dataesr/scientific-paragraphs-categorization huggingface dataset:
E. GA is a Research Associate at the  Harvard-Smithsonian Center for Astrophysics , and thanks the  Spanish Ministerio de Economía y Competitividad  for support under projects  FIS2012-39162-C06-01  and  ESP2015-65597-C4-1-R , and  NASA  grant  ADAP NNX15AE56G. JBS wishes to acknowledge the support of a  Career Integration Grant  within the  7th European Community Framework Program ,  FP7-PEOPLE-2013-CIG-630861-FEASTFUL. J. A. acknowledges financial support from the  Science and Technology Foundation (FCT, Portugal)  through research grants  PTDC/FIS-AST/2194/2012  and  UID/FIS/04434/2013. DLC and JG acknowledge support from  STFC , in part through grant numbers  ST/N000838/1  and  ST/K001051/1. The authors wish to thank  A. Jaffe  and  D. Smith  for useful conversations, and the anonymous referee for their comments.
The libraries were sequenced using Illumina Hi-Seq 2500 with High-Output (HO) mode using a V4 chemistry sequencing kit (Illumina). Each RNA-seq was performed in duplicates. The reads were then processed to remove adapter sequences and poor quality reads by Trimmomatic  (30) , converted to the FASTQ format with FASTQ Groomer  (31) , aligned on the HG001 genome closely related to HG003 genome (32) using BOWTIE2  (33). Finally, the number of reads mapping to each annotated feature has been counted with HTSeq (34) using the interception non-empty protocol. All processing steps were performed using the Galaxy platform  (35). To estimate the enrichment values for the MAPS experiment or the differential expression analysis for the transcriptomic experiment, we used DEseq2  (36). The statistical analysis process includes data normalization, graphical exploration of raw and normalized data, test for differential expression for each feature between the conditions, raw P-value adjustment and export of lists of features having a significant differential expression (threshold P-value = 0.
Gel retardation assays Radiolabelled purified RsaA or RsaAmutC (50 000 cps/sample, concentration < 1 pM) and cold mRNAs were renaturated separately as described above. For each experiment, increasing concentrations of cold mRNAs (20-240 nM for ssaA2 3, 50-900 nM for the other mRNAs) were added to the 5 end labelled RsaA or RsaAmutC in a total volume of 10 l containing the ToeP+ buffer. Complex formation was performed at 37 • C during 15 min. After incubation, 10 l of glycerol blue was added and the samples were loaded on a 6% polyacrylamide gel under nondenaturing conditions (1 h, 300 V, 4 • C). Quantification of the data corresponding to free RsaA or RsaAmutC and to RsaA/mRNA complex present on the autoradiography was done with ImageQuant TL software (GE Healthcare Life Sciences). Under these conditions where the concentration of the labelled RNA is negligible, the K D dissociation constant can be evaluated as the concentration of the cold RNA that showed 50% of binding.
Protein identifications were assigned using Mascot algorithm (version 2. 5, Matrix Science, London, UK) through ProlineStudio 1. 2 package (http://proline. profiproteomic. fr/). Data were searched against the S. aureus HG001 genome. Peptide modifications allowed during the search were: N-acetyl (protein), carbamidomethylation (C) and oxidation (M). Mass tolerances in MS and MS/MS were set to 20 ppm and 0. 5 Da, respectively. Two trypsin missed cleavages sites were allowed. After the import of the Mascot data files, proteins were validated on Mascot pretty rank equal to 1. 1% false discovery rate (FDR), on peptide spectrum matches (PSM) based on PSM score, and 1% FDR on protein sets on protein set score. A spectral counting quantitative strategy was applied on the Mascot identification summaries.
These data show that the steady state level and the stability of RsaA were not affected by the presence of the MS2 tag. Affinity chromatography was then performed to verify the specific retention of MS2-RsaA by the MS2-MBP fused protein. The RNAs extracted from the different fractions (Crude extract, Flowthrough, Wash and Elution) were analysed by Northern blot and revealed with a DIG-probe against RsaA. The data showed that MS2-RsaA was specifically enriched in the elution fraction (Figure  1B ). As we have previously shown that RsaA is a translational repressor of mgrA mRNA, we have tested whether the mRNA was co-purified together with MS2-RsaA. We also used hu mRNA, which was not enriched together with RsaA. The Northern blot performed with a DIG-RNA probe revealed that mgrA mRNA was strongly enriched in the eluted fraction while hu mRNA was primarily detected in the Flowthrough and Wash fractions (Figure  1B ).
7-fold). We could also estimate the cellular abundance of each of the transcripts of the RsaA targetome, which has been used in combination with the enrichment values of the MAPS analysis to estimate in vivo affinities (designated as affinity rank in Supplementary Table  S5 ) between RsaA and its targets. The data were analysed and visualized using Galaxy  (35)  and the Integrative Genomics Viewer (IGV) browser, respectively (39) (Table  1 , and Supplementary Table  S4 ). A detailed protocol for the bioinformatic analysis is provided in 'Materials and Methods' section. Briefly, we aligned the sequencing reads onto our reference genome HG001, counted per feature and normalized, we have estimated the enrichment of putative targets by comparing the number of reads obtained from the tagged MS2-RsaA purification and the MS2 alone as control.
Affinity of complexes between RsaA and HG001 01977 or ssaA2 2 was weaker (>250 nM), but the deletion of the 5 UTR of ssaA2 2 significantly enhanced duplex formation (Supplementary Figure  S3A ). Finally, no gel retardation was observed when complex formation was performed with RsaA and increasing concentrations of ssaA2 4, glpF, rplS, norB-4 and cycA mRNAs (up to 1 M, Supplementary Figure  S3B ). Because the C-rich motif of RsaA was predicted to basepair with the SD of mRNA targets, we analysed whether mutations at these positions would affect RsaA binding as it was demonstrated for mgrA  (25). Complex formation was carried out under strictly identical conditions for the WT and a version of RsaA in which the GUUCUCCC sequence had been replaced by CAAGACCC (RsaAmutC). The data showed that RsaAmutC failed to bind to ssaA2 3 and flr mRNAs (Figure  2B ).
ACKNOWLEDGEMENTS We are grateful to  Anne-Catherine Helfer  for her help in the footprinting and toe-printing assays, to  Jessica Baude  for the RT-qPCR experiments and to  Lauriane Kuhn  and  Philippe Hammann  for their assistance with mass spectrometry analysis (IBMC proteomic platform). We are thankful to  Redmond Smyth  for critical reading of the manuscript, to  Delphine Bronesky  and  David Lalaouna  for helpful advices and discussions.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  to [P. R. ] and has been published under the framework of the  LABEX :  ANR-10-LABX-0036 NETRNA  to [P. R. ], a funding from the state managed by the  French National Research Agency  as part of the investments for the  future program. A Tomasini  was supported by  Fondation pour la Recherche Médicale  ( FDT20150532433 ). Funding for open access charge:  ANR  [ ANR-10-LABX-0036 ].
This is confirmed by the work of Domecq et al  [2] , which shows that among the 5551 recent papers mentioning patient engagement, only 142 give useful data about the way those patients have been involved in protocols. It results from this situation that, although the recognition of patient experiential knowledge (PEK) has reached the level of a social fact attested by diplomas, jobs, laws, and academic concepts  [3] , no standardized keywords and no shared typology can actually be used to facilitate comparative studies of situations where patients and their organizations are recognized as coresearchers. As Domecq et al  [2]  have recommended that "bibliographic databases use indexing terms that identify active patient engagement in research to facilitate future research in this field," it is obvious that progress requires methods and typologies to describe which role PEK plays in health research and how to reduce the risks of tokenism.
Peak list generation The MS data generated for this manuscript, along with the search parameters, analysis parameters and protein databases can be downloaded from PeptideAtlas (www. peptideatlas. org) using the identifiers PASS00976 (whole proteome) and PASS00977 (surface-labeled). Mass spectrometer output files were converted to mzML format using msConvert version 2. 2. 0 (whole proteome data) or 3. 0. 5533 (surface-labeled data)  [22]  and searched with Comet version 2015. 02 rev. 0  [23]. The protein sequence database is described in the following section. The precursor mass tolerance was ±10 ppm, and fragment ions bins were set to a tolerance of 1. 0005 m/z and a monoisotopic mass offset of 0. 4 m/z. Semi-tryptic peptides and up to 2 missed cleavages were allowed. The search parameters included a static modification of +57.
The MS/MS data were analyzed using the Trans-Proteomic Pipeline (TPP)  [24]  version 5. 0. 0 Typhoon. Peptide spectrum matches (PSMs) were assigned scores in PeptideProphet  [25] , peptide-level scores were assigned in iProphet  [26] , and Protein identifications were inferred with ProteinProphet  [27]. Additional TPP parameters are available in S1 File. In the case that multiple proteins were inferred at equal confidence by a set of peptides, the inference was counted as a single identification and all relevant protein IDs were listed. Only proteins with ProteinProphet probabilities corresponding to a false discovery rate (FDR) less than 1. 0% (as determined from the ProteinProphet mixture models) were reported. For comparison with P. falciparum salivary gland sporozoites, a publically available data set  [19]  (available from Pepti-deAtlas using the identifier PASS00095) was re-analyzed with the same software and parameters described above.
The spectra were searched against a database comprising P. falciparum 3D7  [28]  (PlasmoDB v. 30, www. plasmodb. org [29] ), Anopheles stephensi Indian AsteI2. 3 [30]  (VectorBase, www. vectorbase. org [31] ), and a modified version of the common Repository of Adventitious Proteins (v. 2012. 01. 01, The Global Proteome Machine, www. thegpm. org/cRAP) with the Sigma Universal Standard Proteins removed and the LC calibration standard peptide [Glu-1] fibrinopeptide B appended. Decoy proteins with the residues between tryptic residues randomly shuffled were generated using a tool included in the TPP and interleaved among the real entries. P. falciparum protein annotations were updated from PlasmoDB v. 32.
Compiling a reference proteome A protein database containing sequence polymorphisms of P. vivax proteins occurring in Thailand was created by aligning DNA-seq and RNA-seq reads from field isolates to the P. vivax Sal-1 genome  [32]  (PlasmoDB v. 26). DNA-seq reads from 19 Thai field isolates were obtained from www. plasmodb. org and aligned using Burrows Wheeler Aligner (v. 0. 7. 12) and SNVs were called using the Genome Analysis Toolkit (v. 3. 6). RNA-seq reads for 13 isolates  [33]  (obtained from https://www. ncbi. nlm. nih. gov/bioproject/, accession number PRJNA376620) were aligned using STAR (v. 2. 5) and SNVs were called using the Genome Analysis Toolkit (v. 3. 6). All proteins with sequences different from the Sal-1 reference proteome were compiled (S2 File) and added to a database comprising P.
vivax Sal-1  [32]  (Plas-moDB v. 31), P. vivax P01  [34]  (PlasmoDB v. 31), Anopheles stephensi Indian AsteI2. 3 [30]  (VectorBase), and the modified version of the cRAP proteins described above. Decoy proteins were generated as above. Mass spectra from the two whole-proteome samples and the two surface-labeled samples were searched against the database with Comet as described above and the resulting PSMs were analyzed with PeptideProphet and iProphet as described above except that the NSP model was enabled in iProphet. All P. vivax peptides identified with iProphet probabilities corresponding to a model-estimated FDR less than 1. 0% were aligned against the P. vivax P01 reference proteome. A new P. vivax P01 reference proteome was assembled incorporating polymorphism-bearing peptides identified by the above-described search.
If a detected peptide was associated with a given P. vivax protein in at least one of the field isolates but did not align with the P. vivax P01 reference sequence due to sequence polymorphisms, the variant peptide sequence was appended to the end of the reference protein sequence entry in the fasta database. This modified P. vivax P01 reference proteome was added to the An. stephensi and cRAP databases described above. Additionally, the entry for CSP (PVP01_0835600), which contains the tandem repeat region specific to the VK210 haplotype, was appended with the sequence of the VK247 tandem repeat region  [35]. Decoys were generated as above. This database was used for all subsequent analysis of the MS data. P. vivax protein annotations were updated from PlasmoDB v. 32.
Protein quantification Relative protein abundance within and between samples was estimated using a label-free proteomics method based on spectral counting. Extended method details are provided in S1 File. The spectral counts for a protein were taken as the total number of high-quality PSMs (identified at a PeptideProphet probability corresponding to an FDR less than 1. 0%) that identified the protein. PSMs from degenerate peptides (peptides whose sequences were found in multiple proteins in the database) were split among proteins containing that peptide in a weighted fashion  [36, 37]. Relative protein abundance within samples was ranked using the normalized spectral abundance factor. The spectral abundance factor (SAF) for a given protein was calculated as the quotient of the total PSMs identifying that protein and the protein's length.
Identifying phosphorylated proteins Phosphorylated peptides were identified by searching the MS data with the same parameters listed above with the additional potential modification mass of +79. 966331 Da at Ser, Thr, and Tyr. The PSMs generated from these searches were analyzed separately by PeptideProphet as above, except that the DECOYPROBS option was used so that decoy peptides were assigned probabilities and included in the output. Decoy peptides were used to calculate an FDR among the subset of PSMs exhibiting phosphorylation. Due to the infrequent occurrence of phosphopeptides in these un-enriched samples, the decoy-estimated FDR for phosphopeptide PSMs was as high as 24% in the VK210 sample and 19% in the VK247 sample at the probabilities corresponding to a 1. 0% decoy-estimated FDR for the entire population of PSMs.
Even so, a number of high-abundance proteins were identified that were of significantly higher abundance in one species than the other (S3 Table ). For example, PVP01_0314600 (conserved Plasmodium protein, unknown function) was in the top decile of abundance in both P. vivax sporozoite samples, while its syntenic ortholog PF3D7_0718900 (conserved Plasmodium protein, unknown function) was not detected in the P. falciparum sporozoite sample, or for that matter, in any of the P. falciparum proteomics datasets on PlasmoDB spanning the entire P. falciparum lifecycle. In the P. falciparum sporozoite sample, two conserved Plasmodium proteins of unknown function, PF3D7_0215200 and PF3D7_0410500, were in the top decile of protein abundance but not detected at all in either P. vivax sample. Both proteins are up-regulated in P.
The cyan and orange points represent protein orthologs identified in only one species or the other. Diamond points represent proteins with no ortholog in the other species. (B) Protein ratios were calculated based on the adjusted and normalized spectral counts used to calculate the G statistic. The population of log-transformed protein ratios of proteins detected in both samples assumed a Gaussian distribution with a mean near zero. The mean and standard deviation from this distribution were used to calculate p-values using the complementary error in both P. vivax samples, and is putatively surface-exposed on salivary gland sporozoites (discussed below). Mass spectra were searched against the P. vivax P01 reference proteome  [34]  (PlasmoDB v. 31  [29] ). Current high-throughput MS approaches require a precise knowledge of the genome of the organism under study; a protein can only be identified if its exact sequence is contained in the database against which the mass spectra are searched.
Because the samples were obtained from field isolates and not laboratory strains, they were expected to contain protein sequence polymorphisms that would not be represented in the reference proteome. In order to increase the likelihood of identifying isolate-specific polymorphisms, the P. vivax protein database against which the mass spectra were searched was augmented with potential polymorphisms obtained from genomic and transcriptomic analyses of Thai P. vivax field isolates. A total of 13 RNA-seq and 19 DNA-seq data sets were aligned against the P. vivax Sal1 reference genome and a reference proteome was generated containing any protein with an amino acid sequence differing from the reference. Only 22% of the proteins in the reference proteome had completely conserved sequences across all 33 datasets (the 32 field isolates plus the reference genome).
g. , ATP binders, kinases and phosphatase (20. 9% of the phosphoproteins). Also well-represented were components of the gliding machinery, including Myosin A (MyoA; PVP01_1212200), the glideosomeassociated proteins GAP40 (PVP01_1018200), GAP45 (PVP01_1440900) and GAPM2 (PVP01_0532000), several inner membrane complex (IMC) proteins, and the calcium-dependent protein kinase CDPK1 (PVP01_0407500). The P. falciparum salivary gland sporozoite data were searched in the same fashion, identifying 91 phosphorylated proteins (S6 and S8 Tables). All but four of these had syntenic orthologs in P. vivax, and 48 of these (55%) were among the phosphoproteins identified from the P. vivax samples. The list of sporozoite phosphoproteins was compared against thirteen proteomic analyses of P. falciparum blood-stage parasites  [16, [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66]  (including five analyses of phosphopeptides enriched from blood-stage parasites  [56] [57] [58] [59] [60] ) and three proteomic analyses of P.
Label-free protein quantification based on spectral counting was used to compare relative protein abundance within and among samples. In addition to comparing the two P. vivax salivary gland sporozoite proteomes to each other, a union list of identified P. vivax proteins was compared with a P. falciparum salivary gland sporozoite dataset obtained from re-analysis of published proteomic data  [19]. The quantitative data was useful for identifying general trends, e. g. , highly abundant proteins that were identified in all datasets or proteins that were abundant in one species but whose orthologs were conspicuously absent in another. Identifying a protein in one sample and not the other when comparing two similar samples is common in MS-based proteomics; a protein may not be detected because it is truly absent in the sample, because it is below the detection limit of the instrument, or due to some technical issue such as interference from some other species in the sample or the stochastic sampling of the ion stream by the mass analyzer  [75].
, S10 Table). There was a very high In addition to detecting the proteins present in P. vivax salivary gland sporozoites, the data were analyzed for evidence of post-translational modifications, specifically phosphorylation and glycosylation. Protein phosphorylation is of interest for development of antimalarial drugs  [77, 78]  because the reversible modification is involved in regulation of essentially every aspect of the complex Plasmodium life cycle, yet the parasite and the mammalian host are sufficiently phylogenetically divergent that many Plasmodium protein kinases can, in theory, be selectively inhibited  [78, 79]. Although the limited amount of starting material available for this work precluded phosphopeptide enrichment, 139 proteins were identified with evidence of phosphorylation, and the orthologs of 48 of these were also phosphorylated in P.
Census and abundance data Within each plot, all stems ≥10 cm diameter at breast height were tagged, sized, and identified to species-level by William Farfan and Miles Silman during a 2013 or the most recent year before 2013 tree census, and then recorded in the ForestPlots database (https://www. for estplots. net). A subset of these stems were selected based on their abundance for trait sampling. These individuals were examined and potentially renamed by taxonomic experts at the Carnegie Institution for Science (https:// cao. carnegiescience. edu/spectranomics) (Malhi et al. , in review). Abundances for each taxon within each plot were then calculated using the revised names as summed basal area across all stems.
Trait sampling approach From April-November 2013, we measured plant traits as part of the CHAMBASA (CHallenging Attempt to Measure Biotic Attributes along the Slopes of the Andes) project. Based on census data for 2013 or the most recent census year before 2013, a sampling protocol was adopted wherein species were sampled that maximally contributed to plot basal area (a proxy for plot biomass or crown area). We aimed to sample the minimum number of species that contributed to 80% of basal area, although in the diverse lowland forest plots we only sampled species comprising 60-70% of plot basal area. Within each sampled species in each plot, five trees in upland sites and three trees in lowland sites were chosen for sampling. If three trees were not available in the chosen plot, we sampled additional individuals of the same species from an area immediately surrounding the plot.
Using single rope tree climbing techniques, we sampled one fully sunlit canopy branch and a fully shaded branch where possible, each at least 1 cm diameter, from each tree. Across all plots, approximately 40% of trees had also shade branches sampled (some trees had no shade branches available). From each branch, we measured five leaves from simple-leaved species, or five individual leaflets from compound-leaved species (both referred to as "leaf" below) for trait measurements. Branches and leaves with minimal damage were chosen. For this study, data are reported for only angiosperms, because the TER model described below is not necessarily applicable to gymnosperms and ferns with differing physiologies. However angiosperms do comprise the overwhelming majority of biomass and number of individuals across these plots  (Malhi et al.
Trait measurements Area shrinkage (S; m 2 m -2 ). -We calculated the fraction of leaf area that was lost upon drying (varying from 0 to 1). We used the supplementary data provided by  Blonder et al. (2012)  to calibrate a leaf-level shrinkage using a random forest regression model with leaf mass per area, fresh lamina area and leaf thickness as predictor variables. This model explained 46. 6% of the variation in the calibration data. On application to leaves from this dataset, it yielded shrinkage values of S = 0. 12 ± 0. 05 SD. Leaf thickness (d y ; mm). -We measured the thickness of each leaf in the field immediately after collection using a micrometer  (Tresna,. Measurements were taken on the lamina, avoiding primary, secondary, and tertiary veins. Vein density and intervein distance (VD, mm -1 ; IVD, mm).