dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007)  and aligned with the Greengenes Core reference database  (DeSantis et al. , 2006)  using PyNAST algorithms  (Caporaso et al. , 2010a). A phylogenetic tree was built with FastTree 2. 1. 3. (Price et al. , 2010)  for further comparisons between microbial communities. Within community diversity (α-diversity) was calculated using QIIME default scripts. Even depths of 12,000 and 16,000 sequences per sample were used for calculation of Chao1 richness and Shannon and Simpson diversity indices for the ileal MAM and fecal microbiota of MAP-infected and control, respectively (Figure  2 ). Alpha-diversity indices were also calculated according to the inflammation status (comparison between non-inflamed calves and inflamed onescalves with minimum score of 1 for both macroscopic lesions and histologic scores), dose or age of infection (Table  S1 ).
To compare microbiota composition between samples, β-diversity was measured by calculating phylogenetic-based unweighted UniFrac distances  (Lozupone and Knight, 2005). Principal coordinate analysis (PCoA) was applied on resulting distance matrices to generate two-dimensional plots using default settings of the PRIMER-6 (Figure  3 ;  Warwick and Clarke, 2006). Finally, open source software PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states;  Langille et al. , 2013)  was used to predict functional genes of the classified members of ileal MAM (resulting from reference-based OTU picking against Greengenes database). Predicted genes were then hierarchically clustered and categorized under KEGG  (Kanehisa and Goto, 2000)  orthologs (KOs) and pathways (level 1-3; Table  S2 ).
Statistical Analyses The UNIVARIATE procedure of SAS  (SAS 9. 3, 2012)  was used to test the normality of residuals for α-diversity measurements (Table  S1 ). Non-normally distributed data were subjected to boxcox power transformation and then used to assess effects of MAP infection, using MIXED procedure of SAS. The null hypotheses being tested were: (a) the diversity of the ileal MAM and fecal microbiota of MAP-infected calves (based on detection of at least one positive tissue culture) are similar to control group, (b) the diversity of the ileal MAM and fecal microbiota of calves challenged with either high or low doses of MAP at 5 ages are similar; and (c) the diversity of the ileal MAM and fecal microbiota of inflamed calves (all calves with minimum score of 1 for both macroscopic lesions and histologic inflammation) are similar to non-inflamed ones.
The effect of block (experimental year) was treated as a random factor in all comparisons. When normality was not achieved, GLIMMIX procedure of SAS was employed to fit Poisson or negative binomial distributions using a log-link function. The goodness-of-fit criteria for each distribution was compared using the -2 log likelihood (smaller is  Triangles represent samples belonging to calves that were included in the first year of the infection trial, whereas circles represent samples that belong to the second year. Green and red indicate samples obtained from control and MAP-infected calves, respectively. The impact of MAP infection and sampling year on the clustering pattern of microbial communities were tested using PERMANOVA (implemented in PRIMER-6 software). PERMDIS test was performed to test the null hypothesis of homogeneity of dispersions.
P < 0. 05 were considered significant. better), Akaike information criterion (AIC; smaller is better), and Pearson's Chi-square over degrees of freedom (DF) ratio (closer to 1 is better). All pairwise comparisons among the groups were tested using Tukey's studentized range adjustment. Permutational multivariate analysis of variance (PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance. Department of Statistics, University of Auckland, New Zealand) implemented in Primer6 software was used to detect significant differences between β-diversity measures of microbial communities. Label permutations were used in PERMANOVA to estimate the distribution of test statistics under the null hypothesis that within-group UniFrac distances are not significantly different from between-group distances  (Kuczynski et al.
, 2010). Infection status (infected vs. control) was considered as a fixed factor whereas the effect of block (experimental year) was treated as random factor in all comparisons. Because PERMANOVA analysis makes the implicit assumption (similar to ANOVA) that dispersions are roughly constant across treatments, PERMDISP test (implemented in Primer-6 software) was performed to check the homogeneity of multivariate dispersions within treatments using phylogenetic-based unweighted UniFrac distances of microbial communities (Figure  3 ). The PERMDISP test is analogous to PROC UNIVARIATE used above for testing the homogeneity of residuals prior to fitting in ANOVA models. Statistical analyses regarding relative abundances of bacterial taxa at different phylogenetic levels between MAP-infected and control calves were performed using Linear Discriminant Analysis Effective Size (LEfSe;  Segata et al.
The relative abundances of the genera showing the highest LDA scores (revealed by LEfSe analysis) were entered into a logistic regression model and their ability to discriminate group classification was evaluated using area under the receiver operator characteristic (ROC) curve (Figure  5 ;  Delong et al. , 1988). Finally, the relationship between abundant (>1% of community) ileal MAM and histopathological parameters was explored using non-parametric Spearman's rank correlation coefficient test implemented in PAST software  (Hammer et al. , 2012)  and the resulting correlation matrix was visualized in a heatmap format generated by the corrplot package of R (Corrplot: visualization of a correlation matrix. R package version 02-0. 2010. [http://CRAN]). For each correlation, the correlation coefficient (Spearman's ρ) and P-value were obtained.
Taxonomic Classification of 16S rRNA Gene Sequences Alignment of OTUs at 97% similarity threshold against Greengenes database resulted in identification of 29 and 14 bacterial phyla, and 489 and 244 bacterial genera within the ileal MAM and fecal microbiota, respectively. While majority of OTUs were identified at genus (g. ) or species levels, some were only classified at the phylum (p. ), class (c. ), order (o. ), or family (f. ). Proportions of the abundant (>1% of community) bacterial phyla and genera have been presented in Table  1. Proportions of the most abundant representative OTUs (>1% of community) in MAP-infected vs. control calves, regardless of significance, were depicted in Figure  S1.
Alterations in Functional Properties of Microbiota in Response to MAP-Infection The reference-based OTU picking resulted in 80. 03% of the sequencing reads being mapped to the Greengenes database. Consequently, a high proportion of MAM were utilized for metagenomic predictions. Major KEGG pathways including valine, leucine, and isoleucine degradation, as well as glutathione and tryptophan metabolism exhibited more than 2. 5 log-fold increase within the ileal MAM of control calves, whereas the proportion of fatty acid, lysine, thiamine, and histidine metabolism pathways were enriched in the ileal MAM of the MAP-infected group (Figure  7 ).
Transcriptome assembly and automatic annotation All 454-sequencing reads were inspected for low quality reads and 454 adapters that were identified by SSAHA2 software  [23]. A reference full transcriptome was then built using C. arabica reads originating from the present project and from the Brazilian Coffee Genome Project (BCGP) available in the GenBank public database  [14, 24]. The Sanger and 454 reads were submitted for a trimming pipeline using bdtrimmer software  [25]  that was used to exclude ribosomal, vector, low quality (regions with a PHRED score less than 20) and short sequences (less than 100 bp). All sequences (454 and Sanger reads) were assembled using MIRA software  [26]. The contigs formed by only Sanger reads were discarded from the full transcriptome assembly. The reference full transcriptome was annotated by Blast2GO software version 2.
Digital gene expression analysis The reference full transcriptome was also used to count all 454 reads/libraries individually by parsing the ACE file generated by MIRA software. The number of sequences anchored in each contig (read counts) was subjected to differential expression analysis between the libraries using DEseq  [28]  and EdgeR  [29]  software in the R/Bioconductor package. A unigene was considered as differentially expressed when it was identified in at least one software considering fold-change ≥ 2 (or foldchange ≤ -2) and p-value ≤ 0. 05. The libraries were compared based on (1) differentially expressed genes in IAPAR59 between C (control) and D (drought) conditions (with the calculation of fold-change based on the I59-D/I59-C ratio), (2) differentially expressed genes in Rubi between C and D conditions (RUB-D/RUB-C), (3) differentially expressed genes in the control library between Rubi and IAPAR59 (RUB-C/I59-C) and (4) differentially expressed genes in the drought library between Rubi and IAPAR59 (RUB-D/I59-D).
Selection of candidate genes The comparison of DNA libraries led to the identification of 80 (20 for each library) candidate genes (CGs) that were up-and down-regulated (see Additional file 3: Table  S2 ). For each CG, primer pairs were designed using Primer Express software (Applied Biosystems) and tested of their specificity and efficiency against a mix of ss-DNAs of plagiotropic buds (data not shown). The best primer pairs (n = 20) were used to monitor the expression of corresponding CGs in plagiotropic buds of Rubi and IAPAR59 under control and drought conditions. These genes corresponded to CaAEP1, CaCAB2, CaCHI1, CaCHI2, CaCHI3, CaDLP1, CaELIP3, CaGAS2, CaGRP2, CaH2A, CaHSP3, CaIPS1, CaJAMT1, CaMAS1, CaPP2, CaPSBB, CaSAMT1, CaSDC1, CaSLP1 and CaSTK1 (Table  1 ). This list of CGs was increased by adding other genes such as 14 orphan genes (CaUNK2-CaUNK7, CaUNK9 and CaUNK11-CaUNK17 already described to present differential gene expression profiles in different organs of C.
Data were analysed using SDS 2. 1 software (Applied Biosystems) to determine cycle threshold (Ct) values. The specificity of the PCR products generated for each set of primers was verified by analysing the Tm (dissociation) of amplified products. PCR efficiency (E) was estimated using absolute fluorescence data captured during the exponential phase of amplification of each reaction with the equation E (in %) = (10 (-1/slope) -1) x 100  [34]. Efficiency values were taken into account in all subsequent calculations. Gene expression levels were normalized to expression levels of CaUBQ10 as a constitutive reference. Relative expression was quantified by applying the formula (1 + E) -ΔΔCt where ΔCt target = Ct target gene -Ct reference gene and ΔΔCt = ΔCt target -ΔCt internal calibrator , with the internal reference always being the Rubi-control (RUB-C) sample with relative expression equal to 1.
( c ): orphan genes (n = 3) with expression already been studied in leaves of D T and D S clones of C. canephora conilon  [10, 11, 36]. ( d ): LTP-encoding genes were previously described  [37]  the "Plate-Forme d'Histocytologie et Imagerie Cellulaire Végétale (PHIV platform)" (http://phiv. cirad. fr/) using microscopes belonging to the Montpellier Rio Imaging platform (www. mri. cnrs. fr). The results are expressed as means (μm) of 11 measured values. The data were statistically processed using (1) an analysis of variance computer program (Statistica, StatSoft, Inc. ), and (2) the Student-Newman-Keuls (SNK) mean comparison test  [38]  when the effect of the factor tested was found to be statistically significant. A probability level of P ≤ 0. 05 was considered significant for all the statistical analyses.
Discussion In this study, we obtained 34. 7 Mbp (coverage 6. 5x) of sequences with longer reads (mean of 379. 2 bp) from plagiotropic shoot apices enriched in meristems and primordium leaves of two cultivars of C. arabica under control (irrigation) and drought conditions. These sequences were assembled giving 43,087 clusters (17,719 contigs exclusively from 454-sequencing and 25,368 hybrid contigs formed by 454 and Sanger sequences) with a mean size ≥ 300 bp each. These RNAseq data, which complement those already available in public databases for coffee ESTs (407 million ESTs: dbEST release June 2015), can be considered as innovative and relevant in the sense that they were produced from C. arabica tissues (meristems) that have never previously been studied  [39]. The transcriptome annotation by Blast2GO provided information based on the nomenclature and organism of Leaves of IAPAR59 and Rubi cultivars of C.
Values of three technical replications are presented as mean ± SD (bar) origin of genes in the NCBI/NR database, the enzyme family, a functional analysis of proteins from the Inter-Pro database, and metabolic functions, biological processes and cellular location from gene ontology. Our results showed that a large percentage of transcriptome alignment had 36,965 hits with known function (85. 8 %), 1,824 genes with unknown function (4. 2 %) both in the NCBI/NR database, and only 1,515 hits in the Interpro database (3. 5 %), thereby enabling the identification of most genes. With this analysis, we identified 34,857 genes related to Coffea sp. (80. 9 % of the total). We also found 1,383 genes from Solanum sp. , 573 genes from Populus trichocarpa, 482 genes from Vitis vinifera and 156 genes from Arabidopsis sp.
[59]idate genes and corresponding primers used for qPCR experiments (Continued)Gene names were assigned based on the best BLAST hit obtained by comparing the coffee ESTs with public databases. C. canephora means coffee sequences that aligned with the candidate genes using BLASTx searches against NR/NCBI and filtration (http://coffee-genome. org[59]). GenBank (GB: http://blast. ncbi. nlm. nih. gov/Blast. cgi), ATP (http://www. lge. ibi. unicamp. br/cirad/) and SGN (Sol Genomics Network, http:// solgenomics. net/) accession numbers of coffee ESTs are also given, as well as the length of base pairs (bp) of amplicons.
Acknowledgments The authors would like to thank  Dr Aymbiré Francisco Almeida da Fonseca  from the  INCAPER Institute  for kindly providing seeds of the Rubi and IAPAR59 commercial cultivars of C. arabica. The authors are also very grateful to  Drs Antonio Fernando Guerra  and  Omar Cruz Rocha  ( Embrapa Cerrados ) for their assistance during the field trial experiments. The authors wish also to thank  Daphne Goodfellow , as well as  Peter Biggins  and  Cécile Fovet-Rabot  ( CIRAD-DGDRS ) for English revision of the manuscript. Funding PM acknowledges financial support from  CIRAD  ( Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier, France)  and the  CIRAD ATP  project "Analysis of phenotypic plasticity in response to water constraints in perennial plants growing under different field conditions".
The first rains then occurred and the Ψ pd values of drought-stressed plants increased almost to those measured in irrigated plants, illustrating the complete recovery of stressed plants. In 2011, Ψ pd was measured at the peak of the drought (end of dry season). Under drought conditions, both Rubi and IAPAR59 had similar Ψ pd values that were more negative than those measured in 2009, indicating more severe drought stress in 2011 (Fig. 2b). Sequencing, assembly and annotation of the Coffee shoot apex transcriptome The final reference assembly generated a total of 34,743,872 bp (34. 7 Mbp) with coverage of 6. 5x and 43,087 clusters, corresponding to 41,512 contigs and 1,575 singletons. The statistical data for the Sanger and 454 reads are listed in Table 2. Transcriptome annotation by Blast2GO using Non- Redundant protein (NCBI/NR) and InterPro databases resulted in 36,965 transcriptome clusters (85.
8 %) with a known protein function, 1,824 conserved proteins of un- known function (4. 2 %), 1,515 proteins identified by InterPro only (3. 5 %) and 2,783 unidentified proteins (6. 5 % no-hits found). These data are composed of: (1) 17,719 clusters (16,238 contigs and 1,575 singletons) from 454 sequences, exclusively; and (2) 25,368 hybrid clusters that contain 454 reads, and at least one contig from Sanger sequencing (public database). The contigs formed by only Sanger reads were discarded from the full transcriptome assembly. On average, 22. 4 % and 55. 6 % of the total raw data were discarded from Sanger and 454, respectively, due to low quality. After removing the adapters, these reads had a size of 379. 2 bp (on average).
The growth of galaxies is a key problem in understanding the structure and evolution of the universe. Galaxies grow their stellar mass by a combination of star formation and mergers, with a relative importance that is redshift dependent. Theoretical models predict quantitatively different contributions from the two channels; measuring these from the data is a crucial constraint. Exploiting the UltraVISTA catalog and a unique sample of progenitors of local ultra massive galaxies selected with an abundance matching approach, we quantify the role of the two mechanisms from z = 2 to 0. We also compare our results to two independent incarnations of semi-analytic models. At all redshifts, progenitors are found in a variety of environments, ranging from being isolated to having 5-10 companions with mass ratio at least 1:10 within a projected radius of 500 kpc.
At z < 0. 9, ∼45% of progenitors with at least one companion have been found to live in X -ray selected COSMOS groups  (George et al. 2011). No group catalogs are available at higher redshift. In Figure  5  are also overplotted the same quantities for data drawn from the models. The upper panel shows the results for the DLB07 model, the lower panel for the H15 one. At each redshift, we randomly extract the same number of progenitors found in observations at the corresponding redshift, and then we compute the projected distances 1  to define the number of companions within a certain projected radius. We repeat the sampling ten times, to take into account sample variance. We then consider the maximum range spanned by the extractions. Qualitatively, within a large spread, both the DLB07 and H15 models follow the observational trends, at all distances.
ACKNOWLEDGMENTS We thank the referee for her/his comments that helped us to improve the manuscript. B. V. acknowledges the support from the  World Premier International Research Center Initiative (WPI) ,  MEXT, Japan  and the  Kakenhi Grant-in-Aid for Young Scientists (B)( 26870140)  from the  Japan Society for the Promotion of Science (JSPS). D. M. acknowledges the support of the  Research Corporation for Science Advancement 's Cottrell Scholarship. B. M. J. acknowledges support from the  ERC-StG  grant  EGGS-278202. The  Dark Cosmology Centre  is funded by the  DNRF. This study is based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under  ESO  programme  ID 179. A-2005 and on data products produced by TERAPIX and the  Cambridge Astronomy Survey Unit on behalf of the Ul-traVISTA consortium.
Ecosystem characteristics and global catch data To explore whether differences in the average ecosystem impacts across the 12 ecosystem models could be explained by some ecosystem characteristics in the wider large marine ecosystem (LME), we tested a range of ecosystem properties accessed from the Sea Around US Project (SAUP) website (www. seaaroundus. org) for each corresponding LME, including net primary production (NPP), invertebrate catch per unit area, species richness, the number of fisheries, years fished, mean total catch per year fished, sea surface temperature (SST), and LME area. To see whether global catch data explained variation in observed ecosystem impacts, we investigated average invertebrate catches by LME from 2006 to 2010 from the SAUP for the corresponding LMEs (Fig. 1 ).
To determine the temporal change in global invertebrate catch, we obtained invertebrate catches by functional group from 1950 to 2012 from FAO FishStatJ software, using filters for the appropriate functional groups (Fig. 1 ). We used linear regression analysis to evaluate links between LME properties and average ecosystem impacts of invertebrate exploitation at 60% depletion. Next, we evaluated whether ecosystem model characteristics explained variation in the observed ecosystem impacts. To do so, we compared the ecosystem model indicators, model area, the number of trophic groups (Table  S1 ), total ecosystem biomass, ecosystem connectance (proportion of feeding links compared to all possible links), and predatory biomass (TL > 4) with the average ecosystem impact of invertebrate exploitation at 60% depletion for each ecosystem using linear regression analysis.
2008 (Anderson et al. , 2011a, b), b). For example, in the United States, only approximately 42% of the 89 federally managed invertebrate stocks are assessed, compared to 79% of the 388 federally managed finfish stocks (NMFS 2015). Clearly, greater attention is needed for invertebrates within fisheries management agencies. Yet, there are challenges in assessing invertebrate populations due to difficulty in developing age and growth data  (Punt et al. 2013 ) and serial depletion across space  (Berkes et al. 2006; Anderson et al. 2011a,b) , which violates the assumption of most assessment models of spatial homogeneity in fishing mortality rates. Interestingly, our results suggest that on average, MSY targets for invertebrates occur at lower levels of depletion than forage fish. Although some invertebrate groups have high production rates in certain ecosystems, resulting in MSY at higher levels of depletion, for others, MSY occurs at much lower depletion levels, requiring more restrictive management targets.
Acknowledgements 52 Acknowledgements We thank  Boris Worm ,  Daniel Boyce ,  Tony Smith ,  Ivonne Ortiz  and two anonymous reviewers for valuable comments that greatly improved the manuscript. We acknowledge the  Sea Around Us Project  for providing catch data, obtained from seaaroundus. org. We acknowledge  Richard Methot  and  Stacey Miller  from  NOAA , who provided U. S. fisheries stock information from the  SIS database. Key financial support  for this project was provided by the  Lenfest Ocean Program  with a grant to HKL. MC was partially funded by the  European Commission  through the  Marie Curie Career Integration Grant Fellowships   -PCIG10-GA-2011-303534to  the  BIOWEB  project and by the  Spanish National Program Ramon y Cajal. VC acknowledges support from  NSERC ,  Canada. SM gratefully acknowledges the support provided by  Defra  project  M1228  ' Fizzyfish '.
Methods Atomistic calculations. The pipe diffusion barrier calculations used the LAMMPS molecular simulation package  26  with empirical potentials for iron by Gordon et al. 27  and for tungsten by Marinica et al. 28. Dislocation loops were created by inserting a random palette of SIAs on (111) or (100) planes into a perfect lattice before a structural relaxation. Away from a so called 'magic' number of SIAs, loops cannot form perfect hexagons or squares, resulting in a geometrically necessary roughening of the loop perimeter. Possible migration pathways were investigated by selecting at random SIAs on the perimeter of a palette, then translating the selected SIA by an a/2〈111〉, a〈100〉 or a 110 lattice vector to another perimeter position before a further relaxation. As the SIA atoms are already in off-lattice positions, a lattice vector translation also finds an off lattice position.