dataesr/scientific-paragraphs-categorization huggingface dataset:
The final dataset of macrofossils included 22 415 occurrences assigned to 443 genera, of which 263 are extinct and 180 are extant. The fossil occurrences were evenly distributed across the three plant groups described above: spore-bearing plants (34%), nonflowering seed plants (34%) and angiosperms (32%). Angiosperms encompassed 58% of the diversity (with 257 genera) whereas the remaining genera were evenly distributed among the other two plant groups. The large majority of fossil occurrences included temporal ranges (minimum and maximum ages), which typically derive from the upper and lower temporal boundaries of the stratigraphic units to which fossils were assigned. In our dataset, temporal ranges spanned on average 12. 7 Myr with a standard deviation of 12. 6 Myr. In order to account for these dating uncertainties in the analyses of origination and extinction rates we considered the temporal ranges as uniform distributions and, from these, we randomly resampled the ages of each fossil occurrence following  Silvestro et al.
Diversification rate analyses We carried out the analyses of plant diversification using a hierarchical Bayesian model to infer the temporal dynamics of origination and extinction  (Silvestro et al. , 2014b). This approach, implemented in the program PyRate (http://sourceforge. net/projects/pyrate/;  Silvestro et al. , 2014a) , uses as input data all fossil occurrences that can be assigned to a taxon, in this case fossil-genera, to jointly model the preservation and diversification processes. The preservation process infers the individual origination and extinction times of each taxon based on all fossil occurrences and on an estimated preservation rate (expressed as expected  Ó 2015  The Authors New Phytologist Ó 2015 New Phytologist Trust. (2015) 207: 425-436  www. newphytologist. com occurrences per taxon per Myr).
The estimation of origination and extinction rates within fixed time intervals improved the mixing of the MCMC and allowed us to obtain an overview of the general trends of rate variation throughout a long timescale, that is, almost the entire the Phanerozoic, within reasonable computational effort (a few days on a computer cluster). We emphasize that both the preservation and the birth-death processes are still modeled in continuous time and are not based on boundary crossings. Thus, the origination and extinction rates are measured as the expected number of origination and extinction events per lineage per Myr. One potential problem in fixing a priori the number of rate shifts is overparameterization. We overcame this issue by assuming that the rates of origination and extinction are part of two families of parameters following a common prior distribution, with parameters estimated from the data using hyper-priors  (Gelman, 2004).
Thus, we used Cauchy prior distributions centered at 0 and with scale (hyper-)parameters estimated from the data in the MCMC. The use of hyper-priors is a convenient way to reduce the subjectivity in defining the priors and to reduce the effective parameter space, thus limiting the risks of overparameterization  (Gelman, 2004). We ran PyRate for 5000 000 MCMC generations on each of the 100 randomly replicated datasets. After excluding the first 20% of the samples as burnin phase, we combined the posterior estimates of the origination and extinction rates across all replicates and used them to generate rates-through-time plots. Rates of two adjacent intervals were considered significantly different when the mean of one lay outside of the 95% HPD of the other, and vice versa. We looked at the marginal posterior distributions of origination and extinction rates through the largest extinction events documented in geological history, the so-called 'Big Five' mass extinction events in life's history  (Jablonski, 2005; McElwain & Punyasena, 2010).
In particular, we examined the diversification dynamics at four of the 'Big Five' events (the Frasnian-Famennian, Permian-Triassic, Triassic-Jurassic and Cretaceous-Paleogene boundaries; see the Introduction section; Figs  1, 2 ). The Ordovician-Silurian extinction event occurred before the appearance of vascular plants and was therefore not considered. We focused on the magnitude of rate changes, their statistical significance and the uncertainty around those estimates. We measured extinction as the rate of disappearance of fossilgenera, which are described based on morphology and might not always represent phylogenetic lineages. Thus, it was not possible with our data to discern potential events of pseudo-extinction (i. e. when two fossil-genera describe a single lineage evolving to a modified form).
Origin of major plant clades The estimated times of origin of vascular plants, seed plants and angiosperms were best fitted by log-normal, gamma and Laplace distributions, respectively (Fig. 3 ; Table  S3 ). Our results indicate that the diversification of vascular plants started during pre-or Early Silurian times (mean = 433. 5; 95% HPD 449. 0-424. 0 Ma; Fig. 3a ). The origins of seed plants and angiosperms were estimated to the Middle Devonian (mean = 373. 8, 382. 3-367. 2 Ma; Fig. 3b ) and Late Jurassic-Early Cretaceous (mean = 143. 8, 95% HPD: 151. 8-133. 0 Ma; Fig. 3c ), respectively. These ages predate those of the oldest fossil occurrences included in this study for each group, because they reflect the use of all available occurrence data to model the preservation process while incorporating the uncertainties around the fossil ages.
). These distributions can be directly implemented as calibration priors for dating the phylogenies of these clades using Bayesian relaxed molecular clocks. The parameterization provided here follows that implemented in the popular molecular dating program BEAST  (Drummond et al. , 2012). In comparison with the Permian-Triassic and the Cretaceous-Paleogene crises, our results suggest that the other widely recognized mass extinction events (at the Frasnian-Famennian and Triassic-Jurassic boundary) had less detectable effects on plant diversity. However, although  Cascales-Minana & Cleal (2014)  found no significant increase in extinction rates at the Frasnian-Famennian boundary at the family level, here we have detected important changes at the generic level. The high extinction rates estimated for the Late Devonian (Fig.
Our estimation derives from an analysis of the entire set of macrofossil occurrences and accounts for the preservation process. Although the direct incorporation of fossil taxa in molecular dating is possible and promising  (Ronquist et al. , 2012; Heath et al. , 2014) , these methods either require large morphological datasets or are computationally intensive and potentially difficult to implement on large clades. Thus, many studies still rely on fossil prior calibrations for molecular dating. The methodology used in this study constitutes a novel approach to objectively define calibration priors, in a process-based and data-driven analysis that is able to handle the inevitably incomplete nature of the fossil record. The age estimates derived here for the three major plant clades (Fig. 3 ) could thus be directly used as prior crown age distributions of those clades in molecular dating analyses.
Acknowledgements We thank  Susana Magall  on,  Erika Edwards  and  Xavier Meyer  for discussions, and the organizers of the  Plant Radiation Meeting (Zurich, Switzerland, June 2014) Colin Hughes ,  Peter Linder  and  Reto Nyffeler. Analyses were run at the high-performance computing center Vital-IT of the  Swiss Institute of Bioinformatics (Lausanne, Switzerland). We are thankful to  Allister Rees ,  Hallie Sims ,  Bruce Tiffney ,  Peter Wilf ,  Cindy Looy ,  Rick Lupia ,  Bill Stein  and  John Alroy , the major contributors of paleobotanical data to Paleobiology Database. We acknowledge financial support from  Carl Tryggers  stiftelse and  Wenner-Gren  foundation to D. S. , Project  ANR-2010-BLAN-607-02  ' TERRES ' to B. C-M. , and from the  Swedish Research Council  ( B0569601 ) and the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013,  ERC  Grant Agreement n.
Automatic annotation yielded 4,557 (D6-117. 07) and 4,804 (D6-113. 11) nonredundant coding sequences. The strains are available upon request at the International Center for Microbial Resources-Bacterial Pathogens (CIRM-BP [http://www6. inra. fr/cirm_eng/Pathogenic-Bacteria]) under collection reference numbers and 2IM-260 (D6-117. 07) and CIRMBP-549 (D6-113. 11). Nucleotide sequence accession numbers. The draft genome sequences have been deposited at the European Nucleotide Archive (ENA) under study accession numbers CCCP00000000 (D6-117. 07) and CCCO00000000 (D6-113. 11).
Sequence analysis Protein sequences were retrieved as follow: M. larici-populina (http://genomeportal. jgi-psf. org. html; Duplessis et al. , 2011) , M. lini  (Nemri et al, 2014) , Puccinia striiformis f sp tritici and Puccinia graminis f sp tritici  (Cantu et al, 2013) , N. benthamiana (http://solgenomics. net/;  Bombarely et al. , 2012)  and P. trichocarpa (http://www. phytozome. net/poplar; Tuskan et al. , 2006). Protein sequence analyses were performed with ClustalX and Jalview softwares. Protein targeting sequences were predicted with SignalP 3. 0 (http://www. cbs. dtu. dk/services/SignalP-3. 0/), TargetP 1. 1 (http://www. cbs. dtu. dk/services/TargetP/), ChloroP 1. 1 (http://www. cbs. dtu. dk/services/ChloroP/) and Wolf P Sort (http://psort. hgc. jp/). Protein parameters were calculated with Protparam (http://web.
ACKNOWLEDGMENTS We thank  Y. Dagdas  ( TSL, Norwich, UK ) and  T. Bozkurt  ( Imperial College, London, UK ) for critical reading of an early version of that manuscript. We thank  P. Dodds  ( CSIRO, Canberra, Australia ) for discussions. B. Petre was supported by an  INRA   Contrat Jeune Scientifique (CJS) , by the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills' fellowship  (under grant agreement n°  267196 ) and by the  laboratory of excellence (Labex) ARBRE , through the award of a mobility grant ( 12RW53 ). D. Saunders was supported by  Leverhulme early career fellowship  and a fellowship in computational biology at TGAC, in partnership with the  John Innes Centre , and strategically supported by  BBSRC. C. Lorrain  is supported by an  INRA   CJS  and by the  Labex ARBRE.
The genotypes of the 348 individual insects were analysed by the ADEGENET 1. 4-2 package of the R statistical software  (Jombart 2008)  to ascertain the genetic variability and differentiation, among the C. cosyra populations, including number of alleles per locus (N all ), observed and expected heterozygosity (H obs , H exp ) and deviations from the Hardy-Weinberg equilibrium (HWE). The function genotype_curve of the R package POPR  (Kamvar et al. 2014 ) was used to calculate a genotype accumulation curve (this function randomly sample loci without replacement and count for the number of multilocus genotypes). Linkage disequilibrium was tested for each population across each pair of loci using the log likelihood ratio statistic implemented in GENEPOP 4. 3  (Rousset 2008 ) and assessing significance through Markov-chain randomizations based on 1000 dememorizations, 100 batches, and 5000 iterations per batch.
FreeNA 1. 0  (Chapuis and Estoup 2007)  was used to estimate null allele frequencies (per locus and population) according to  Dempster et al. (1977). Probability values of repeated tests were corrected for Type I errors using the False Discovery Rate (FDR) procedure  (Benjamini and Hochberg 1995). Isolation by distance (IBD) was verified in ADEGENET through Mantel test between Edwards' genetic distances and Euclidean geographic distances (1000 permutations). Principal Component Analysis (PCA) was used to ordinate specimens in multivariate space. Prior to PCA, the SCALEGEN function of ADEGENET was used to centre the data and replace missing genotypes with mean allele frequencies. Specimens from different populations were then ordinated by maximising between-group variances through Discriminant Analysis of Principal Components (DAPC).
Since this method only detects the uppermost level of population structure when different hierarchical levels exist, we further investigated the genetic substructuring of our dataset by following the sequential clustering method described in  Coulon et al. (2008). For this purpose, replicated STRUCTURE runs were performed by (a) dividing the main dataset in subsets of data including individuals assigned to the same cluster, (b) recalculating the optimal K value  (Evanno et al. 2005)  of each subset of data and (c) repeating the STRUCTURE analyses of each subset of data. We set Q = 0. 7 as an arbitrary threshold for cluster assignment and individuals not reaching the threshold were discarded from further replicated runs. For each value of K, five iterations were run for 3 million generations (with 1.
DAPC considered the populations as a priori defined groups and was based on 90 PCs. Stressing the ordination of points by using a prior group only allowed a better resolution of the population from Burundi and, possibly, of that from Tanzania. The latter was clearly separated from the other populations only when excluding 4 STRUCTURE outliers from DAPC (see below). Confidence ellipses of all other populations were largely overlapping (Figure  1 ). The STRUCTURE analysis of the entire dataset (n=345, run 0, Figure  2 ) showed ∆K values  (Evanno et al. 2005)  peaking at K=2 indicating that the main hierarchical level of the population structure is based on 2 genotype groups. A first and smaller group (then separately analysed in run 1) included all specimens from Burundi (n=32) and 22 out of the 26 individuals from Tanzania.
Acknowledgements We wish to thank  Valérie Balmès  and all colleagues and collectors who provided specimens for this study. This research was part of  Research Project  " Development of Molecular and Morphological Diagnostics for Taxonomic and Population Genetic Characterization of African Fruit Fly Pests  ( 16032/R0 )" supported by the  International Atomic Energy Agency (IAEA). We would like to thank  Allen Norrbom  for insightful comments on an earlier version of this ms.
Pressure-Sensitive Position Mat Maertens et al. [29]  developed the Gaitwise system. It is a pressure-sensitive position mat which provides spatio-temporal and relative force information of two complete gait cycles of cows walking over the measurement zone (Figure  1 ). The Gaitwise system automatically measures the gait of the cows after milking (twice a day) without human interference. Gait variables are available a few seconds after the cow has passed the measurement zone. The raw data of this sensor is transformed to 20 basic variables that describe the general gait of cows walking over the measurement zone together with an additional 10 more specific gait variables that are closely related to gait characteristics used in locomotion scoring systems: "stride length", "stride time", "stance time", "step overlap", "abduction" and variables covering asymmetry between left and right limbs: "asymmetry in step width", "asymmetry in step length", "asymmetry in step time", "asymmetry in stance time" and "asymmetry in relative force".
The practical set-ups and methods of those studies that used locomotion scores as reference to test their measured variable are summarized in Table  1  for studies using load cells, in Table  2  for a pressure sensitive position mat; in Table  3  for computer vision, in Table  4  for accelerometers, and in Table  5  for the combination of already available sensor data. In most of these studies, locomotion scores were obtained by trained observers. However, especially the larger studies combining already available sensor data selected their lame cows based on the observation of the herdsman, not necessarily confirmed by a veterinarian. This approach increases the risk of missing mildly lame cows or even considering severely lame cows in the non-lame group. More importantly, the approach in using the locomotion scores as a reference differs considerably between the different studies.
In this manner, three clusters (plant classes) were formed within GC yards and five others within TC yards (Table  4 ). The fecal concentrations of ALK were then corrected for their recovery rates determined under controlled conditions: 0. 29, 0. 40, 0. 40, 0. 48 and 0. 62, respectively, for the ALK C25, C27, C29, C31 and C33  (Jurjanz et al. , 2014). Subsequently, the proportion of each diet ingredientthat is, feed and the plant classes available on the yardwas estimated by a multiple regression (REG procedure, SAS ® version 9. 3, SAS Institute, 2011) of the ALK profile of all possible ingredients on the ALK profile in feces using the following model: ALK i feces ¼ALK i feed + ALK i Pc1 + ALK i Pc2 + ALK i Pc3 + ε i ; corresponding to the proportion of the alkane i (C25 to C33) in the fecal matter, feed or plant classes 1, 2 and 3, as well as the residual error ε of the considered alkane.
Statistical analyses The amounts of ingested feed, plants and soil obtained, along with body weights and feed efficiency, were analyzed using ANOVA (procedure MIXED, SAS ® version 9. 3, SAS Institute, 2011) with the repeated option on the age factor within the considered yard for a given flock. The model was as follows:  [type i × age k × flock j ], e ijkl represent the analyzed variables (intake in g DM/day), the overall mean, the fixed effect of the yard type (i = 2 : TC or GC), the effect of the flock number (k = 1 to 5), the effect of age (j = 2 : 51 or 64 days), the interactions between the effects and the residual error term, respectively. Replications (n = 2) correspond to the two plots of the same flock on the same type of yards. Least square means were compared by a multiple t-test according to Tukey.
Acknowledgments This study was funded by the  French program  PSDR Grand Ouest as a part of the project  AlterAviBio. E. Lamothe, Ph. Rousseau , and the staff of  EASM Le Magneraud  are gratefully acknowledged for their care of experimental animals and assistance with measurements throughout the study, and part of the chemical analyses laboratory. Ch. Grandclaudon,  P. Hartmeyer  from  UR Animal et Fonctionnalités des Produits Animaux (Nancy)  is also acknowledged for the n-alkane and AIA assays, as well as 'InPuzzle' for having post-edited the English style.
These efficiencies were estimated by fitting the (limited) available data to model predictions. Basal endogenous losses are accounted for but with different losses of endogenous protein for gestation and lactation (17. 6 and 9. 8 g/kg DM intake, respectively), but with the same AA composition as for growing pigs. The InraPorc and the NRC models can be parameterized for similar conditions and Figure  5  illustrates the SID Lys requirement during gestation and lactation followed very similar patterns in both models. The average SID Lys during gestation was 3% greater with the InraPorc model than with the NRC model, while the reverse was true during lactation.
Acknowledgments This paper was presented at the  Chinese Swine Industry Symposium  organized jointly by the  Gentech Industries Group  and the  American Society of Animal Science (Shanghai, October   30-31 ,  2014 ). The theses and post-doctoral work of  Roberto Barea ,  Mathieu Gloaguen , and Alberto Conde has contributed to our understanding of AA nutrition and metabolism in pigs and their contribution is greatly acknowledged. Researchers and support staff of the  INRA experimental facilities and laboratories  is acknowledged for their skilled expertise and commitment to our research program on AA nutrition and metabolism. Received: 10 November 2014 Accepted: 3 April 2015.
11. 2012-to 05/2013-to date 16. 11. 2012-to Three vaccination SCoFCAH date (last date campaigns a year reported case 26. 03. 2015) FLI, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Germany; EURL CSF-DB, European database for Classical swine fever; OIE, world organization for animal health; SANCO, European Commission Directorate-General for Health and Consumers; ADNS, European Animal Disease Notification System; SCoFCAH, European Standing Committee on the Food Chain and Animal Health.
The soil characteristics were measured in the 0-30 cm layer. Soils are composed of 63 % sand, 20 % silt and 17 % clay  (Niu et al. , 2010) , with concentrations of soil organic C, N and P of 1. 55, 0. 17 and 0. 03 %, respectively. The C : N : P ratio of soil is about 51. 7 : 5. 7 : 1. Compared to the global database of  Xu et al. (2013) , the soil of the study site is a nutrient-rich soil. Mean bulk density is 1. 31 g cm -3 , and the soil pH is 7. 7. The native vegetation is represented by typical steppe communities, where Stipa krylovii Roshev. , a perennial bunchgrass, dominates. Other common species include Leymus chinensis (Trin) Tzvel, Cleistogenes squarrosa (Trin. ) Keng, Agropyron cristatum (L. ) Gaertner, Artemisia frigid Willd. , Potentilla acaulis L. , and Carex duriuscula CA Mey.
Statistical analysis Data management and statistical analyses were performed using the SPSS software package (SPSS, Chicago, IL, USA). Two-way analysis of covariance (ANCOVA) was used to detect the effects of litter addition and year (sampling time) on soil inorganic N and available P, above-ground and belowground biomass, total biomass, litter, the C, N, P pools and C : N : P stoichiometry of plant, litter and soil, and C and N pools and C : N of soil microbial biomass. Multiple comparisons were also performed to permit separation of effect means using the Duncan test at a significance level of P < 0. 05. ). The soil inorganic N and the available P contents were different each year (P < 0. 01) but no interaction occurred between litter addition and year. Treatments with different letters are significantly different (P < 0.
Acknowledgements. This study was financially supported by " Strategic Priority Research Program   -Climate Change: Carbon Budget and Related Issues " of the  Chinese Academy of Sciences  ( XDA05050407 ), the  National Natural Science Foundation of China  (No. 31370462 ), and  State Key Laboratory of Vegetation  and  Environmental Change, Institute of Botany , the  Chinese Academy of Sciences. We thank  L. H. Li  for providing weather data, and  Z. Q. Pei ,  J. Y. Xia  and  J. L. Zhang  for help with field sampling. We gratefully acknowledge the  Duolun Restoration Ecology Experimentation and Demonstration Station  for help with logistics and access permission to the study site. I. A. Janssens  and  B. Guenet  acknowledge support from the  Flemish National Science Foundation , from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no.
Molecular weight determination Analysis of various samples was performed using size exclusion chromatography (SEC) equipped with a triple detection: multi-angle light scattering (MALS) (Down HELEOS II, Wyatt Technology, Ca, USA), viscometer detector (VD) (Viscostar II, Wyatt Technology, Ca, USA) and differential refractive index (DRI) (RID 10 A Shimadzu, Japan). The SEC system consists of a pump (LC10 Ai Shimadzu, Japan) at a flow rate 0. 5 mL/min and two columns OHPAK SB 804 and 806 HQ. The samples were dissolved in the eluent (LiNO 3 0. 1 mol/L) at 2 g/L. The dissolution was carried out by stirring at 380 rpm for 24 h at room temperature. 3 mL solutions were filtered through membrane 0. 45 microns (regenerated cellulose) before injection. The analyzes were performed by a data processing Zimm  [15]  "order 1" using angles from (from 34.
The data on utilization of ME were adjusted by covariance analysis for similar ME intake between periods and genetic lines. Three piglets were removed from the dataset (2 RFI+ and 1 RFI-), because of euthanasia due to pulmonary edema, underfeeding or malfunctioning of the respiration chamber. The statistical analyses were then performed on 13 animals (six and seven from RFI+ and RFI-line, respectively), measured before and after the CFA injection.