dataesr/scientific-paragraphs-categorization huggingface dataset:
This result showed that both EVs-lncRNAs and tumor lncRNA have similar capacities (and proportion) to encode high affinity binding to MHC. In conclusion, among the uEVs-enriched lncRNAs, we identified 15 transcripts (called EVs-neoLncRNA hereafter) that could potentially serve as templates for the translation of 768 peptides with potent neoantigens properties, if internalized and translated into the host cells. To define the translation capacities of the 15 uEVs-neolncRNA, we first quantified their expression in PC3, DU145 and LNCaP PCa cell lines and corresponding cEVs. As shown in the heatmap, all the 15 EVs-neolncRNA are expressed, at least in one of the 3 cell lines, at lower but comparable levels as the POLR2A mRNA used here as a moderate expressed control (Fig  5c ). We then re-analyzed a public ribosome profiling dataset for the PC3 cell line, to determine ribosome occupancy on the 15 EVs-neolncRNAs  45.
Extracellular vesicles characterization Characterization, particle number and size, of the EVs-enriched pellets was performed using nanoparticle tracking analysis (NTA) using a ZetaView PMX-120 video microscope (Particle Metrix) equipped with a 488 nm laser and the software Zeta View version 8. 05. 10. For optimal measurements, samples were diluted with PBS until particle concentration was within the optimal concentration range for particle analysis. Experiments were performed briefly as follows: the instrument was set at 25°C, sensitivity of 70 and shutter of 75. Measurements were done at 11 different positions (5 cycles per position) and frame rate of 30 frames per second. 55 seconds videos were recorded with a resolution of 0. 714μm/px). Then, the software tracks the brownian motion of individual vesicles, visualized by light scattering, and calculates their size and total concentration with corresponding standard error.
Bioinformatics analysis -RNA-seq reads quality control and alignment for FFPE and uEVs: The Curie bioinformatic platform performed the quality control of the RNA-seq data using MultiQC, and the alignement of the reads on the human genome hg38 was done using STAR 2. 6. 1a, with the following parameters : --outMultimapperOrder Random --outSAMtype BAM Unsorted --outSAMattributes All --outSAMprimaryFlagOneBestScore --outSAMmultNmax 1 --outFilterTypeBySJout --outFilterMultimapNmax 20 --outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0. 04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 [37] [38]. -Differential expression analysis of FFPE and uEVs: Read counting was performed for each sample on the human gene annotation gencode v32 (https://www.
gencodegenes. org/human/release_32. html) and on the human repeats (http://www. repeatmasker. org/species/hg. html), using Kallisto 0. 46. 1 with the parameter --rf-stranded [39]. The Kallisto index was built with the extracted sequences from the human genome hg38, using the annotations and the getfasta command of BEDTools 2. 29 (for gencode 32, before the extraction, the exons of all the transcripts of each gene were merged by location, in order to obtain directly the counts at the gene level) [40]. The tool CIRIquant 1. 1 was used on the samples with default parameters to discover the location of circRNAs and to quantify them  59. The results (counts from human genes, repeats and circRNAs) were concatenated. The conditions FFPE and uEVs were compared using DESeq2, with the following parameters: betaPrior=FALSE, independentFiltering=F, cooksCutoff=F.
Only the features with adjusted p-value <= 0. 05,abs(log2FoldChange)>= 0. 585, and normalized counts >= 20 in at least one sample were retained as differentially expressed. Heatmaps of expression were obained using the R package ComplexHeatmap from bioconductor. -Read count genomic distribution: We created from the human gene annotation gencode32 (https://www. gencodegenes. org/human/release_32. html) 6 classes of genomic features (exon, intron, 5'-UTR, 3'-UTR, promoter, intergenic), as follows: The exons from all the transcripts of each gene were merged by location, in order to have non-redundant segments of exons, using the merge command of BEDTools 2. 29. Introns were inferred from these exons using the R package GenomicFeatures from bioconductor. The 5' and 3' UTRs from gencode32 were merged and were subtracted from the previous features.
Promoters were inferred by using a distance of 1kb upstream of the first exon (when possible), in respect of the strand. Intergenic parts were constructed after the concatenation of all the previous features, using the complement command of bedtools. Strand-specific counting from the alignment files was performed on these features using featureCounts of the Subread package (https://sourceforge. net/projects/subread/files/) [42]. The priority order when the counts were on several features was the following : exon > UTRs > intron > promoter > intergenic. -Exon-intron read count ratio: The exons from all the transcripts of each gene of the human gene annotation gencode32 (https://www. gencodegenes. org/human/release_32. html) were merged by location using the merge command of BEDTools 2. 29, in ordrer to have non-redundant segments of exons.
Introns were inferred from these exons using the R package GenomicFeatures from bioconductor. Genes with no introns were discarded. The alignment files were converted to BEDPE format and were intersected with the new formed annotation to determine their status (exonic or intronic), using the count command of BEDTools 2. 29. The number of exonic and intronic counts for each gene was then determined, using the same command. The counts were normalized using the length of each feature, and the ratio exonic count over intronic count was computed (genes that had 0 count on numerator and denominator before calculation were discarded. When just a part is equal to 0, +1 is added to both parts to avoid division by 0). The average ratio for each gene was computed for each condition, and the results were plotted using the R package ggplot2.
-Metagenes: To select the exons and introns features, we have created firstly metatrancripts as follows: The exons from all the transcripts of each gene were merged by location using the merge command of BEDTools 2. 29, to have non-redundant segments of exons, and these exons were numbered; Introns were inferred from these exons. The first exon, first intron, second exon, last intron and last exon were selected for each gene having at least 1 raw read count (read count from Kallisto). The alignment files were converted in BigWig files with RPM normalization (reads per million of mapped reads), using UCSC tools (http://hgdownload. cse. ucsc. edu/admin/exe/linux. x86_64/), and for each part and each gene, the read coverage was extracted. The obtained signal was scaled on 100 positions using the R base approx function (allows to compute interpolations), with the following parameters: method="linear",ties="ordered".
The average, minimum, and maximum values were computed at each of the 100 positions, and the result was plotted using the R package ggplot2. -Neoantigen analysis : HLA allotypes from FFPE and uEVs samples were determined using the tool seq2HLA. Upregulated lncRNAs from FFPE &uEVs were selected, and a research of the ORFs (start codon = AUG) of the most abundant transcript for each gene was performed using TransDecoder (https://transdecoder. github. io/) and the R bioconductor package ORFik https://github. com/Roleren/ORFik. The translation into peptides from the 3 frames was performed using the SeqinR R package  60  , and only the ones with a length >= 8 amino acids were kept. The list of peptides for FFPE and uEVs was given to the tool netMHCpan-4. 1 in order to find neoantigens. Only the peptides with an elution ligand rank <= 2 were kept for downstream analysis.
ACKNOWLEDGEMENTS We deeply thank all the members of our labs for discussions and critical reading of the manuscript. Daniel Gautheret  for his constant encouragement and critical readings. Pascale Soyeux  for technical help on FFPE manipulation. The platform of biological resources (PRB) of  Henri Mondor  hospital for tissues collection. We also would like to thank  Benoît Albaud ,  Sonia Lameiras ,  Patricia Legoix-Né ,  Virginie Raynal , and  Sylvain Baulande  from the  Next Generation Sequencing (NGS) platform of Institut Curie  for discussions and expertise. RNA-seq sequencing efforts were supported by the grant from the  ICGex program of Institut Curie  attributed to  A. M. Marc Gabriel  and  Matthieu Lejars  and this project were supported by the  European Research Council  ( 875532-PROSTATOR-ERC-2019-PoC ), CARNOT-2018-PROSTATOR, 17 MECENAT, attributed to A.
The chosen regions are those where the daily death incidence had reached 10 deaths at least once as of 23 rd April 2020 according to the John Hopkins Coronavirus Resource Center database. We fit the model by maximum likelihood to the case and death count data assuming the data points each day are drawn in a negative binomial distribution with mean given by the model prediction, and with an inferred dispersion parameter. The maximum-likelihood model generally fitted the data very closely (Fig. 2  for a few example regions, Supplementary Fig. 2  for all regions). We validate our projections by comparing the inferred total attack rates-the proportion of individuals in the population that have ever been infected at a given date-with the number of infected individuals in twelve regions where the number of infected at a certain time is known by systematic survey on a representative sample (Supplementary Table  1 ).
Likelihood method To fit the model and infer transmission and case detection parameters, we use data on the number of confirmed cases over time and the number of deaths over time in 79 states and countries from different public sources detailed below. We include all states and countries that had a daily incidence of 10 deaths or more at least once as of 23th April. As we want to estimate the impact of sudden social distancing measures in an essentially uncontrolled epidemic, we exclude South Korea and Japan from the analysis. In these two countries, SARS-CoV-2 was introduced earlier and strong control measures including social distancing and contact tracing were immediately in place. The likelihood of the model is defined as in other epidemiological studies  (Salje et al. , 2020; Flaxman et al.
, 2020). Simulating the deterministic model gives the expected number of detected cases C(t) and deaths D(t) at time t as a function of model parameters. We assume that the probability to observe a certain number of cases (resp. deaths) in the data at day t is the density of a negative binomial distribution with mean given by the theoretical predictions for cases (resp. deaths), and dispersion parameters that we infer. The likelihood for cases (resp. deaths) is the product of these probabilities over all days. The overall likelihood is the product of the likelihood of cases and the likelihood of deaths. For the number of deaths, we include the period from the first day to the last day when at least 1 death and 5 cases were recorded. For the number of cases, we include the period from the first day to the last day when at least 5 cases were recorded.
Data sources Epidemiological data For France, we used data from OpenCOVID19 available at https://g ithub. com/opencovid19-fr/data. This website curates data from Agence nationale de santé publique, the French governmental public health agency. For Italy, we used data from the Civil Protection Department (Dipartimento della Protezione Civile), available at https://github. com/pcm-dpc/COVID-19. This data includes daily cases and deaths, and daily number of tests. For other European countries, we used data from the European Center for Disease Control (ECDC) available at https://opendata. ecdc. europa. eu/covid19/casedistribution/ For American states, we used data from the COVID Tracking Project that compiles data from American official sources, available at https:// covidtracking. com/api/v1/states/daily.
csv. This data includes daily cases and deaths, and daily number of tests. For other countries, we used data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), available at https://github. com/CSSEGISandData/COVID-19 Daily number of tests data for regions other than Italy and American states were compiled from Our World in Data at https://covid. ourworld indata. org/data/owid-covid-data. csv We considered test data only for regions for which the number of tests was strictly superior to the number of cases recorded for at least 80 % of the days. The reported number of tests are sometimes exactly equal to the number of cases that day, indicating that negative tests are not reported. Since we ignore whether negative tests are not reported or reported at a later date (as sometimes suggested by a peak in the number of reported tests a few days after), we exclude these datapoints and exclude regions where this artefact is often observed.
Acknowledgements We thank  Florence Débarre  and  Chris Wymant  for helpful comments. We thank the many people involved in the collection and curation of the epidemiological data that we use. F. B. was supported by a  Momentum grant  from the  CNRS. A. B was supported by a scholarship from  Ecole Polytechnique. We are grateful to the  INRA MIGALE bioinformatics facility (MIGALE, INRA, 2018. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing computational resources.
Part 2: Model application to high-risk nosocomial pathogens We applied this modeling framework to simulate colonization dynamics of four nosocomial pathogens in the hospital setting: C. difficile, methicillin-resistant S. aureus (MRSA), extended-spectrum beta-lactamase-producing E. coli (ESBL-EC), and carbapenemase-producing K. pneumoniae (CP-KP). Data from the literature were used to parameterize the model to each pathogen (Appendix 1-figure 5, Appendix 1-table 2-6). Literature estimates for microbiome-pathogen interaction coefficients are scarce, and the species-specific relevance of different within-host interactions (intraspecific strain competition, microbiome competition, HGT) in the hospital environment are not well-defined. To characterize ecological interactions for each species and inform model structure, we conducted interviews with a panel of subject-matter experts in medical microbiology and antibiotic resistance epidemiology (details in Materials and methods).
Colonization dynamics for each ARB were modeled against a common backdrop of antibiotic consumption. This was parameterized at the level of antibiotic class, using national data from French hospitals in 2016 (Appendix 1-table 8; Agence nationale de securitedu medicament et des produits de sante, 2017). Using data from the literature, antibiotic classes were assumed to vary in their impact on microbiome dysbiosis (very low, low, medium, or high rate of inducing dysbiosis) and on each pathogen strain (classified as sensitive, intermediate, or resistant) (Figure  5C  and D;  Baggs et al. , 2018; Brown et al. , 2013; McCormack and Lalji, 2015). For each of 10,000 Monte Carlo simulations, in which parameter values were sampled randomly from their respective probability distributions, epidemiological outcomes were evaluated at population dynamic equilibrium.
Mathematical models of bacterial colonization We evaluated ODE systems describing colonization dynamics of bacterial pathogens in the healthcare setting. The final model, comprising pathogen colonization (Equation  1 ), intraspecific strain competition (Equation  3 ), microbiome-pathogen competition (Equation  4 ), and horizontal gene transfer (HGT), is given alongside all assumptions in the supplementary appendix (Appendix Equation A1). ODEs were integrated numerically to calculate steady-state epidemiological outcomes for nosocomial P R colonization: colonization prevalence (the sum of all compartments C R ), colonization incidence (the daily rate of C R acquisition), and the resistance rate (C R /(C S + C R )). (See Appendix 1 for technical details, and R and Mathematica files available online at https://github.
If yes, they were asked to quantify the relative impact of dysbiosis on that outcome i for each ARB j as a histogram (estimated using MATCH) given by a vector x i,j,k for each expert k. Experts made estimates for each ARB consecutively, such that parameters for each ARB were estimated relative to other included ARB. Raw data for expert distributions are provided in Appendix 1-figure  9. Experts differed substantially in estimated ranges of different parameters, but Friedman's tests using medians of each parameter distribution suggested that the species rank order was conserved across experts, with C. difficile generally having the strongest estimated microbiome-pathogen interaction coefficients, and MRSA the weakest. To conserve species rank order when combining expert estimates x i,j,k to form pooled histograms, distributions were re-centred by the mean relative distance z i,j between each j and the reference ARB (taken as MRSA) over all experts k (Appendix 1-figure  10 ).
For C. difficile and MRSA, HGT was excluded from simulations and pooled distributions were not re-centred. Each expert distribution was weighted equally, contributing to 10% of the final pooled distribution for each species and parameter. Pooled histograms were fit to six candidate distributions (normal, lognormal, Weibull, Cauchy, exponential and gamma), and the final distribution was selected as the fitted distribution giving the lowest Akaike Information Criterion (using the function fitdist from the R package fitdistrplus). Final fitted distributions for each parameter and ARB are given in Appendix 1table 3-6, and for select parameters in Figure  5. Among other observations, experts highlighted (i) uncertainty about an influence of strain competition on hospital colonization dynamics for all ARB, with the majority of experts believing strain competition was irrelevant for C.
Acknowledgements We express our gratitude to the subject-matter experts who participated in our expert elicitation exercise:  Antoine Andremont ,  Christian Brun-Buisson ,  Aurelien Dinh ,  Stephan Harbarth ,  Jean-Louis Herrmann ,  Solen Kerne  ´is,  Alban Le Monnier ,  Benoı ˆt Pilmis ,  Etienne Ruppe  ´and  Paul-Louis Woerther. The work was supported directly by internal resources from the  French National Institute for Health and Medical Research , the  Institut Pasteur , the  Conservatoire National des Arts et Metiers , and the  University of Versailles-Saint-Quentin-en-Yvelines / University of Paris-Saclay. This study received funding from the  French Government 's ' Investissement d'Avenir' program ,  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (Grant  ANR-10-LABX-62-IBEID ).
Results and discussion Genetic mapping of a coconut bi-parental population by GBS. In order to assign the coconut sequences to their positions on coconut chromosomes, we created a linkage map, based on a back-cross MYD × (MYD × WAT) produced and planted in Côte d'Ivoire (WAT stands for the West African Tall and MYD for the Malayan Yellow Dwarf). After DNA extraction, a legitimacy test involving fifteen microsatellite markers was performed on 320 progenies and confirmed the origin of 292 progenies, which were genotyped by GBS, along with the parents. We obtained GBS sequences from 240 progenies. The 9,459,318 variants assigned to 7904 scaffolds were initially identified by TASSEL-GBS  33  pipeline and VcfHunter package  34. Several filtration steps (see "Methods") retained 8402 SNPs with less than 20% missing data per marker, assigned to 2303 scaffolds, and scored on 216 progenies with <25% missing data.
Based on recombination distances, Joinmap 35 identified 16 linkage groups, corresponding to the number of chromosome pairs in coconut. Only one of them (Cn15) was made up of two weakly linked subgroups (see below). Software Scaffhunter 36 was used to order markers within linkage groups while preserving scaffold integrity. We could also verify that appreciable linkage occurred only within the linkage groups identified by Joinmap (Supplementary Fig. S2 ). After genotyping error correction, the estimated chromosome length after error correction varied from 81 cM (Cn13) to 225 cM (Cn06), yielding a total length of 2365 cM. The number of markers per chromosome varied from 244 (Cn15) to 904 (Cn01), achieving a dense coverage of most of the coconut genome (Fig. 1a ). In average, the marker density was 8.
We used the orthology relationships between coconut and oil palm to identify the same blocks in the coconut genome. In total, 9866 orthology relationships between coconut and oil palm allowed identifying these ancestral blocks in the coconut genome (Supplementary Data 2). Out of the 2666 coconut paralogous pairs, oil palm orthologous genes were retrieved for both members in 889 cases, confirming block orthology in all cases but two. Combining automatic analysis with the MGRA  50  software and manual curation, we propose an ancestral genome pre-p of 9 chromosomes made of 13 duplicated blocks (from A to M) in coconut as follows: (1) M, (2) A, (3) G, (4) L, (5) C, (6) EF, (7) JK, (8) BD and (  9 ) HI (Supplementary Fig. S6 ). This corresponds to "scenario 2" proposed in Figure  10 of Armero' s PhD thesis  51.
The LTR insertion times were calculated in C. nucifera and Phoenix dactylifera using the LTR_STRUC program  54  and their distributions are shown in Fig. 3b. In coconut, the insertion rate was low as of 6 million years ago, increased gradually, and reached a peak at 0. 5-1 Mya, suggesting that the expansion of the coconut genome was quite recent, in comparison with the age of the Arecaceae family. In addition to the differences in LTR insertion rates, their natures differ: in coconut, 67% of LTR retrotransposons in the insertion peak are in the Copia subfamily, while the Gypsy subfamily dominates in date palm, representing 61% of the LTRs (Fig. 3c, d ). While the CATD sequence  26  appears to exhibit a smaller proportion of LTRs and a lower Copia/Gypsy ratio, the chronology of the LTR invasion is very similar, at least if we rely on the Ks values.
Signaling pathways involved in salt-stress response. As noted above, coconut uses the ocean currents to disseminate its seeds and its natural habitat consists of seashores and estuaries where freshwater and seawater mix. This means that it must be adapted to a variable, but often high water salinity. In order to understand the response of coconut to salt-induced stress, we submitted young plants from two varieties (Hainan tall and Aromatic dwarf) to a salt-stress experiment. We collected leaf RNA samples from tall and dwarf coconut genotypes at the following times: 0 h, 4 h, 6 days, and 10 days after saltwater application. RNA was extracted and sequenced. Incidentally, this allowed crossvalidation of the quality of the draft sequence and of the transcriptome by aligning the clean RNA-seq reads onto both the scaffold reference and the annotated gene reference (see Supplementary Data 4 and 5).
Parents were replicated to ensure SNP detection and high-quality parental information for the estimation of marker segregation types. The MYD parent was included as a control in all batches, while P994 hybrid parent was only present in the batches sequenced in May. In total, they were repeated six and ten times, respectively. Based on sequencing statistics, 240 progenies were retained for bioinformatics analysis. Five fastq files were produced from the three batches (6 plates in total) and analyzed with TASSEL-GBS 33 pipeline V5. 2. 29 (see Supplementary Fig. S12  and Supplementary Table  S1 ) on the Cirad HPC data center of the South Green Bioinformatics platform resulting in 631,283 SNP variants assigned to 7903 scaffolds (VCF file 9× read depth). In an effort to enrich the map with large scaffolds, the allele calling was repeated, targeting only 368 of the largest scaffolds as yet unmarked and using the Process-Reseq pipeline.