dataesr/scientific-paragraphs-categorization huggingface dataset:
We address the impact of different first guesses for both the SS and MS optimisations for both methods in each section of the results. Additionally, for one PFT (temperate deciduous broadleaf forest, TempDBF) we performed the same four numerical experiments using pseudo-observations generated from outputs of the model with random parameters, in order to verify if the assimilation methods were finding the correct minima of the cost function. Such pseudo data are not biased by observation uncertainties (no added uncertainties) or model discrepancies, and thus allow us to directly assess the performance and convergence properties of the optimisation schemes. Note that for these pseudo-data experiments the second term of the cost function (Eq. 1) was excluded. The performances of the optimisation methods are compared using the reduction of the model-data root mean square difference (RMSD) between the prior and the posterior, expressed as 1 -RMSD post /RMSD prior • 100 %, as well as the range of posterior parameter values and their difference with the true value as estimated from the pseudoobservation tests.
This could thus explain that the optimisation degrades the model-data fit at these sites but it could also indicate that there is no optimal parameter set improving the model-data fit at all sites, suggesting the need to refine the PFT description and/or to improve the model structure (see a list of potential impacts of missing processes in the ORCHIDEE model in Sect. 2. 1). Note that MS optimisations are likely to be more impacted by model structural errors than SS optimisation, as these errors may have different impacts at each site. Limits arise when the objective is to study the response of a specific ecosystem (and not a generic PFT) to external drivers as the MS parameter set might be suboptimal. The performance of the two methods differs between the PFTs. For TropEBF, TempENF and C 3 grass PFTs the GA still provides on average significantly larger RMSD reduction than the gradient method, with ratios between the two of 2.
Acknowledgements. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (U. S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program ;  DE-FG02-04ER63917  and  DE-FG02-04ER63911 ),  AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux ,  Fluxnet-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia and USCCC. We acknowledge the financial support to the eddy covariance data harmonisation provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Universiteì Laval, Environment Canada  and  US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience ,  Oak Ridge National Laboratory, University of California -Berkeley  and the  University of Virginia.
Code availability. The ORCHIDEE model code and the run environment are open source and available at https://orchidee. ipsl. fr (last access: 26 November 2018). The tangent linear version of the ORCHIDEE model has been generated using commercial software (TAF; http://www. fastopt. com/products/taf, last access: 26 November 2018), thus only the "forward" version of the ORCHIDEE model is available for sharing. The optimisation tool is available through a dedicated web site for data assimilation with ORCHIDEE (https://orchidas. lsce. ipsl. fr, last access: 26 November 2018). Nevertheless readers interested in running ORCHIDEE and/or optimisation tool are encouraged to contact the corresponding author for full details and latest bug fixes. Supplement. The supplement related to this article is available online at: https://doi.
Accordingly, we omitted the sequences from E. tripsacoides and E. muticus; in these analyses the bootstrap values placing Z-T clade 2 sister to the Rottboellia-Hemarthria clade were noticeably higher (82 vs. 42), suggesting strong signal connecting the latter two groups (Fig. 1 ). To test the apparent sister relationship of the U-V clade to Z-T, we generated transcriptomic data and focused particularly on gene trees that included known syntenic pairs of paralogous genes (syntelogs) as defined by  (14, 15). These are pairs of genes hypothesized to result from the allopolyploidization event. To mitigate the effect of missing data, we examined only gene trees that included, at a minimum, sequences from Zea, Tripsacum, Urelytrum and Vossia. (RNA was not available for Oxyrachis or Rhytachne. ) In total, 2,615 gene trees met these conditions.
Taxon selection Taxa were selected to encompass the diversity of Tripsacum and Zea and the majority of genera within Andropogoneae. Species names, voucher information, and GenBank accession numbers for the 4-gene data, transcriptome data, and plastome data are listed in Supplemental Tables  1 2 3 , respectively. All data matrices and tree files are deposited at www. dryad. org, accession number xxx. DNA and RNA Isolation and Quantification DNA was isolated using a modified CTAB protocol adapted from Doyle and Doyle (32) from live and silica dried leaves. DNA quantity was checked using a Nanodrop (ThermoFischer Scientific, Waltham, Massachusetts, USA) and a Qubit 2. 0 (ThermoFischer Scientific, Waltham, Massachusetts, USA). Double-stranded DNA concentrations were used to determine starting material for library preparation.
Four-locus Data Sequencing and Processing Data for the four-locus phylogeny was obtained in the same manner as  (16)  with some minor alterations. Trees were estimated using RAxML v. 8. 0. 22  (33)  under the GTR + gamma model with 500 bootstrap replicates. A Bayesian inference (BI) phylogeny was reconstructed using MrBayes v. 3. 2. 6  (34)  with two independent runs with four chains-one cold and three heatedwith six substitution types under the GTR+gamma evolutionary model sampling every 1,000 for 5,000,000 generations. Convergence was verified using potential scale reduction factor (PSRF) values calculated by MrBayes.
McKain et al. -8 Chloroplast genomes were assembled using the Fast-Plast method  (35). Raw reads were trimmed using Trimmomatic v. 0. 36  (36)  with adapter trimming of appropriate adapters (Nextera or NEBNext), a seed mismatch of 1, a palindrome clip threshold of 30, and a simple clip threshold of 10. Reads were further filtered using a sliding window of 10 basepairs with a minimum average quality score of 20 and a minimum length of 40. Reads were then mapped to a set of Andropogoneae chloroplast genomes identified from GenBank, which was updated as plastomes were finished, using bowtie2 v. 2. 1. 0 (37) under the "very-sensitive-local" parameter set. Mapped reads were assembled using SPAdes v. 3. 9. 0  (38)  with the "only-assembler" option and k-mer sizes of 55, 69, and 87 for sequencing run with reads of 100 bp and 55, 87, and 121 for runs with reads larger than 100 bp.
Assemblies were filtered based on coverage estimated by SPAdes to remove extremes of coverage in contigs less than 1000 bp. The program afin (in FastPlast  (35) ) was used to attempt contig fusion and extension using the full, trimmed read set with three iterations. The first iteration consisted of 150 search loops, an initial contig trim of 100 bp, a minimum overlap 20 bp, a minimum coverage of 2, and a max extension length of 0. 75  the max read length for the sample. The second and third iterations consisted of 50 search loops, an initial contig trim of 100 bp, a minimum coverage of 1, and differed with a minimum overlap of 15 and 10 bp, respectively. If the result was a single contig, the assembly sequence_based_ir_id. pl and orientate_plastome_v. 2. 0. pl scripts were used to identify boundaries of the small single copy, large single copy, and inverted repeat regions of the chloroplast genomes  (35).
If an afin run did not result in a full plastome, contigs were assembled manually in Sequencher v. 5. 3 (Gene Codes Corporation) using Schizachyrium scoparium (NC_035032. 1) to scaffold as described in McKain et al. (39). Gaps were closed through in silico plastome walking if possible, but otherwise filled using N's. A coverage analysis as implemented in Fast-Plast was done for each plastome to identify regions of low quality assembly. These were investigated manually and adjusted based on read coverage when necessary. Chloroplast genomes were annotated using Verdant  (40). Annotations were verified using GenBank validation (https://github. com/mrmckain/Verdant_Utilities/). If any genes were found to be missing from the annotation or seemingly pseudogenized, the assemblies were verified by mapping reads as above and visual inspection in Sequencher.
If an alternative assembly was found to be more supported and provided sequence for a functional gene, the assembly was changed and the plastome reannotated. Final annotations are available in Verdant (verdant. iplantcollaborative. org) and GenBank (XXX-XXX). Chloroplast genomes were split into their subcomponent regions: LSC (large single copy), SSC (small single copy), and IR (inverted repeat) using a Perl script (https://github. com/mrmckain/Genome_Skimming_Utilities). These regions were aligned individually using MAFFT v. 7. 245 (41) using high speed default parameters. Once aligned, these regions were concatenated using a Perl script (https://github. com/mrmckain/Genome_Skimming_Utilities). The best base pair substitution model was determined to be GTR + gamma + I using the Aikake Information Criterion (AIC) in jModelTest2 v.
When libraries were made using the NEBNext Directional Kit, the stranded option was used in Trinity. Other parameters were defaults. After assembly, the "align_and_estimate_abundance. pl" script packaged with Trinity was used to estimate read abundance and the FPKM for each script using RSEM v. 1. 2. 1  (46). Transcripts were filtered using the "filter_fasta_by_rsem_values. pl" packaged with Trinity and set to a minimum isoform percentage value of 1. 00. Once transcripts were filtered, coding sequence was estimating using Ref-Trans (39) by BLASTing the transcripts against a database of CDNA from all available PACMAD genomes in Phytozome 10 (https://phytozome. jgi. doe. gov/pz/portal. html) using tblastx with an e-value cutoff of 1e-10. A reciprocal 85% overlap of a transcript and a database sequence was required to proceed to annotation.
Best matches were used to guide translations with GeneWise (part of the Wise v. 2. 2. 0 package; (47) under default parameters. For each translation, the longest identified reading frame was used in subsequent steps. Annotated sequences were identified to orthogroups using OrthoFinder v. 0. 2. 0 (48) with default parameters. Orthogroups were filtered so they had at least five representative taxa. Amino acids for each orthogroup were aligned using PASTA v. 1. 6. 3  (49)  with the "auto" parameter setting. PAL2NAL v. 14  (50)  was used to produce a codon alignment from the amino acid alignment under default parameters. Orthogroups were further filtered by sorting aligned orthogroups into clusters with a Hamming distance threshold of 0. 25 using a Perl script (core_split_alignments_distance_v1.
McKain et al. -10 The package PhyDS (https://github. com/mrmckain/PhyDS) was used to identify the putative evolutionary origin of identified maize paralogs. For each paralog, the PhyDS algorithm starts at the representative tip of the tree and moves to the ancestor node. In each iteration, descendants of the current node are identified. PhyDS was parameterized so that this process was iterated until either 1) a taxon was encountered that was not from either Zea or Tripsacum or 2) the sister paralog (syntelog) to the current target paralog was found. When either of these conditions was met, the bootstrap value was recorded for the current node as well as the descendent genes identities. If the Zea mays sister paralog was encountered, then the sister lineage was designated as Zea mays to differentiate these instances.
Acknowledgements: This project was supported by  NSF  grants  DEB-11456884  and  DEB-1457748  to EAK. Pre-publication data for the Panicum virgatum genome was provided by the  Department of Energy Joint Genome Institute. Collecting in Tanzania by STM was done in collaboration with the  Missouri Botanical Garden  through a permit from  COSTECH. We thank  James Schnable  for pre-publication access to an updated list of paired syntenic loci in the maize genome. We also thank  Cassiano A. D. Welker  for  DNA of Schizachyrium  sanguineum for the plastome sequence, and  Trevor Hodkinson  for  DNA of Polytoca and Hemarthria  for the 4-gene tree. Shao Ying  in the lab of  Thomas Brutnell  extracted several of the RNA samples used for transcriptomes.
Identification of New Non-coding Transcripts during Embryonic Development To obtain a comprehensive view of the transcriptional landscape during early and mid-stages of embryogenesis, we deeply sequenced rRNA-depleted total RNA samples from multiple developmental stages, cellular contexts (fluorescence-activated cell sorting [FACS]-sorted mesodermal cells [Meso] versus whole embryo  [WE] ), and subcellular compartments (nuclear RNA versus whole cell; Figure  1A ; STAR Methods). The meso-dermal samples were obtained by generating a transgenic Drosophila line with nuclear EGFP specifically expressed in mesoderm (under the control of a twist enhancer)  [43]. Live embryos were dissociated into single cells and mesodermal cells isolated using FACS to greater than 95% purity (Meso; STAR Methods).
Altogether, this resulted in 14 conditions (time, tissue, nuclear enrichment, and RNA-seq method), each with biological replicates (with the exception of the CAGE data; Figures  1A, S1A , and S1B; Table  S1 ). To obtain a comprehensive view of new transcripts, we applied a combination of de novo and reference-based transcript assembly (STAR Methods; Figure  1B ), an approach previously shown to give more accurate and complete transcriptome assemblies  [44]. Our assembled transcripts were subjected to differential expression analysis across stages and tissue (Figure  S1C ). In contrast to other lncRNA identification efforts  [45] [46] [47] , we did not require transcripts to be spliced but rather required a minimum length of 500 nt for monoexonic transcripts (in contrast to the standard 200 nt) and applied a series of strict filters to remove spurious, poorly supported, transcripts (Figure  S2A ; STAR Methods).
(E) Pie charts showing the genomic distribution of novel and annotated lncRNA genes with respect to PCGs. Genes are assigned to one class following the hierarchy: TSS > TES > exon > intron > promoter > enhancer > intergenic. (F) Polyadenylation status of lncRNAs. Heatmaps show expression levels of novel lncRNAs in total RNA-seq (ribodepleted) and polyA-selected RNA-seq libraries from matched 6-8 hr whole-embryo samples. See also Figures  S2  and S3  and Tables  S1, S2 , S3, and S4. were used for all subsequent analyses. Alignment of the novel transcripts to the latest genome build revealed that 9 of our 179 lncRNA genes ($5%) were included in the most recent annotation (currently containing 2,507 lncRNA genes; see STAR Methods). We have therefore compiled a comprehensive set of 362 lncRNA genes (192 annotated + 170 novel) that are confidently expressed during these stages of embryogenesis, which spans from blastoderm stages through to mid-embryogenesis.
Characterization of Novel Developmental lncRNAs We applied three complementary approaches to assess whether the novel transcripts are indeed likely to be non-coding (STAR Methods). First, we used coding potential assessment tool (CPAT) to score for coding potential  [48]. Comparing the CPAT score of our novel transcripts to FlyBase annotated lncRNAs and PCGs expressed at the same stages (using the high-confident sets defined above) indicates that our novel transcripts have a very low coding potential, even lower than previously annotated lncRNAs (Figure  1C ). Only 6 novel genes exceed the threshold of 0. 39, calibrated for discriminating coding from non-coding transcripts in D. melanogaster. Second, BLAST we used to determine whether any predicted open reading frame (ORF) could correspond to known proteins or protein domains (Figure  S2B ).
Third, we measured signatures of selection within the predicted ORFs across 12 Drosophila species using PhyloCSF  [49]  (Figure  S2C ). These analyses confirmed that the new transcripts have a coding capability comparable to that of annotated lncRNAs (Figures  1C, S2B , and S2C). To confidently predict individual instances of potentially coding genes, we required at least two of these methods to have values beyond threshold (see STAR Methods). Only 10 novel genes (out of 170) were positive for any two methods, and 3 were positive for all three methods. This is comparable to currently annotated Drosophila lncRNAs, where 8 high-quality (HQ) annotated genes (out of 183) were positive for any two methods and 2 were positive for all three methods. Therefore, the vast majority ($94%) of the newly identified genes likely correspond to novel lncRNAs, although we cannot exclude that some transcripts may encode micropeptides  [50].
We also assessed whether novel lncRNAs might harbor primary microRNAs (miRNAs). Nucleotide BLAST was used to search for matches to known miRNAs, requiring both strands of the miRNA duplex to be on the same strand of the lncRNA gene, separated by a short region. Only two of our novel lncRNA genes show matches to known miRNA. Both occur on the opposite strand (Table  S2 ), indicating that none of the lncRNA transcripts harbor primary miRNAs. In contrast, we identified nine lncRNAs within the currently annotated lncRNA set that harbor primary miRNAs (Table  S3 ). Using the RNA-seq signal and data from matching 5 0 CAGE libraries (Figure  S2C ), we adjusted the start positions of 71 lncRNA transcripts to match a proximal 5 0 CAGE peak (STAR Methods). These corrections improved the agreement of our annotated TSSs with independent indicators of gene start sites, such as mesoderm-specific accumulation of Pol II and chromatin modifications associated with active promoters  [43] , resulting in similar distributions to currently annotated Drosophila lncRNAs (Figure  1D ).
Although this is consistent with a possible role of these nuclear transcripts in the cis regulation of early embryonic patterning genes, this may also reflect other phenomena, such as the sharing of regulatory elements acting on these processes. To more formally assess the dynamic expression of the nonnuclear lncRNAs, we applied two rounds of k-means clustering (STAR Methods). This resulted in five robust clusters, containing 130 of the 362 lncRNA genes, with highly correlated expression during development (Figure  2B ). Clusters 4 and 5 contain genes with expression enriched in mesoderm; cluster 4 transcripts are expressed at higher levels at the earlier two stages and then decrease, and cluster 5 transcripts increase as embryogenesis   S5. proceeds, being highest in the later time point (Figure  2B ).
729). This lack of negative correlation argues against transcriptional silencing mediated by antisense transcription  [16, 17]  as a widespread mechanism in Drosophila embryonic development, although we note that such effects may be masked in measurements of steady-state RNA levels. Divergent transcription of lncRNA-PCG pairs is frequent in mammals, and the expression levels of both genes co-vary during embryonic stem cell (ESC) differentiation  [56]. In addition, the presence of a divergent lncRNA is often associated with the strong induction of transcription at the main sense promoter  [19]. To specifically assess the impact of divergent transcription during Drosophila embryogenesis, we used our mesodermspecific total RNA-seq datasets to limit potential heterogeneous signals coming from many cell types in the embryo.
Genotype-Dependent Changes in lncRNA Expression As lncRNA expression is commonly tissue specific and rapidly evolving  [4] , we next examined lncRNA expression across two different genetic strains. To detect the lncRNAs described above, we used a Drosophila line (twi::EGFP) based on the Oregon R reference strain (Figure  1 ). To assess the impact of strain-specific differences, we compared RNA-seq data from this strain to a second strain (vas::Cas9), which was used for  the CRISPR-mediated deletions. We selected genes that are expressed in whole-embryo 6-8 hr Oregon R samples, using a relaxed threshold of 1 FPKM. Surprisingly, many lncRNA genes are expressed at levels below that threshold in the vas::Cas9 genetic background (Figure  6A ), including 40% (27 out of 67) that are virtually undetectable (Figure  6B , red points).
These results indicate that a proportion of lncRNAs are very young and not fixed within the population, which suggests that they are non-functional despite their impressive expression. This scenario might be more common when looking at early developmental stages, particularly considering the proposed role of male reproductive organs as a source of novel genes during metazoan evolution  [63]. In keeping with this, our lncRNA genes expressed at early embryonic stages have significantly lower conservation than those expressed constitutively or at later stages (median of phastCons scores 62, 169, and 195 for early, constitutive, and late, respectively; Mann-Whitney's p values of 0. 0124 for early versus constitutive and 0. 0179 for early versus late; STAR Methods). This trend is also observed if only intergenic transcripts are considered, eliminating possible confounding effects of lncRNA overlap to PCGs.
QUANTIFICATION AND STATISTICAL ANALYSIS Mapping and assembling pipeline Reads were pre-processed with Trimmomatic version 0. 30 (https://github. com/timflutre/trimmomatic) to trim the first nucleotide. Before mapping, FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) was used to confirm sequence quality. To generate a comprehensive transcriptome assembly out of the six considered conditions (WE 3-4h, WE 4-6h, WE 6-8h, Meso 3-4h, Meso 4-6h, Meso 6-8h) a combination of de novo and reference-based assembly strategies were used. Sample reads were aligned to the Drosophila melanogaster genome dm3 (corresponding to BDGP5) (soft-masked using ENSEMBL release 70) with STAR version 2. 3. 1z [68]. Reads from biological replicates were aligned together. Reads from nuclear enriched samples of developmental times 3-4h and 6-8h were mapped with Meso 3-4h and Meso 6-8h samples respectively.
The mapper was run with options ''-alignIntronMax 100000-alignMatesGapMax 500-outFilterIntronMotifs RemoveNoncanonical-outFilterType BySJout-outSAMunmapped Within. '' The genome was formatted with STAR with option ''-runMode genomeGenerate-sjdbOverhang 100'' and providing the FlyBase version r5. 55 annotations. The read alignments were post processed to remove soft clipped bases. Individual reference-based transcript assemblies were generated for each condition with cufflinks  [71]  version 2. 1. 1. The FlyBase reference annotations version r5. 55 were used to guide the assemblies. In parallel, Trinity  [69]  release 2014-07-17 was used to assemble transcript isoforms de novo. Assembled transcripts were projected onto the Drosophila melanogaster genome dm3 with the splice mapper GMAP  [70]  version 2013-03-31.