dataesr/scientific-paragraphs-categorization huggingface dataset:
, unpublished data), Indonesian populations including the Lebbo' and Bajo 9 , and groups from Sumba (Cox, unpublished data), together with CEPH-HGDP data  44  , HUGO Pan-Asian SNP data  45  and data for three Malagasy populations (Mikea, Vezo and Temoro)  9  (Supplementary Table  S5 ). The final high density dataset comprises a subset of the populations in the low density dataset, specifically covering 311,871 SNPs in 820 individuals from 28 populations. Population structure analysis. The low density dataset was analysed using the following approaches. Genetic diversity was described using pairwise F ST distance calculations and Principal Components Analysis using the 'smartpca' algorithm of EIGENSOFT v6. 0. 1  46. The Runs of Homozygosity (ROH) analysis was performed in PLINK v1. 9 from the linkage-disequilibrium-pruned dataset.
ADMIXTURE v1. 23  47  was used to estimate the profile of individual genomic ancestries using maximum likelihood for components K = 2 to K = 20. Ten replicates were run at each value of K with different random seeds, then merged and assessed for clustering quality using CLUMPP  48  , and the cross-validation value was calculated to determine the optimal number of genomic components (here, K = 14). ADMIXTURE and PCA plots were generated with Genesis 49 and the results were confirmed using the high density dataset, to avoid any misinterpretation due to a potential bias driven by the density of SNPs. Gene flow between populations was investigated using two different approaches: i) SNP frequencies using TreeMix v1. 12  50  , with blocks of 200 SNPs to account for linkage disequilibrium and migration edges added sequentially until the model explained 99% of the variance (the TreeMix outputs in Newick format were visualized with MEGA6 51 ); and three-population (f3) statistics  52  , defining the African Yoruba population as an outgroup for the low density dataset; and ii) haplotype sharing using the Refined IBD algorithm of Beagle v.
4. 0  53  visualized with Cytoscape v. 3. 2. 1  54  using the high density dataset to estimate the total number of shared genetic fragments (logarithm of odds ratio > 3) between each pair of individuals. To characterize the Island Southeast Asian ancestry in Malagasy individuals, we discarded estimated African components using PCAdmix  55. First, genome-wide SNP data from Malagasy, Yoruba and Asian samples (represented by the Ma'anyan, the Igorot and the Bajo to cover a range of Asian diversity) of the high density dataset were phased using Beagle v4. 0. The Yoruba and Asian samples comprised 100 randomly selected individuals, and were defined as 'parental' populations compared to the Malagasy 'daughter' population for the purposes of the PCAdmix software. The ancestry of each defined linkage disequilibrium window was estimated by the Viterbi algorithm for each individual and used to mask all potential African SNPs.
Acknowledgements We wish to acknowledge support from the  GenoToul bioinformatics facility of the Genopole Toulouse Midi Pyrénées, France. This research was supported by the  French ANR  via grant  ANR-14-CE31-0013-01  (OCEOADAPTO to F. -X. R. ), grant  ANR-12-PDOC-0037-01  (GENOMIX to D. P. ), as well as the  Region Aquitaine of France  (MAGE to D. P. ), the  French Ministry of Foreign and European Affairs (French Archaeological Mission in Borneo (MAFBO)  to F. -X. R. ), a  Rutherford Fellowship  from the  Royal Society of New Zealand  ( RDF-10-MAU-001  to M. P. C. ) and the  French Embassy in Indonesia through its Cultural and Cooperation Services (Institut Français en Indonésie).
the Pacific and Atlantic  10. A clone of Z. marina was sequenced from the Archipelago Sea, southwest Finland, using a combination of fosmid-ends and whole-genome shotgun (WGS) approaches (Methods, Supplementary Note 2). The 202. 3 Mb Z. marina genome encodes 20,450 protein-coding genes, 86. 6% of which (17,511 genes, Supplementary Note 3. 1) are supported by transcriptome data from leaves, roots and flowers (Extended Data Fig. 1 , Supplementary Notes 3. 2-3. 3 and Supplementary Data 1-3). Genes are located in numerous gene-dense islands separated by stretches of repeat elements accounting for 63% of the non-gapped assembly (Extended Data Fig. 2 , Supplementary Note 3. 1) as compared to only 13% in the only other sequenced alismatid, the freshwater duckweek, Spirodela polyrhiza (Alismatales, Araceae)  11.
A total of 212,101,273 reads (Supplementary Table  2. 1) was assembled using our modified version of Arachne v. 20071016 (ref. 35). Subsequent directed Arachne modules were applied to collapse adjacent heterozygous contigs. The entire assembly was then run through another Arachne process starting at Stage 6 Rebuilder. This produced 15,747 scaffold sequences (30,723 contigs), with a scaffold L50 of 409. 5 Kb, 613 scaffolds larger than 100 Kb, and a total genome size of 237. 5 Mb (Supplementary Table  2. 2). Scaffolds were screened against bacterial proteins, organelle sequences, GenBank NR (nr_prot) and RefSeq protein databases, and removed if found to be a contaminant. Scaffolds consisting of prokaryotes, chloroplast, mitochondria and unanchored rDNA were removed. We also assembled the chloroplast and partial mitochondrial genomes (Supplementary Notes 2.
2 and 2. 3, Supplementary Fig. 2. 1). Additionally, short (<1 Kb) scaffolds or scaffolds containing highly repetitive sequence ( > 95% 24-mers found more than four times in large scaffolds) or alternative haplotypes were also removed. Following repeat analysis and gene prediction, all scaffolds were subjected to a filtering process (based on NCBI nr_prot + NCBI taxonomy database) to eliminate remaining bacterial (and other) contaminants (Supplementary Table  2. 3). Assembly validation was performed using a set of 12 fully sequenced fosmid clones. In 4 of the 12 fosmid clones, full-length alignments were not found due to fragmentation in the region of the fosmid clone. In five of the remaining eight fosmid clones, the alignments were of high quality (<0. 05% bp error). The overall base pair error rate (including marked gap bases) in the fosmid clones that aligned to full length was 0.
marina genome. With respect to masking repeats before gene prediction analysis, a de novo repeat identification was carried out with RepeatModeller (v. open-1. 0. 7; http://www. RepeatMasker. org)  36  to identify repeat boundaries and build consensus models from which potential over represented, non-transposable element, protein-coding genes were removed. RepeatMasker (v. open-4. 0. 0, WUBlast) was used in combination with this custom repeat library to mask the assembly and prepare it for gene prediction with EuGene. Furthermore, in order to perform a qualitative and quantitative analysis of repeats with greater resolution  37  the genome assembly was processed for de novo repeat detection using the TEdenovo pipeline from the REPET package v. 2. 2 (ref. 38); parameters were set to consider repeats with at least five copies.
The consensus sequences generated by TEdenovo were then used as probes for whole genome annotation by the TEannot 39 pipeline from the REPET package v. 2. 2. The consensus repeat sequences were classified using Pastec  40. Comparing the genomic positions of transposable elements (TE) to those of exons from the set of predicted genes enabled us to identify that 909 gene predictions most likely represent TEs and these were filtered from the gene set. The REPET package v. 2. 2 was also used to annotate repetitive elements in the Spirodela polyrhiza genome assembly with the same parameters as for Z. marina. See Supplementary Fig. 3. 1. Transcriptome library preparation, sequencing and assembly. Leaf, root and flower tissues were separately frozen in liquid nitrogen immediately following harvest from either ambient (field collected) or experimental (mesocosm) conditions (Supplementary Note 3.
We used the RSEM pipeline  43  to obtain the contig expression values and removed contigs with FPKM (fragments per kilobase of transcript per million fragments mapped) value <1 and IsoPct (percentage of expression for a given transcript compared with all expression from that Trinity component) < 1. In total, we obtained between 39,000 and 53,000 assembled contigs from each library, and 52,000 contigs from the Finnish clone library (Supplementary Table  3. 4). Prior to mapping the genome sequence and the predicted genes, we used the CD-HIT 44 program (v. 4. 6. 1) to collapse redundant contigs, which resulted in 79,134 low redundant transcript contigs. Differential gene expression analysis. High-quality RNA-seq reads were mapped to the genome assembly v. 2. 1 by TopHat  45. Differential gene expression analysis was performed by the Cufflink pipeline  45  based on the Z.
marina v. 2. 1 gene models by converting the number of aligned reads into FPKM values. Genes with significant expression difference (log 2 > 2) were selected for further investigation by GOstats  46  to perform Gene Ontology (GO) term enrichment analysis with P ≤ 0. 05 (Supplementary Note 3. 3, Supplementary Table  3. 5) MicroRNA analysis. Genomic precursors of known miRNAs were mapped on the Z. marina genome following the procedure described in ref. 47 for the maize genome. miRNA entries from the miRBase database (release 21, 2014) were aligned to the chromosomes of the Z. marina genome. Up to three mismatches were allowed in the alignment, using SeqMap  48. In parallel, novel potential DCL1/ AGO1-dependent miRNAs were enriched by selecting 5′-U 20-22 nt small RNAs from three different sequenced libraries from Z.
Letter reSeArCH Potential miRNA targets were identified in silico using the generic small RNAtranscriptome aligner GSTAr from the CleaveLand package (v. 4)  50. Predicted targets were accepted with an Allen score <4 or a MFE (minimum free energy) ratio ≥ 7. 5. (Supplementary Note 3. 4). Gene prediction. Training of the gene prediction programs started with the collection of high quality ESTs. EST information was used, for example, to train the splice predictor SpliceMachine  51. Detection of conserved splice sites was further investigated by RNA-seq splice junctions (count > 10) to construct a WAM model in EuGene (v. 4. 1)  52. Coding-potential was modelled with an interpolated Markov Model (IMM) constructed from the BLASTX alignments of proteins from the PLAZA v. 2. 5 database  53. An additional protein 'monocot' Markov Model was built based on the protein sequences from Brachypodium, maize and sorghum.
Starting from EST and protein alignments, a set of 215 gene models was manually constructed and curated using the genome browser GenomeView  54. The 215 models were then used as a training set for EuGene in order to optimize the different splice site and coding-potential models, as well as the weights for the extrinsic EST and homology evidence. An overall fitness score of 80. 1% was achieved, which is high enough to obtain reliable results without overfitting. GeneMark  55  and Augustus  56  were separately trained (using the same input data as EuGene) and their predictions were integrated with EuGene using a custom script to evaluate the best gene structure at each locus. All gene models were automatically screened to highlight possible erroneous structures (for example, in-frame stop codons, deviating splice junctions) and manually curated.
Transfer-RNA gene models were predicted by tRNAscan-SE (v. 1. 31)  57  and their structures were verified with Infernal (v. 1. 1rc1, rfam11 covariant model database)  58. For each gene, UTRs were assigned by identifying a set of ESTs and RNA-seq assemblies that uniquely overlapped with it. We subsequently selected the longest mapped transcript on either end of the predicted coding sequence and designated the section outside the coding sequence as the UTR. Finally, all genes were uploaded to the ORCAE platform (http://bioinfor matics. psb. ugent. be/orcae)  59 , enabling all members of the consortium to refine and curate the gene model and assign gene function. A list of protein domains, as well as the derived Gene Ontology (GO) terms and KEGG pathway identifiers were generated using an InterProScan (v.
5. 2. 45)  60  analysis and available in ORCAE. More specifically, gene functional descriptions were added either manually by consortium expert scientists or automatically through sequence homology searches. The automated method relies on the EC (Enzyme Commission) number reported by InterProScan to retrieve the enzyme name with BLASTP search against UniProtKB/Swiss-Prot 61 to filter out hits that are below 60% identity and 70% query/hit coverage. Although such high stringency on per cent identity and sequence coverage reduced the available number of functional descriptions, it reduced the false-positive prediction rate, as desired here. Construction of age distributions and WGD analyses. K S -based age distributions were constructed as previously described  62. In brief, the K S values between genes were obtained through maximum likelihood estimation using the CODEML program  63  of the PAML package (v.
For further details see Supplementary Note 4. 1. Absolute dating of the identified WGD event was performed as described previously  13, 29. In brief, paralogueous gene pairs located in duplicated segments (anchors) and duplicated pairs lying under the WGD peak (peak-based duplicates) were collected for phylogenetic dating. Anchors, assumed to be corresponding to the most recent WGD, were detected using i-ADHoRe 3. 0 (refs 66,67). Only a low number of duplicated segments and hence anchors could be identified, most likely because of the fragmented assembly of Z. marina. However, the identified anchors did confirm the presence of a broad WGD peak between a K S of 0. 8 and 1. 6 (data not shown). For each WGD paralogueous pair, an orthogroup was created that included the two paralogues plus several orthologues from other plant species as identified by InParanoid (v.
OrthoMCL results and related protein resources are available in the ORCAE download section. To further understand gene family expansion or contraction in Z. marina in comparison with other sequenced genomes, gene family sizes were calculated for all gene families (excluding orphans and species-specific families) (Supplementary Note 4. 2). The number of genes per species for each family was transformed into a matrix of z-scores in order to centre and normalize the data. The first 100 families with the largest gene family size in Z. marina were selected. The z-score profile was hierarchically clustered (complete linkage clustering) using Pearson correlation as a distance measure. The functional annotation of each family was predicted based on sequence similarity to entries in the InterProScan and Pfam protein domain database where more than 30% of proteins in the family share the same protein domain.
The phylogenetic profile and phylogenetic tree topology provided at PLAZA  84  were used to reconstruct the most parsimonious series of gene gain and loss events. The Dollop program from the PHYLIP package  85  was used to determine the minimum gene set at ancestral nodes of the phylogenetic tree. The Dollop program is based on the Dollo parsimony principle, which assumes that novel gene families arise exactly once during evolution but can be lost independently in different phylogenetic lineages. Search for presence/absence of orthologues for specific genes and families. A dedicated search for orthologues/homologues was performed for genes and proteins involved in stomata differentiation (Supplementary Note 5. 1), volatile biosynthesis and sensing with focus on ethylene and terpenes (Supplementary Note 6.
1), as well as genes involved in male flower specification and pollen Letter reSeArCH differentiation (Supplementary Note 11. 1). To this end, queries were chosen from documented genes involved in these pathways (usually from Arabidopsis but occasionally from Oryza, Zea and tomato). Next, the search for homologues in Zostera marina, Spirodela polyrhiza, Oryza sativa japonica and Arabidopsis thaliana (when not used as a query) was performed using BLASTP. To avoid missing or poorly annotated genes a TBLASTN search was conducted using the above queries against the Zostera marina and Spirodela polyrhiza genomes. Putative orthologues were identified based on reciprocal BLASTP searches with Arabidopsis (or the other queries). Owing to speciesspecific duplications, this may produce some paralogous genes to appear orthologous to the query, or vice versa (see Extended Data Tables  1 2 3 ).
Letter reSeArCH Extended Data Figure  4  | Unrooted maximum likelihood tree of genes encoding light-harvesting complex A (LHCA) and LHCB proteins of Z. marina, Spirodela polyrhiza and Arabidopsis thaliana. The analysis was carried out on protein sequences using PhyML 3 with LG substitution model and 100 bootstrap replicates. Supplementary Note 7. 1, Supplementary Table.
Letter reSeArCH Extended Data Figure  5  | Alignment of metallothionein (MT) and half-metallothionein (HMT) genes in Z. marina as compared with other plants. Alignments were performed in ClustalW on the Lyon PBIL web server and edited manually. The upper alignments are for type 1-3 MTs and HMTs; the lower alignment is for type 4 EcMTs where there is no Zostera homologue. Conserved residues are shown in red and residues in the same amino acid group in blue. Cys and His residues, putatively involved in binding metals, are highlighted in green and yellow, respectively. Aromatic amino acids absent in canonical animal MTs are highlighted in grey. MTs and MT-like proteins were obtained from: Arabidopsis thaliana (ARATH), Japanese rice (ORYSJ), Cicer arietinum (CICAR), banana (MUSAC), wheat (WHEAT), potato (SOLTU), Setaria Italica (SETIT), Vitis vinifera (VITVI) and the alismatids: Posidonia oceanica (POSOC) highlighted in grey, Spirodela polyrhiza (SPIPO) highlighted in blue, and Zostera marina (ZOSMA) highlighted in yellow.
Letter reSeArCHRNA-seq (flower tissues) was funded by the Excellence Cluster, Future Ocean, Kiel to T. B. H. R. Participation of G. P. and E. D. was supported by the MIUR Italian Flagship project RITMARE(NRP 2011-2013). G. A. P. was supported by FCT-EXCL/AAG-GLO/0661/2012. We thank I. D. Gromicho, KAUST, for his artistry in the production of Extended Data Fig. 6. This work also benefited from discussions within the ESSEM COST action ES0906, "Seagrass productivity from genes to ecosystem management" (2009-2014), J. L. O. , G. P. and T. B. H. R; and the Linnaeus Centre for Marine Evolutionary Biology (CEMEB)-Tjärnö, Gothenburg University, J. L. O. and M. T. J. L. O. especially thanks K. Johannesson (CeMEB-Tjärnö), C. Boyen (SBR-Roscoff), R. Reinhardt (MPI-Cologne) and E. Serrão (CCMAR-Faro) for their ongoing encouragement, and the more than 70 colleagues who submitted letters of support for the original proposal to the JGI-Community Sequencing Program.
V. d. P. Y. V. d. P also acknowledges support from the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement 322739-DOUBLE-UP. RNA-seq (Finnish clone genotype) was funded by the Marine Benthic Ecology and Evolution (MarBEE) group, within the former Centre for Ecological and Evolutionary Studies (now Groningen Institute for Evolutionary Life Sciences), University of Groningen to J. L. O. the following co-authors: J. W. J. , the chromosome assembly analysis; B. V. and Y. -C. L. , gene family clustering and comparative phylogenomics; A. R. K. and E. B. B. , Pfam domains; E. D. P. and P. J. G. , miRNA; R. L. , K. V. and Y. V. d. P. , wholegenome duplication; F. M. , Y. -C. L. and Y. V. d. P. , transposable elements; B. V. , co-linearity and synteny comparisons; M.
Pair-wise sequence alignments Pseudomonas sp. Pseudomonas agar base with cetrimide (0. 01 g l -1 ), fucidin (0. 01 g l -1 ), cephaloridine (0. 05 g l -1 ) Spread-plate 24 30 Acetic acid bacteria Glucose solid GYC with cycloheximide (0. 1 g l -1 ) Spread-plate 96 30 Yeasts Sabouraud Dextrose with chloramphenicol (0. 1 g l -1 ) Pour-plate 48 30 a All the culture media were produced by Oxoid Ltd. (Basingstoke, UK), with the exceptions of SDB and Glucose solid GYC, which were laboratory-made. were carried out using the BLAST search in the NCBI Nucleotide Collection Database  (Altschul et al. , 1990).
Following denoising, the selected OTU were chimera checked using the UCHIME software  (Edgar et al. , 2011). In detail, each trimmed read was mapped to its corresponding nonchimeric cluster using the USEARCH global alignment algorithm  (Edgar, 2010). Each sequence in a cluster was then aligned to the consensus sequence. Each sequence was corrected base by base in order to remove noise. Analysis of microbial diversity was finally performed by running the centroid sequence from each cluster against the USEARCH algorithm, using a database of high quality sequences derived from the NCBI. Lastly, the output was analyzed using an internally developed python program that assigns taxonomic information to each sequence. The percentage of each bacterial OTU was analyzed individually for each sample, providing relative abundance information among the samples based on the relative numbers of reads within each  (Andreotti et al.
Statistical Analyses Data (at least three replicates) of pH, lactic acid, acetic acid, ethanol, carbohydrates, FAA, and cell density of presumptive LAB and yeasts were subjected to one-way ANOVA, and pair-comparison of treatment means was achieved by Tukey's procedure at P < 0. 05, using a statistical software (Statistica 7. 0 per Windows). Principal Component Analysis (PCA) was also performed using Statistica 7. 0.
The data used relate to human population, national production of plant, animal and aquatic products which were transformed into kilo calories and areas of land cultivated for food and non-foodstuffs. Physical balances (past and future) between food biomass resources and their use are at the heart of Agribiom. Such balances were reconstructed from the 1960s and simulated for six regions of the world (the same ones used by the MA -members of the OECD 4 in 1990; the Former Soviet Union; Latin America; sub-Saharan Africa; the Middle East and North Africa; and Asia). To assess biomass uses a number of explicit assumptions had to be made. The two quantitative scenarios were built as the result of the interaction and adjustments between the modellers of Agribiom and the expert panel. Step three -building complete scenarios: The variables considered in the Agrimonde scenarios were: global context, international regulations, dynamics of agricultural production, actors' strategies, knowledge and technologies, and sustainable development.
Land use scenarios for the global regions were also built and these were described through short narratives and the consequences of each scenario were illustrated with the GlobAgri-AgT platform. For the quantitative scenarios it was assumed that all regions followed the same scenario. Step four -analysis: When examining the impact of each scenario on the four dimensions of nutrition and food security Agrimonde-Terra has been at pains to take scale fully into account. The governance of land use tends to be local or national, although the international scale can be important, while the governance of food security is both national and international. Agrimonde-Terra has also used a quantitative platform called GlobAgri for generating databases and biomass balance models from FAOStat 5 and other data.