dataesr/scientific-paragraphs-categorization huggingface dataset:
The data from the full magnetic field setting (FF) and the reversed full magnetic field setting (RFF) were also analyzed separately. The results are generally consistent within the statistical errors. The difference between the FF and RFF results from the combined FF and RFF data set, whichever is larger, is taken as part of the systematic uncertainties. Table  I  shows the sources and magnitudes of the systematic uncertainties on the charge multiplicity asymmetry correlation measurements for two selected centrality bins. The systematic uncertainties from the various sources are added in quadrature to yield the total systematic uncertainties. The systematic uncertainties are taken to be symmetric between the positive and negative sides. The total systematic uncertainties are shown in the shaded areas in Fig.
φ-Efficiency Correction The TPC sector boundaries could create "dynamical" event-by-event correlations even though the EP angles are uniform. Specifically, the east TPC sector with systematically lower efficiency introduces a dynamical eventby-event correlation (see below). To reduce this sort of fake dynamical correlation, the sector boundary effects were corrected for by the φ-dependent correction 1/ǫ(φ). This correction is applied on average over the entire dataset. In principle, any time variation in ǫ(φ) would introduce dynamical event-by-event correlations if a single set of correction factors is applied. However, it was found that ǫ(φ) is stable over the entire run period of the present data. Figure  18  shows the charge asymmetry correlations A 2 +,UD , A 2 -,UD and A + A -UD before and after the φ-efficiency corrections.
Acknowledgments We thank the  RHIC Operations Group  and  RCF at BNL , the  NERSC Center at LBNL  and the  Open Science Grid consortium  for providing resources and support. This work was supported in part by the  Offices of NP  and  HEP  within the  U. S. DOE Office of Science , the  U. S. NSF , the  Sloan Foundation ,  CNRS/IN2P3 ,  FAPESP CNPq of Brazil, Ministry of Ed. and Sci. of the Russian Federation, NNSFC ,  CAS ,  MoST , and  MoE of China, GA  and  MSMT of the Czech Republic, FOM  and  NWO of the Netherlands, DAE ,  DST , and  CSIR of India, Polish Ministry of Sci. and  Higher Ed. ,  National Research Foundation  ( NRF-2012004024 ),  Ministry of Sci. , Ed. and Sports of the Rep. of Croatia, and  RosAtom of Russia.
Statistical analysis Means, standard deviations and p-values were calculated with the GraphPad InStat3 software (San Diego, CA, USA). All bar graphs represent means as per at least three independent experiments for cytokine detection and at least five independent experiments for cell cycle studies. Error bars represent standard deviations. To assess statistical differences, means were compared through a one-way ANOVA test followed by a Tukey post-test for multiple comparison significance test between the analyzed groups. When specified, for single comparison of a population with the control cell group, the ANOVA test was followed by a Dunnett post-test. * stands for p < 0. 05, ** for p < 0. 01 and *** for p < 0. 001.
Acknowledgments This research was supported by the  Agence Nationale de Recherches sur le Sida  et les hépatites virales ( ANRS-Lymphovir sub-study ),  Sidaction , the  Centre National de Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  University Joseph Fourier , the  Grenoble University Hospital. We thank  Evelyne Manet ,  Henri Gruffat  ( ENS-Lyon, France ) and  Xavier Ronot  ( FRE AGIM CNRS-UJF 3405, Grenoble, France ) for helpful advice and discussions. We thank  Rose-Laure Revel-Goyet ,  Françoise Lacroix  and  Jean-Philippe Kleman  ( Institut de Biologie Structurale, Grenoble ) for the support and access to the Cell imaging Platform. This work used the platforms of the Grenoble Instruct center (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
g. , Oncorhynchus mykiss,  Ardren and Kapuscinski (2003) ,  Araki et al. (2007)  and Salmo trutta,  Serbezov et al. (2012a) ), demonstrating the feasibility of such a procedure. Moreover, in situations where all the potential anadromous Atlantic salmon breeders have been sampled, mature male parr presence and fertilization success can be deduced from pedigree reconstructions  (Saura et al. 2008; Richard et al. 2013 ). Rather than Ne, which corresponds to the effective population size over a generation, the effective number of breeders for a given reproductive year, Nb, can be inferred from such pedigree reconstitution  (Waples 2005; Palstra and Fraser 2012). Here, using a recently published dataset  (Richard et al. 2013) , we document the contributions of mature male parr on the effective population size, genetic diversity and inbreeding in a medium size Atlantic salmon population.
An exhaustive pedigree reconstruction of this North American Atlantic salmon population was achieved by assigning parentage, using 12 microsatellite markers, of 2548 fry collected in 2010 to 144 anadromous females and 101 anadromous males that returned to the river in 2009 and to 462 mature male parr for which genotypes were reconstructed using the Colony software (see  Methods and Results). In this study, we first quantify the contributions of mature male parr to the effective number of breeders (Nb) using both demographic and genetic estimate of Nb, respectively, based on variance in reproductive success among breeders and on linkage disequilibrium among loci. Second, we empirically compare the sensitivity of both methods used to estimate Nb. Third, we quantify the bias in the estimate of Nb/Nc resulting from neglecting the spawning participation of mature male parr to breeding in the study population.
Nb ¼ 4 Â Nb½male Â Nb½female Nb½male þ Nb½female ð3Þ To investigate further the contribution of mature male parr individuals on Nb, we estimated Nb (referred as Demo(  1 )) for two different datasets corresponding to parents-fry assignments implicating (i) anadromous breeders and (ii) anadromous and mature male parr breeders. Mature male parr and anadromous males are likely to compete for egg fertilization  (Myers and Hutchings 1987). Therefore, estimates of Nb based on only anadromous males could be biased due to an underestimation of females' reproductive success since their eggs has been partly fertilized by mature male parr. We thus performed a second demographic estimate of Nb, referred as Demo(2), using the first aforementioned scenario Demo(1) but simulating the possibility that fry fathered by mature male parr could have been fathered by anadromous males.
To do so, we replaced the mature male parr by anadromous males in the parentage results, proportionally to the reproductive success of anadromous males with each female. To consider that some males may have equal reproductive success, this replacement procedure was run 1000 times in silico. We then estimated Nb for this new parentage file using the aforementioned equations. Genetic estimates of the effect of mature male parr on the effective number of breeders (Nb) We used the LDNe program  (Waples and Do 2008)  to estimate Nb (referred as Nb LDNe) and the contribution of anadromous males and mature male parr individuals to Nb and. We excluded alleles with frequencies below 0. 01 and estimated 95% confidence intervals using the parametric test implemented in LDNe.
Estimates of the sensitivity of Nb estimates to the number of fry sampled To investigate the influence of the number of fry sampled on the demographic estimate of Nb, we estimated Nb (Nb Demo(1)) for the two above-mentioned datasets, subsampling from 100 to 2500 progeny by steps of 100 progeny, with 1000 subsamples for each step and without replacing individuals. To investigate the influence of the number of fry sampled on the genetic estimate of Nb (Nb LDNe), we ran the software on random subsets of 100 to 2500 fry (with increments of 100 progeny) resampled 100 times without replacing individuals.
Similar trends were observed for datasets including only anadromous salmon or both anadromous fish and mature male parr. Estimate of the effect of mature male parr on genetic diversity among progeny Figure  4  illustrates a higher number of alleles among fry produced by pairs of anadromous females mature male parr than for fry produced by pairs of anadromous fish. It also shows a lower number of alleles among fry produced by anadromous pairs than among fry produced by both anadromous pairs and that produced by anadromous females and mature male parr. For instance, sampling 1000 progeny 100 times, the average number of allele were 227 (SD = 1. 44), 241 (SD = 2. 95), and 248 (SD = 1. 39) when considering progeny assigned to anadromous pairs, anadromous female and mature male parr, and both type of pairs, respectively.
Discussion Our study yields five important insights into the influence of nonanadromous males on key population genetic features in Atlantic salmon. First, it empirically illustrates the substantial increase in precision obtained for both demographic and linkage disequilibrium estimates of Nb with the number of samples used. Second, we found that mature male parr increased male Nb and population Nb by 3. 07 and 1. 79 times, respectively. Third, Nb/Nc was overestimated by at least 2. 6 times when mature male parr were overlooked. Fourth, mature male parr significantly increased the number of alleles found among the progeny. Finally, mature male parr decreased the average relatedness among breeders. These results have consequential evolutionary and conservation implications. Comparisons between demographic and genetic estimates of the effective number of breeders (Nb) While demographic and genetic methods used to estimate Nb technically differ, the Nb estimates calculated with these were quite similar, illustrating the strength of these approaches when applied to large datasets.
Our results are in line with the study of  Serbezov et al. (2012b)  showing modest differences only between genetic (Nb = 53) and demographic (Nb = 40) Nb estimates in a small Salmo trutta population. Hoehn et al. (2012)  also found comparable estimates of Nb using demographic and genetic estimates in a gecko species. Such comparisons of demographic and genetic estimates, however, remain relatively rare because the demographic method requires collecting and genotyping all or most of the potential parents in a population or otherwise having direct observations on the number and sex of parents and their reproductive success. Although both genetic and demographic methods yielded similar estimates of Nb when applied to the entire dataset, the demographic method was very sensitive to the number of progeny used to estimate reproductive success, contrasting with the robustness of the genetic method.
Acknowledgements We thank  E. Valiquette ,  J. Larivi  ere,  C. Baltazar ,  J. B edard ,  E. Henry ,  M. Racine ,  G. Gravel ,  M. Duchesne ,  M. Talbot ,  N. Harnois ,  D. Fournier ,  V. Cauchon  and  W. Cayer-Blais  for their field assistance. We are grateful to  Y. Demers  and to  A. Dussault  and to all the staff at the CGRSE. We also thank  R. Waples  for his advices regarding estimates of effective population size using the linkage disequilibrium method. We thank  J. S. Moore  and  T. Gosselin  for insightful discussions about puny males and alternative sexual practices. We are grateful to  A. Dalziel  for kind and talented linguistic adjustments to the manuscript. We finally thank five anonymous referees and the associate editor  Dr. C. Eizaguirre. This project was funded by a  Natural Sciences and Engineering Research Council of Canada  strategic project grant to L.
RNA LABELING AND MICROARRAY We have developed a DNA microarray specific to S. xylosus C2a based on an Agilent technology microarray. This array has 19,805 probes targeting the whole S. xylosus C2a genome. On average there are 6-10 probes per gene. A complete description of the array is available at the NCBI Gene Expression Omnibus (GEO) database under platform accession number GPL19201. RNA from each sample was reverse transcribed to cDNA with SuperScript Reverse Transcriptase according to the manufacturer's instructions (Invitrogen). The RNA-cDNA hybrids were then digested using RNAse H (Invitrogen). The cDNA was labeled with Cy3 dCTP or Cy5 dCTP using the Bioprime DNA kit according to the manufacturer's instructions (Invitrogen). Unincorporated Cy-dye was removed using a CentriSep column (Applied Biosystems, Warrington, United Kingdom) and dye swap experiments were performed.
MICROARRAY DATA ANALYSIS AND STATISTICAL TREATMENT Microarrays were analyzed by the Feature extraction software  (Agilent, version 9. 1. 3. 1)  in 3 steps: localization of the spots, segmentation of the pixels and extraction of qualitative and quantitative data. These data were filtered to remove spots that were saturated or of poor quality. Within each array, Cy3/Cy5 log-ratios were normalized to correct for the mean spot intensitydependent dye effect. Significant differences in the probe set intensities between the two conditions were identified using a linear model with an empirical Bayes method using all information probes to moderate the standard errors of the estimated logfold changes  (Smith, 2004). The probabilities were corrected by the Benjamini-Hochberg procedure in order to control the false-discovery rate (FDR) with a p-value cut-off of 0.
ACKNOWLEDGMENTS Aurore Vermassen  is a PhD Research Fellow funded by the  French "Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche (M. E. N. E. S. R. ). "The microarray development was financially supported by the  ANR  project  " Genoferment "  ANR-05-PNRA-020. We are grateful to  Pierre Micheau  for his assistance in data organization and to  Souad Christieans  ( ADIV Viande Performances ) for her assistance in nitrate-nitrite measurements in meat. We would like to thank  Jean-Paul Chacornac  for valuable technical assistance with meat inoculation and sampling. The authors are grateful to  David Marsh  for correcting our English.
Isotopic Composition Analysis by Laser Absorption Spectroscopy According to the Beer-Lambert law of linear absorption, the absorbance A(v) can be related to the transmitted light I(v) and incident intensity I 0 (v) intensities: A(v) = ln (I 0 (v)/I(v)) = Cσ(v)L ( 1 ) where C is the number density of absorbing species (in mol/cm 3 ), σ(v) is the frequency-dependent absorption cross section (in cm 2 /mol), and L is the optical absorption path length (in cm). The integrated absorbance A I (in cm -1 ) can be written as:        n T CLS dv v CL dv v I v I dv v A A I / ) ( ) ( )) ( / )) ( ln( ) ( 0  (2) with S(T) the temperature-dependent molecular line absorption intensity in cm -1 /(mol• cm -2 ) and n the fractional isotope abundance; both can be found in the HITRAN database  [29]. The dependence of the absorption line intensity S(T) on the temperature T can be described as follows  [13] :                          0 0 2 / 3 0 0 1 1 exp ) ( ) ( T T k hcE T T T S T S ( 3 ) with T 0 = 296 K, E 0 the lower level energy expressed in cm -1 , h the Planck constant (J s), c the speed of light in vacuum (cm/s) and k the Boltzmann constant (J/K).
The first part (~8%) was reflected by a beam splitter (CaF 2 ) and directed to a homemade Fabry-Perot etalon for spectral metrology. The frequency scale was linearized by means of the interference fringes produced by the etalon consisting of two air-separated uncoated CaF 2 plates with a free spectral range (FSR) of ~0. 0283 cm -1. Positions of the H 2 O vapor absorption lines provided by the HITRAN database  [29]  were used as absolute frequency reference for laser frequency calibration. The main part of the laser beam was coupled into a Herriott cell with an absorption path of 20 m in a 36-pass configuration. The emerging absorption signal was focused with a 50 mm CaF 2 lens F2 onto a LN 2 cooled HgCdTe detector (J15D22-M204-S01M-60). A home-built bridge circuit was employed to realize DC coupling of the HgCdTe detector to a low noise preamplifier (Model 5113, EG&G, Albuquerque, NM, USA).
The signal background (corresponding to -laser off‖), determined by the dark current level of the detector, was found to be fluctuating by about 1% over a 1 h time interval. This probably was due to variation of the detector temperature or its DC power supply. In order to correctly retrieve absorption spectra, a beam shutter (Thorlabs, SH05) was placed before the detector for background level acquisition at the beginning of each absorption spectral scan. The laser frequency was periodically scanned at a rate of 10 Hz across the absorption lines of the water isotopologues H 16 OH, H 17 OH, H 18 OH, and HDO by means of a triangular wave voltage. The output signal from the preamplifier was digitized with a laptop using a 16-bit analogue/digital data acquisition card (DAQ Card-6036E, National Instruments, (Austin, TX, USA) controlled with a Lab Windows-based program.
Acknowledgments This work is mainly supported by the  IRENI program  of the  Ré gion Nord-Pas de Calais. The support of the  Groupement de Recherche International SAMIA between CNRS (France) ,  RFBR (Russia)  and  CAS (China)  is acknowledged. This research was supported by  National Natural Science Foundation of China  (No. 41265011 ),  International Cooperation of Jiangxi Province of China  (No. 20132BDH80006 ) and  Natural Science Foundation of Jiangxi Province, China  (No. 20114BAB212008 ). We acknowledge helpful discussions with  Peter Werle  and  Frank Tittel  on signal processing and the Kalman filtering technique and with Erik Kerstel on isotope issues. The authors thank  Francis Leveugle  for prompt technical help.
Data sources The domain compositions of the proteins of all analyzed bacterial genomes and the superfamily functional annotations were retrieved from the SUPERFAMILY database (21) (release 1. 75 27/1/2013), which contains protein domain superfamily assignments using the SCOP  (22)  structural classification of proteins, for all protein sequences in completed genomes. We considered the 1065 bacterial genome species in the data set, and excluded the strains, as their presence would bias the abundance profiles. We also analyzed separately a set of 225 unicellular eukaryotes merged with data from 62 higher eukaryotes for which the domain assignment of the longest transcript per gene (excluding splicing variants) were available from the SUPER-FAMILY database. For the analysis of functional annotations, we considered the function annotation of SCOP domain superfamilies, which is a scheme of 50 detailed functional categories, mapped to seven more general function categories, developed by C.
Vogel  (23). Horizontal transfers inferred by the following three independent methods were considered. (i) The Horizontal Gene Transfer Database (HGT-DB (24)), which scores genes that differ statistically from the rest of the genome in G+C content and/or amino-acid usage. (ii) The Dark-Horse database  (25) , which scores phylogenetically atypical genes on a genome assigning a 'lineage probability index' (LPI); we considered the set genes having scores higher than the threshold LPI > 0. 6 and excluded matches with reciprocal LPI scores <0. 75. (iii) The data set from  (13) , which detected, using phylogenetic methods, gene-family expansion by both horizontal transfer and duplication. For each data set, the analysis was restricted to the set of genes present in the SUPERFAMILY database. The final data set (i) contains a total of 549 genomes and 51 658 domains, data set (ii) a total of 348 genomes and 21 004 domains, and data set (iii) a total of 20 genomes and 1864 domains.
The range of behaviors predicted by the model is found in empirical data The model contains a number of radical assumptions, but guides the analysis of the empirical gene-family abundance distributions. As we will see, the distributions predicted by the model are present in a relevant fraction of the empirical families and correspond very well to their expected biological and evolutionary features. We have considered data of domain assignments relative to 1065 sequenced bacteria of different species from the SUPERFAMILY database, which provides a coarsegrained definition of a domain family/superfamily, and thus allows to obtain well-sampled per-family abundances. We performed the main analyses on a total of 1530 superfamilies, and verified that the results were consistent at the family taxonomy level.
Evolutionary behavior and gene-family abundance profile The model suggests that different values of Q f should also correspond to different rates of cross-species HGTs and duplications/intra-species HGTs. To obtain a direct proof that abundance fluctuations are connected with rates of fixed HGTs and gene duplications, we employed four bioinformatics data sets that measured these quantities on a large set of genomes, and compared the amounts of scored transfers and duplication with the index Q f. We converted the gene-based data into domains using the simple criterion that all domains belonging to transferred or duplicate genes are scored as transferred or duplicate domains. The first data set, HGT-DB  (24) , scores putative horizontal transfers on a set of 959 species' genomes using GC content.
This is in line with the model predictions (considering that intraspecies transfers and duplications are not distinguished by the model). Additionally, we repeated the analysis considering data from the DarkHorse database  (25) , which scores horizontal transfers on genome-wide basis by a comple-mentary approach, based on phylogenetic information from the NCBI taxonomy database  (36) , and the results are very robust (Supplementary Figure  S6 ). Third, we tested the correlation of Q f with the enrichment of a family on plasmids as a proxy for the tendency of the family members to be found on 'mobile elements'. To perform this analysis, we used the domain assignments of the NCBI plasmid sequence collection. We found that families with Q f 0 tend to be more enriched on plasmids, while families with increasingly larger positive values of Q f are increasingly underrepresented (Figure  5 ), in line with the idea that abundance fluctuations are related to family 'mobility' across genomes.
For each genome pair in the data set, we thus obtained three distances, which we compared to a reference distance obtained from a 16S ribosomal RNA phylogenetic tree  (38). While family-based metrics are usually a good signal of phylogeny, HGTs should 'scramble' the signal from vertical descent. In line with this, the genome distances based on Poisson-like and overdispersed fam- (A) The scatter plot is the same as the plot in Figure  2 , but the color and size of each point correspond to the parameter H f , which is an average number of horizontally transferred domains in that family (estimated here using data from the HGT-DB database), normalized, for each genome where family f is found, to the family size. Points with H f = 0 are in gray. Compatibly with the expectation of the model, many horizontal transfers are found for Poisson-like families, i.