dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS AND DISCUSSION Total protein content was 1. 8 g/100 g in the three baby foods, without significant differences in the samples before and after the different sterilization treatments. Data are expressed for fresh matter taking into account the water content of the baby purees (89-91%) in order to provide a more realistic approximation about the composition of the baby food for consumption. Protein content was in line with that which was expected according to the ingredients of the purees and the Table of Food Composition (Moreiras-Tunio, Carbajal, Cabrera-Forneiro, Cuadrado-Vives, 2013). Considering an average weight of 200 g for a vegetable baby food sample, it would provide around 27-33% of the recommended daily protein intake for children aged 6 months-3 years  (National Research Council, 2005).
Introduction Detection of orthologs is of fundamental importance in many fields of biology, particularly in the annotation of newly sequenced organisms, functional genomics, gene organization in species, evolutionary studies of biological systems, and phylogenomic analyses. Accurate determination of evolutionary relationships between gene (protein) families involving multiple species is of utmost importance for such goals. Starting in 1995 with the first complete genome sequence of a free-living organism, Haemophilus influenzae,  1  genomics has opened a new research era on species evolution. The availability of full genome datasets raised the hope to decipher species relationships in terms of evolution. Only a few years later, the accumulation of complete genome sequences from three phylogenetic domains has revealed the existence of significant evolutionary processes such as gene exchange between species,  2, 3  partial and whole-genome duplication,  4  and gene loss,  5, 6  casting doubt on the established species tree topology.
Thus, it is solely the conservation of the neighboring genes in S 1 and S 2 that may help to hypothesize on the speciation and gene duplication events, and consequently on the nonorthology of (g 21 and g 12a ) and (g 21 and g 12b ). A similar, albeit more complex, example involving two rounds of WGD and speciation events is shown in Ref. 92. Beyond sequence comparisons, synteny, and phylogenybased methods, there are other, less traditional, methods that attempt to improve the prediction of orthologs and their functional analyses. 97 Some of these are based on large-scale analysis of protein-protein interactions and gene coexpression networks. 98, 99 A recent database, IsoBase,  100  resulting from function-oriented ortholog identification, seeks the integration of sequence data and protein-protein interaction networks to help identifying functionally related proteins.
The accumulation of these evolutionary events makes the reliable identification of orthologous genes a major challenge, particularly for distantly related species where traces of similarity are hardly recognizable. Even though the manual detection of orthologs may be efficient for a small number of genes, automatic approaches are needed to deal with the large amount of genome data currently available. However, despite great efforts devoted to the Genomics insiGhts 2016:9 development of orthology detection methods, the situation appears unsettled and to a large extent the quest for orthologs  17  is still an ongoing task in large-scale genome comparisons. The motivations of "The Quest for Orthologs" (http://questfororthologs. org/) consortium that is an open community are to benchmark, to improve accuracy and standardize orthology inference through collaboration, use of shared reference datasets (http://www.
Only a few distantly related species received particular attention concerning this matter, from the human and teleostan fish genomes to the bacteria Escherichia coli and Bacillus subtilis, including the yeast Saccharomyces cerevisiae and the ciliate Paramecium tetraurelia. These efforts confidently allowed the automatic annotation of a number of closely related species, but rapidly have shown their limits when large-scale comparisons have been considered. As an illustration, the rate of erroneous annotations has been measured to be 30% in the UniProt/Swiss-Prot database. 108 axon sampling and species coverage. With the exception of the model organisms mentioned earlier, the accurate selection of sequenced genomes is still a pending question. While a number of scientific communities focus on very specific groups of monophyletic species as for example the 1000 109 and 1001 110 genome projects for Homo sapiens and the plant A rabidopsis thaliana, respectively, the great majority was attached to sequence and characterize distantly related species, from known or unknown taxonomic groups, with the aim of extending our knowledge on the tree of life.
This is the case for example in yeast for the Dikaryome project that aims to extend the known phylogeny to the Dykarya phylum. 111 his bias is important in orthology identification using phylogenetic methods, and obviously penalizes automatic discrimination between true orthologs and horizontally acquired genes. 112 False bacterial hits are favored in the case of eukaryotic genes, and erroneous most similar hits in the case of bacterial genes. Indeed, the biased distribution of proteins toward bacteria and model organisms in current databases artificially increases the chance to find bacterial (rather than eukaryotic) genes as the most similar sequences to the one under study. The reconstruction of syntenic blocks, chromosomal segments in which the order and orientation of the genes are conserved through the evolution of corresponding species, is also limited by the correct taxon sampling of the species under study.
ebi. ac. uk/reference_proteomes/) and orthology-curated databases (http://questfororthologs. org/orthol-ogy_databases and http://eggnog. embl. de/orthobench2) to be considered for ortholog assignments and evaluation have been suggested. Unfortunately, while these sets are useful to show the differences between orthology detection methods, they are insufficient to estimate their respective accuracy with regard to the aforementioned difficulties (including HGT, duplication, loss, and proteins with multidomain ancestry, among others). An optimal standard set should include both closely and more distantly related species sampled from the species tree. The selected species should correspond to the pointed difficulties in detecting orthologs, including gene loss, HGT, duplication, WGD (one or several rounds), and genome reduction.
We do not, however, use the GEOCARB approach to derive Ordovician surface temperature from atmospheric CO 2 , but instead we use the average land surface temperature estimated by the LMDZ atmospheric circulation model, which is also used to force the non-vascular vegetation model. In this way estimates of both biotic and abiotic weathering are based on the same CO 2 and temperature forcing. Furthermore, GEOCARB III includes the effects of changes in uplift, land surface area and biotic enhancement of weathering. For uplift, we use the GEOCARB III value for the late Ordovician. The change in land surface area is derived from our forcing data to be consistent with the estimate of biotic weathering. GEOCARB III assumes that the lack of large vascular land plants before the Devonian reduced the global weathering rate to a fraction of 0.
3 ). Code availability. The non-vascular vegetation model used in this study is integrated into an interface for parallel computing, which was developed at the Max Planck Institute for Biogeochemistry, Jena, Germany. In addition to the model, a post-processing script is necessary to derive the results presented in this study from the model output. The model, the interface and the script are freely available as long as the copyright holders and a disclaimer are distributed along with the code in source or binary form. The code is available from the corresponding author upon request.
Acknowledgements The  Bolin Centre for Climate Research  is thanked for financial support. The  Max Planck Institute for Biogeochemistry  provided computational resources and the  Laboratoire des Sciences du Climat et de l'Environnement  provided data resources. P. P. acknowledges funding from the  European Union   FP7-ENV  project  PAGE21  under contract number  GA282700. T. M. L. was supported by the  Leverhulme Trust  ( RPG-2013-106 ) and by a  Royal Society Wolfson Research Merit Award. A. P. and Y. D. thank the  CEA/CCRT  for providing access to the HPC resources of TGCC under the allocation 2015012212 made by  GENCI. B. W. gratefully acknowledges support by the  Max Planck Society  ( Nobel Laureate Fellowship ) and the  German Research Foundation  (project  WE2393/2 ). The authors thank four anonymous reviewers for their thorough and helpful comments.
Identification of Pl. halstedii putative RXLR and CRN effectors We identified 54 putative effector genes (Fig  1 ), hereafter named 'effectors', with 27 RXLR and 27 CRN listed on the Web page (S1 Table, Plasmopara halstedii effector polymorphism). Genomic sequences were found for 53 out of the 54 effectors in the seven representative pathotypes (100, 300, 304, 334, 700, 703 and 710), and PhRXLR41 was found in only three pathotypes (100, 300, 334). Of the 27 RXLR-type effectors, 11 have the RXLR consensus motif and three present an alternative RXLQ motif, previously shown as a functional translocation motif in Workflow of the study. In grey, the procedure used to select candidate effector genes. Transcriptomic data from sunflower leaves infected by Pl. halstedii or isolated zoospores were analyzed by PSI-tBLASTn using the annotated sequences that were available in NCBI in March 2010 as models for RXLR and CRN effectors.
Five CRN present a LXLYLAK domain followed, by the HVLVVVP motif for three of them. Other CRN-type effectors have variant motifs (S1 Table, Plasmopara halstedii effector polymorphism). Presence of conserved effector motifs found in effectors from other oomycete species, and/or high PSI-tBLASTn values against the oomycete effector database (43 genes show e-values smaller than 10 -8 , S1 Table, Plasmopara halstedii effector polymorphism) suggest that these genes are reliable effector candidates. Pl. halstedii effector genes are more diverse at the non-synonymous level than random non-effector genes While 18 effector genes presented no genomic polymorphism in the set of seven pathotypes (and in the set of 3 pathotypes for PhRXLR41), 36 effector genes (14 RXLR and 22 CRN) exhibited at least one SNP (S1 Table, Plasmopara halstedii effector polymorphism).
Discussion The rapid modification of Pl. halstedii virulence profile illustrated by the occurrence of new pathotypes breaking down Pl resistance genes is an important issue for the cultivation of sunflower crop. Use of housekeeping genes to design molecular markers, with the goal of characterizing different pathotypes, was hampered by recent Pl. halstedii diversification. Here, we focused on effector genes, which have been characterized as key determinants of pathogen virulence in model oomycetes, to set up molecular markers necessary to improve Pl. halstedii pathotype determination, which in turn will facilitate the epidemiology survey of this field disease. How to select Pl. halstedii reliable effector genes from in silico data? A preliminary work on a smaller Pl. halstedii transcriptome database, called HP  [32]  led to the discovery of 5 RXLR and 15 CRN cDNA clusters, from which 7 CRN were polymorphic.
Compared to this smaller database which counted at least 425 Pl. halstedii genes, our study was done on a larger database allowing the identification of 54 putative effector genes. In our current analysis, 13 of the 20 previously described effectors were selected  [32]. This ratio might result from the drastic selection of genomic sequences in the seven pathotypes and effector screen we included to our selection procedure. Compared to effector repertoires published for model oomycetes, the present effector list is likely to be not exhaustive, but was sufficient to find polymorphic effectors among sequenced Pl. halstedii pathotypes. To minimize exclusion of false negative effectors, we selected effector sequences that fulfilled at least two of the three criteria used in this study (see Materials and methods section), thereby allowing to a certain degree of freedom of choice.
Identification of expressed Pl. halstedii putative RXLR and CRN effectors and their corresponding genomic sequences in seven pathotypes The adopted strategy to find putative effectors is summarized in Fig 1. A PSI-tBLASTn analysis  [46]  was performed starting from transcriptomic data, in order to work on genes expressed during the interaction and to avoid pseudogenes that could have been selected from genomic data. PSI-tBLASTn search was realized on cDNA databases including Pl. halstedii and H. annuus sequences using an annotated oomycete effector gene database generated from Genbank  [32]. cDNA consensus sequences present in these databases and named Plhalxxxxxx resulted from clustering of ESTs originating from inoculated sunflower and Pl. halstedii germinating spores of the four reference isolates for pathotypes 100, 304, 703, and 710  [32, 47].
To build a subset of Pl. halstedii non-effector genes, we screened the EST database  [32]  for the following four criteria: (i) the cDNA clusters contained reads from the libraries of germinated spores from the pathotypes 100, 304, 703, 710, (ii) they had an INTERPRO annotation, (iii) their hit definition contained the word "Phytophthora" and (iv) cluster length was between 300 and 2300 bp. A total of 2933 EST clusters were obtained, from which 150 were randomly chosen. Out of theses EST clusters, only 125 alignments were obtained when blasted against the seven representative genomic sequences. We checked that no EST in this subset was annotated as effector. The same procedure was used to identify NA and AA polymorphisms between the genomic sequences of a minimum of six out of the seven pathotypes.
Acknowledgments We thank  François Delmotte  and  Sylvie Richart-Cervera  ( INRA SAVE, Bordeaux, France ) for providing DNA from P. halstedii isolates,  Denis Tourvieille de Labrouhe  and his colleagues ( INRA Clermont-Ferrand, France ) for collecting and determining pathotypes,  GetPlage Plateform (Toulouse, France ) for sequencing duties,  Jérôme Gouzy  and  Ludovic Legrand  ( Bioinformatics, LIPM, Toulouse, France ), and  Yann Pecrix  ( LIPM, Toulouse, France ) for his help in formatting the figures.
Cloning of NtDGK5-like coding sequence NtDGK5-like coding sequence was amplified by PCR from cDNA obtained from untreated 7day-old BY-2 cells using high fidelity DNA polymerase (Phire Hot Start, ThermoFisher SCIENTIFIC) and specific primers: forward (F1c85), 5'-ATGGCGAATTCTGAGTCCGA-3'; and reverse (R1c85), 5'-CTAACTGAGACGAGAAACATCGA-3'. Primers were designed according to the sequence of SGN-U439985 (Solanacea Genomic Network database). Resulting PCR products were cloned into pSC-B Amp/Kan vector using the topoisomerase-based StrataClone Blunt PCR cloning kit (Agilent). Seven clones were sequenced among which one was selected for further work. The sequence was deposited in GenBank (accession number KU934207). This cDNA was named NtDGK5-like because its closest Arabidopsis thaliana ortholog is AtDGK5.
Furthermore, amongst the 7 partial tobacco DGK coding sequences that were retrieved, four are present in cluster I, one in cluster II and two in cluster III. Overexpressing a member of the phylogenetic cluster III, NtDGK5-like, does not result in a gain-of-function Among tobacco cluster III members, the full-length cDNA corresponding to the unigene SGN-U439985 (hereafter referred to as NtDGK5-like after its closest Arabidopsis thaliana ortholog) was cloned and deposited in GenBank (Accession number KU934207). The isolated NtDGK5-like cDNA corresponds to a 1482 bp-long coding sequence that encodes a 493amino acid protein displaying a molecular weight of 55. 24 kDa and a theoretical pI of 6. 75, with no predicted signal peptide. The NtDGK5-like gene is constitutively expressed in BY-2 cells but its expression is significantly up-regulated in response to cryptogein (Fig.
2002 ) reveal a simpler organization that falls into three evolutionary phyla  (Vaultier et al. 2008). In this work, we focused our efforts on DGKs of phylogenetic cluster III. Two members of this cluster have been either biochemically or functionally characterized. The rice DGK1 was stably expressed in tobacco plants and found to confer enhanced disease resistance to tobacco mosaic virus (TMV) and to the oomycete Phytophthora infestans  (Zhang et al. 2008). Two distinct spliced mRNA variants were reported for the tomato LeDGK1/CBK1-encoded gene and this transcriptional regulatory mechanism was demonstrated to regulate protein docking at the plasma membrane  (Snedden & Blumwald 2000)  In addition, the AtDGK5 gene expression was found to be upregulated in response to flagellin 22, a bacterial MAMP, as well as several avirulent pathogens (GENEVESTIGATOR database,  Hruz et al.
ACKNOWLEDGMENTS This work was supported by the  French National Research Agency (ANR ,  Programme Blanc  PANACEA  NT09_517917 ), the  Bordeaux Metabolome Facility-MetaboHUB  (grant no. ANR-11-INBS-0010  to S. M. ) and platform  Métabolome-Lipidome-Fluxome of Bordeaux  (contribution toward lipid analysis equipment). We would like to thank  Dr. Olivier Lamotte  and  Pr. Nathalie Leborgne-Castel  for their support and stimulating discussions, and  Dr Michael Hodges ,  IPS2 ,  Orsay France  for carefully reading the manuscript. The authors have no conflict of interest to declare. AUTHOR'S CONTRIBUTION: JLC, PGP, JF, ER, CC, DT, TK & KM carried out experiments; FSP, SM, EJ, PGP, JLC & ER designed experiments and discussed results; JLC, PGP & ER wrote the manuscript; SM &  FSP  coordinated the project and retrieved  ANR  funds.
Statistical analyses There was no significant difference between the mass of the left and right eye lens for the sub-sample of animals for which both lenses were available (n  96; paired t-test p  0. 24 at all five drying stages). We therefore included individuals with only one eye lens in our analyses. For individuals with two eye lenses we used the lens with the largest mass in our analyses because the other lens likely had a lower mass due to a problem occurring during the drying process (e. g. cracking). We did not expect differences in lens development between sexes  (Augusteyn 2014)  and therefore pooled data from males and females for all analyses. We first evaluated the functional relationship between eye lens dry mass and age of animals harvested using five non-linear models previously used for describing asymptotic growth: Lord, Richards, monomolecular, Gompertz, and logistic growth models  (Winsor 1932 , Lord 1959 , Richards 1959 , Dudzinski and Mykytowycz 1961 , France et al.
370 g had posterior probabilities of 0. 33 and 0. 67 of being in the yearling and prime-age classes, respectively. A deer with an eye lens dry mass of 0. 360 had posterior probabilities of 0. 63 and 0. 37 of being in the yearling and prime-age classes, respectively. This example indicates how a small change in lens mass (0. 05 g) substantially changes the posterior probabilities in favor of one class or the other due to the high discriminatory power of the function. 97. 5% quantiles of the distribution of the error rates. The same priors for being in a given age class were used during analyses and corresponded to the class proportions for the overall dataset. These priors should therefore represent a typical sample of harvested animals. We performed all analyses using R 3. 0. 2 (< www. r-project.
Computer code and data availability The R script for implementing all models is provided in Supplementary material Appendix 1. All supplements are available online at < www. wildlifebiology. org/appendix/wlb-00185 >. Data associated with this paper ("hog. txt" in Appendix 1) have been deposited in the Dryad Digital Repository: < http:// dx. doi. org/10. 5061/dryad. 0r31r >. (Forsyth et al. 2016).
Acknowledgements -We gratefully acknowledge the assistance of Parapark Co-operative Game Reserve with this study, particularly  R. Mayze , S. and  L. Matthews , and the late  G. Moore. R. Augusteyn  and  M. Lindeman  helped extract, dry, and weigh eye lenses. We thank  C. Calenge  for assisting with the ratio-of-uniform method, and  R. Augusteyn ,  J. Birtles  and  D. Ramsey  for comments on the manuscript.
b. Alternative method The alternative method consisted of qPCR performed with commercial kits. Three different commercial kits (arbitrarily named Kit 1, Kit 2, and Kit 3) were tested for B. bruxellensis DNA extraction and amplification. DNA extraction and amplification protocols were performed according to the manufacturers' instructions. Standard curves were used in two kits, whereas one kit allowed direct quantification based on the amplification of reference DNA. Each laboratory used two kits, i. e. each sample was analyzed by the same kit in triplicate by two different laboratories. Amplification reactions were performed on a CFX96 real-time PCR system (Bio-Rad) for two laboratories and on an iCycler IQ5 system (Bio-Rad) for the third. Results were analyzed using Bio-Rad CFX Manager® software.
Validation criteria and statistical results Counts obtained using the alternative method are presented in log 10 units. The validation criteria and statistical results for the Côtes du Rhône, Burgundy, and Bordeaux wines are shown in Supplementary data 1, Supplementary data 2 and Supplementary data 3, respectively. Repeatability was calculated for each wine according to population level and kit. The mean repeatability was 0. 257, 0. 183, and 0. 390 log 10 cells/mL for Kits 1, 2, and 3, respectively. Kit 1 underestimated the four population levels in Burgundy wine by a mean of 1. 2 log 10 cells/mL. The population levels determined by Kit 1 red wines were overestimated by 0. 5 and 1. 1 log 10 cells/mL relative to the reference method. Kit 2 underestimated all population levels in the Burgundy and Côtes du Rhône wines (bias of -2.
The occurrence of six CMG species in Madagascar Besides PCR typing data on a large number of infected Madagascan cassava samples  [22] , prior to this study very little sequence information was available for CMGs circulating on the island. The information available in public sequence databases included only three complete CMG genome component sequences: the DNA-A component of a SACMV [GenBank:AJ422132]  [35] , and the DNA-A and DNA-B components of a novel recombinant begomovirus, CMMGV  [15]. As part of this study, a total of 279 full DNA-A and 117 full DNA-B components were cloned and sequenced from 173 symptomatic Madagascan cassava leaves sampled during an epidemiological survey conducted between 2009 and 2011  [22]  as well as 91 additional samples collected on the island between 2005 and 2008.
In contrast to ACMV and SACMV isolates, the 43 Madagascan EACMKV isolates (accounting for 15 % of the sampled DNA-A sequences) and the 13 Madagascan EACMCV isolates (accounting for 4. 6 % of the sampled DNA-A sequences) were more diverse, with mean DNA-A identities of 94. 4 % (all >84. 3 %) and 96. 3 % (all >93. 1 %), respectively (Additional file 2: Figure  S2 ). Interestingly, one of the EACMKV isolates from the Diana region of Madagascar [GenBank:KJ888083] was <91 % identical to all other presently available isolates of this species but one (KJ888079 with 91. 0 % identity). Whereas this isolate cannot be considered a member of a new species, it is the only one representing a novel strain that is possibly unique to the SWIO islands. With only one sequence represented in our large sample, it is, however, impossible to determine whether this novel strain has had, or will in the future have, any epidemiological importance.