were considered depending on the system and country: breeding farms, farrow-to-finish farms and fattening farms. Data collected for the environmental theme concerned: (i) animal performance, including sow productivity, mortality rates, pig growth and feed intake during post-weaning and fattening periods, and slaughter characteristics; (ii) feed composition, including metabolisable energy, CP and phosphorus (P) contents, and when available, feed-ingredient contents; (iii) animal housing, including type of housing (e. g. indoor, outdoor, free-range), type of floor (e.
g. litter bedding, complete or partially slatted floor), and ambient temperature; and (iv) manure handling, including management in the building (e. g. liquid, solid, removal frequency) and during storage (type and duration of storage), manure treatment (composting, anaerobic or aerobic digestion) and type and distance of spreading. From the data collected, an 'average' system was built for each farming system. Performance and nutrient flows and emissions were calculated for each production stage, that is, sows and their piglets until weaning, post-weaning piglets and fattening pigs. In this way, it was easy to aggregate up to entire production systems by considering the number of piglets weaned per sow per year and mortality rates of pigs during post-weaning and fattening periods.
Life cycle inventory analysis Production of feed and feed ingredients. The amount and nutrient contents of complete feed used by each category of pig was obtained from survey data. However, since information was generally lacking about the ingredient contents of feed, they were estimated in a way similar to that performed by  Nguyen et al. (2010) , assuming that complete feed was a mixture of cereals (wheat, barley and maize), protein-rich ingredients (soybean meal, rapeseed meal and peas) and minerals (phosphate and calcium carbonate). This calculation was performed for all diets used by each pig category. LCA data on conventionally grown feed ingredients were based on  Mosnier et al. (2011) , who give a detailed description of the methodology used to evaluate impacts of producing non-organic feed ingredients.
Soybean meal was assumed to come from soybeans grown in southern Brazil (i. e. no land-use change within the past 20 years). For other crops, inputs used were based on  AGRESTE (2006). For the transformation of crop products into feed ingredients, data were based on the study by  Nemecek and Kägi (2007)  for maize drying and  Nemecek and Kägi (2007)  and  Jungbluth et al. (2007)  for the production of soybean meal, rapeseed meal and rapeseed oil. Data were also obtained for monocalcium phosphate  (LCA Food Database, 2007). Data concerning resource use and emissions associated with the production and delivery of several inputs for crop production (fertilisers, tractor fuel and agricultural machinery) came from the ecoinvent database, version 2. 0  (Nemecek and Kägi, 2007). The production of seed for sowing was taken into account assuming that inputs required for seed production were similar to those required for the corresponding crop.
Values for organic feed ingredients used in organic pig production systems were based on the LCA Food Database (2007). Production of pigs. Emissions to air were estimated for NH 3 , N 2 O, NO x and CH 4. Emission of CH 4 from enteric fermentation and manure management were calculated according to  Rigolot et al. (2010a and 2010b)  and Intergovernmental Panel on Climate Change  (IPCC, 2006). Direct N 2 O-N emissions from manure during in-house and outdoor storage and during field application were calculated according to  IPCC (2006). Emissions of NO x were estimated according to  Nemecek and Kägi (2007). NH 3 -N emission during in-house storage, outside storage and field application of manure were calculated according to  Rigolot et al. (2010a and 2010b)  according to type of effluent (slurry, solid manure), duration and type of storage and method of spreading.
Life cycle impact assessment The following impact categories were considered: climate change (CC), eutrophication potential (EP), acidification potential (AP), cumulative energy demand (CED) and land occupation (LO). The indicator result for each impact category was determined by multiplying the aggregated resources used and the aggregated emissions of each individual substance with a characterisation factor for each impact category to which it may potentially contribute. CC, EP, AP, CED and LO were calculated using the CML2 'baseline' and 'all categories' 2001 characterisation methods as implemented in the ecoinvent v2. 0 database. CC was calculated according to 100-year global warming potential factors expressed in kg CO 2 equivalents (eq), CH 4 : 25, N 2 O: 298, CO 2 : 1 (IPCC, 2006). EP was calculated using generic EP factors in kg PO 4 -eq, NH 3 : 0.
35, NO 3 : 0. 1, NO 2 : 0. 13, NO x : 0. 13, PO 4 : 1  (Guinée et al. , 2002). AP was calculated using average European AP factors in kg SO 2 -eq, NH 3 : 1. 6, NO: 0. 5, NO: 0. 5, SO: 1. 2  (Guinée et al. , 2002). CED (MJ) was calculated according to version 1. 05 as implemented in the ecoinvent v2. 0 database. LO (m 2 year) refers to on-farm and off-farm area used to produce feed and raise pigs. A description of the CML 2001 and CED methods can be found in  Frischknecht et al. (2007) ; because of a lack of data, terrestrial ecotoxicity and use of pesticides could not be assessed. Some authors have suggested adapting the functional unit chosen to the impact category, that is, kg of product for global impacts and ha of land for local impacts  (Haas et al. , 2001; de Boer, 2003). We followed this approach to compare the non-conventional systems (AC, T and O) and define a smaller set of indicators to be used for an overall (environmental, economic, social) assessment of sustainability (Bonneau et al.
Acknowledgement The authors gratefully acknowledge European Community financial support from the  Sixth Framework Programme for Research, Technological Development and Demonstration Activities  for the Integrated Project Q-PorkChains ( FOOD- CT-2007-036245 ). Disclaimer: The views expressed in this study are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of the information. The information in this study is provided as it is, and no guarantee is given that the information is fit for any particular purpose. The user, therefore, uses the information at its sole risk and liability.
11%, respectively. The global 1,572,948 reads were filtered according to the read qualities. Illumina reads were trimmed using Trimmomatic (13), then assembled with Spades software  (14, 15). Contigs obtained were combined by using SSPACE  (16) , Opera software v 1. 2  (17) , and GapFiller v 1. 10 (18) to reduce the set. Some manual refinements using CLC Genomics v 6 software (CLC bio, Aarhus, Denmark) and homemade tools improved the genome sequencing. The final draft genome of M. mageritense consists of six contigs, containing 7,966,608 bp and a GϩC content of 66. 95%. Noncoding genes and miscellaneous features were predicted using RNAmmer  (19) , ARAGORN  (20) , Rfam  (21) , and PFAM  (22). Open reading frames (ORFs) were predicted using Prodigal  (23) , and functional annotation was achieved using BLASTP against the GenBank database (  24 ) and the Clusters of Orthologous Groups (COGs) database  (25, 26).
The genome was shown to encode at least 120 predicted RNAs, including 4 rRNAs, 95 tRNAs, 1 tmRNA, and 20 miscellaneous RNAs. A total of 7. 675 genes were identified, representing a coding capacity of 7,385,502 bp (coding percentage, 92. 7%). Among these genes, 7,615 (99. 2%) genes matched a least one sequence in the COGs database with BLASTP default parameters, 972 (12. 66%) encode putative proteins, and 1,431 (18. 64%) were assigned as hypothetical proteins. Nucleotide sequence accession numbers. The Mycobacterium mageritense strain DSM 44476 T genome sequence has been deposited at EMBL under the accession numbers CCBF010000001 through CCBF010000006.
Study area and sample sites Between June 2012 and May 2013, periodic sampling of aquatic communities was performed in Akonolinga and Bankim, two regions in Cameroon where BU is endemic  [7, 8]. In order to track colonization dynamics, monthly samples were collected in Akonolinga. In addition, sampling was performed every three months in Bankim, allowing a description of a wider range of environmental characteristics (savannah and tropical rainforest). Within each region, selection of survey sites was done in a two-step procedure. Initially, we classified the villages in each region based on (i) BU human prevalence and (ii) surrounding environmental conditions, according to national health data and land cover data respectively. We pre-selected a number of villages that represented a gradient in both of these parameters within each region.
Detection of MU DNA by quantitative PCR Oligonucleotide primer and TaqMan probe sequences were selected from the GenBank IS2404 sequence  [52]  and the ketoreductase B (KR) domain of the mycolactone polyketide synthase (mls) gene (Table  1 ) from the plasmid pMUM001  [52, 53]. QPCR mixtures contained 5 ml of template DNA, 0. 3 mM concentration of each primer, 0. 25 mM concentration of the probe, and Brilliant II QPCR master Mix Low Rox (Agilent Technologies) in a total volume of 25 ml. Amplification and detection were performed with Thermocycler (Chromo 4, Bio-Rad) using the following program: 1 cycle of 50uC for 2 min, 1 cycle of 95uC for 15 min, 40 cycles of 95uC for 15 s and 60uC for 1 min. DNA extracts were tested at least in duplicates and the 10% negative controls were included in each assay.
Data analysis All statistical analyses were conducted using R statistical software, version 2. 14. 0  [54]. Maps were created using ArcGIS 10. 0 and information displayed in them was obtained from the USGS Shuttle Radar Topography Mission (elevation data)  [55] , IFORA project (hydrographic network) and Institut National de Cartographie du Cameroun (roads). Data on rainfall was obtained from the NASA Tropical Rainfall Measuring Mission  [56]. Pearson Chi Square tests were used to compare proportions of positive sample-pools coming from different types of ecosystems and p-values were computed by Monte-Carlo simulation. Onesample proportions tests with continuity correction were used to calculate the confidence intervals of the proportions. Associations of MU colonization dynamics of taxonomic groups or MU colonization of different ecosystems with rainfall patterns were investigated by calculating the cross-correlation of the time series two by two.