dataesr/scientific-paragraphs-categorization huggingface dataset:
R-project. org/package=pheatmap) by calculating the Pearson correlation coefficient between samples. To assess the variability among samples, principle component analysis (PCA) was performed from read counts via the plotPCA function in R package DEseq2  (Love et al. 2014). The comparisons between the transcripts lists from four condition were conducted via the interactive tool Venny (http://bioinfogp. cnb. csic. es/tools/venny/index. html). Differential expression analysis between inoculated groups and mock inoculated groups was performed by DESeq2  (Love et al. 2014 ) using a model based on the negative binomial distribution and the resulting pvalues were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted p-value lower than 0.
striiformis f. sp. tritici, P. triticina, Melampsora larici-populina and the genome of the basidiomycete Laccaria bicolor, whose genomes are available in the JGI Mycocosm  (Grigoriev et al. 2013). In parallel, the unigenes were compared to 97,304 cleaned Pucciniales ESTs retrieved from dbEST at GenBank (blastn, e-value ≤ 10 -5 ). Unigenes showing homology in any of the searched databases were deemed as G. yamadae unigenes. The fungal unigenes were annotated using public protein databases, including the National Center for Biotechnology Information (NCBI) non-redundant protein and nucleotide (NR, NT) databases, Swiss-Prot, Eukaryotic Orthologous Groups (KOG), Gene ontology (GO), protein families (PFAM), KEGG Orthology (KO) using Blastx (e-value ≤ 10 -.
5 ). The proteomes of G. yamadae spermogonia and aecia stages were predicted from the corresponding unigenes using TransDecoder v3. 0. 1. Assignment of unigenes to rust fungi, unigene expression estimates and annotations were performed by Novogene (Beijing, China). A secretome prediction pipeline with a combination of bioinformatic tools, including SignalP v. 4, WolfPSort, TMHMM, TargetP and PS-Scan algorithms, was used to predict secreted proteins as described in  Pellegrin et al. (2015). Candidate CAZymes, proteases and lipases were annotated in the predicted secretomes using dbCAN v2. 0 HMM-based CAZy annotation server  (Zhang et al. 2018) , Merops  (Rawlings et al. 2016 ) and the Lipase Engineering database  (Fischer and Pleiss 2003) , respectively. The three proteomes predicted from G.
Reads of each sample were then mapped on the selected contigs using bowtie version 2. 3. 0  (Langmead and Salzberg 2012). Counts were determined using SAMtools version 1. 7  (Li et al. 2009 ). Contigs supported by less than 3 samples and less than 5 counts were discarded. The remaining contigs were annotated using a blast-like procedure using DIAMOND version 0. 9. 19  (Buchfink et al. 2015)  with the parameters --more sensitive --max-target-seqs 1 --max-hsps 1 --evalue 0. 00001 and JGI-Mycocosm (https://genome. jgi. doe. gov/mycocosm/home) predicted proteins from fungal genomes (deposited before July 2018) as a reference database. Diamond annotation was doubled using NCBI-NR (March 2018 version) to check for fungal false positives, based on the comparison between Mycocosm and NR best bit scores.
Spermogonia formed on the upper surface of apple leaves after 10 days post inoculation (dpi). The infected and control seedlings groups were unbagged and moved outdoors. After 30 dpi, aecia formed on the lower surface of apple leaves. Samples were collected from leaves at 10 and 30 dpi. Random leave pieces of similar surface than in infected conditions were collected for the mock-inoculated controls at both time points. Table  S1. Malus domestica transcripts expression in Gymnosporangium yamadae inoculated conditions and in mock-inoculated controls at 10 and 30 days post-inoculation (dpi) and assignment to metabolic pathways with subsequent enrichment analysis (MapMan and KEGG enrichment).
Sample Apple transcripts informations such as Gene_id, chromosome, Strand, Start, End, Length are provided. Read count and FPKM value for each gene were obtained for three biological replicates in four conditions (Inoculated_10dpi, Mock-inoculated_10dpi, Inoculated_30dpi, Mock-inoculated_30dpi) and differentially expressed levels between inoculated and mock-inoculated groups at 10 dpi and 30 dpi were evaluated with DESeq2 (adjusted p-value padj <0. 05). All significantly differentially expressed genes between inoculated and mock-inoculated groups were assigned into MapMan functional categories and KEGG pathways. KEGG pathways enrichment tests realised with KOBAS v2. 0 (hypergeometric test of KEGG pathways with Benjamini and Hochberg corrected p-values) are also presented. Table  S2. Gymnosporangium yamadae unigenes, predicted secreted proteins and identification of specific proteins at apple infection stages.
For spermogonia and aecia unigenes, Gene id, Gene length, expression information (read count and FPKM value) for each biological replicate and annotation results from seven public databases (NR, NT, KO, Swissprot, PFAM, GO, KOG) are presented. For the secreted proteins predicted in spermogonia and aecia, CPL_fam represent CAZymes, proteases and lipases families predicted in the secretome; per C is the percentage of cysteine content in each amino acid sequence. NLS means Nuclear Localisation Signal prediction. Proteins from spermogonia, aecia and telia were clustered by Markov Cluster Algorithm (MCL) to identify the proteins specific to spermogonia, aecia and apple infection (spermogonia and aecia) stages. Table  S3. Meta-transcriptomic analysis results. Illumina RNA-seq trimmed reads (Total reads) and the un-mapped reads after mapping reads against the Malus domestica reference genome using CLC Genomics Workbench 11.
This work was financed by the National Natural Science Foundation of China (No. 31870628). The authors acknowledge the financial support from China Scholarship Council (No. 201806510009) for the Joint PhD Program between Beijing Forestry University and INRA-Nancy. SD is supported by the French 'Investissements d'Avenir' program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Y. M. Liang and S. Duplessis designed this research. S. Q. Tao performed the experiments. L. Auer and E.
Stability half-life was calculated from the ratio of full-length (FL) product produced relative to the total amount of +4-lock + FL product at each timepoint. Half-life was obtained by fitting the data through a single exponential. Pre-steady state quenched-flow kinetics. Experiments were carried out in a Bio-Logic QFM-300 rapid chemical quench-flow apparatus that controls reaction time by flow rate through a 2. 5 μL chamber between the reaction mixer and the quencher mixer. The nsp12-nsp7L8-nsp8 complex was assembled by first preincubating the proteins on ice for 10 min at a 1:3:3 molar ratio, then adding this to SP10/20 and HP8-29 hairpin RNA in reaction buffer (20 mM HEPES pH 7. 5, 5 mM MgCl 2 , 50 mM NaCl) and further incubated at room temperature for 15 min prior to beginning the rapid-quench experiments, which took ~10 min to complete a set of 22 different reaction times.
The intensity of each band was quantified using the ImageQuant software (GE Healthcare)/Image Gauge (Fuji) and/or using ImageJ as implemented in the Fiji package, with background subtraction. Product yield was determined by dividing the intensity of the product by total intensity of the product + remaining primer and multiplying by the input concentration of RNA. For the rapid quench data, the programme PeakFit (Systat Software) was used to fit gel lane profiles to a set of gaussian peaks and the fractional area contained within each peak was multiplied by the RNA concentration in the experiment to calculate the amount of each elongation species as a function of reaction time. Pre-steady state kinetic data analysis. Rapid quench data were analysed using the programme KinTek Explorer  45  to model the reactions as a series of seven (PT substrate) or eight (HP substrate) irreversible nucleotide addition steps.
Antiviral experiments data were analysed using GraphPad Prism 7 software (Graph pad software). Graphical representations were also performed on GraphPad Prism 7 software. Sequence analysis. Eight overlapping amplicons were produced from the extracted viral RNA using the SuperScript IV One-Step RT-PCR System (Thermo Fisher Scientific) and specific primers (Supplementary Table  4 ). PCR products were pooled at equimolar proportions. After Qubit quantification using Qubit® dsDNA HS Assay Kit and Qubit 2. 0 fluorometer (ThermoFisher Scientific) amplicons were fragmented by sonication in ~200 bp long fragments. Libraries were built adding barcodes for sample identification to the fragmented DNA using AB Library Builder System (ThermoFisher Scientific). To pool the barcoded samples at equimolar ratio a quantification step by real-time PCR using Ion Library TaqMan™ Quantitation Kit (Thermo Fisher Scientific) was realised.
An emulsion PCR of the pools and loading on 530 chip was realised using the automated Ion Chef instrument (ThermoFisher). Sequencing was performed using the S5 Ion torrent technology v5. 12 (Thermo Fisher Scientific) following manufacturer's instructions. Consensus sequence was obtained after trimming of reads (reads with quality score <0. 99, and length <100 pb were removed and the 30 first and 30 last nucleotides were removed from the reads) mapping of the reads on a reference (determined following Blast of De Novo contigs) using CLC genomics workbench software v. 20 (Qiagen). A de novo contig was also produced to ensure that the consensus sequence was not affected by the reference sequence. Quasi species with frequency over 0. 1% were studied. Sequencing data generated for this study are available (SRA accession number: PRJNA633443; GenBank accession numbers are MT594401 and MT594402).
Acknowledgements We thank  Magali Gilles  and  Karine Alvarez  for excellent technical support, as well as Prs. C. Drosten  and  F. Drexler  for providing the SARS-CoV-2 through EVA-GLOBAL (European Union's  Horizon 2020 programme ,  GA 871029 ). This work was supported by the  Fondation pour la Recherche Médicale (Aide aux équipes) , the  SCORE  project  H2020 SC1-PHE-Coronavirus-2020  (grant# 101003627 ) to BCa, REACTing Covid-19 initiative (REsearch and ACTion targeting emerging infectious diseases) with the support of the  Ministry of Solidarity and Health  and the  Ministry of Higher Eductation  to BCa, ED and  BCo, National Institutes of Health  grant  AI059130  to OP, and a grant from  DZIF (German Center for Infection Research)  to J. H. and C. M.
In a departure from the practices of that era, the data from these floats would be freely disseminated in real-time, allowing use in operational ocean and atmospheric models. The data would be further quality-controlled, and this "delayed-mode" version would also be shared freely with the scientific community. It was recognized that Argo would require an international collaboration similar to that developed by WOCE. The floats would be deployed by separate groups from participating countries, but the data would be shared internationally. The standard Argo mission is known as "park-and-profile" (Figure  1 ). The floats park at a target pressure of 1,000 decibars and drift with the ocean currents. Pressure in decibars (dbar; 1 dbar = 10,000 Pa) is approximately equal to depth in meters. The Argo park level of 1,000 dbar was chosen to extend the absolute velocity database collected during WOCE, which employed that level based on its favorable signal-to-noise ratio.
Every 10 days the floats descend to 2,000 dbar and then collect a vertical profile of temperature and salinity during ascent to the surface. The positions of the floats at the sea surface are determined by orbiting platforms, and the data are transmitted via satellite back to shore. The floats then return to their target park pressure and the cycle is repeated. Deployments of Argo floats began in 1999, and the 3,000-float goal was reached in November 2007. Argo collected its one-millionth profile in October 2012 and its two-millionth profile in September 2018. This paper describes the pressure (P), temperature (T), salinity (S), and subsurface velocity data from the Argo Program: the instrumentation used, the technical problems encountered, the scientific quality of the data, the data distribution system, and how the dataset has evolved in response to new technologies.
It has been over 20 years since the first deployment of Argo floats in 1999. This has been a long journey for the scientists who first conceived the Argo array, and yet it is but a short step toward the goal of sustaining a comprehensive global ocean observation system. This paper therefore serves the dual purpose of documenting the characteristics and accuracy of the core Argo dataset from its inception to 2019, as well as foretelling the expansion of this global ocean dataset into 2020 and beyond.
Components of the Argo Data System The initial design of the Argo data system took place in 2001 at the 1st Argo Data Management meeting at Ifremer in Brest, France. The main components of the initial system have generally continued to function well 20 years later (Figure  4 ). The Argo data system was a descendent of the WOCE Upper Ocean Thermal (UOT) Data Centers. Shortcomings of the WOCE UOT dataset, especially the lack of metadata, limited its application and were recognized and addressed in the design of the Argo data system. The data system was designed to serve the twin requirements of A file checker operated at the GDACs checks the format and content consistency of these data files before they are admitted into the global data holdings  (Ignaszewski, 2018). Moreover, for each float, the GDACs concatenate the single-cycle profile files together to make a multi-cycle profile file for each float, for users who require all profiles from each float in one file.
Delayed-Mode In the delayed-mode process, data are subjected to visual examination by oceanographic experts and are re-flagged where necessary, as the real-time automatic procedures are not flawless. Float data can also be affected by sensor drift, but because retrieving floats for recalibration is rarely possible, statistical tools and climatological comparisons are used to adjust the data for sensor drift when needed. Determination of sensor drift requires accumulation of a relatively long time series. In Argo, the usual practice is to examine the profiles in delayed-mode initially about 12 months after they are collected, and then revisit several times as more data from the floats are obtained, until the floats become inactive. Thus, the most recent version of the global dataset should be used whenever possible to take advantage of these activities.
This aids in the identification of sensor drift at an early stage. Results of these global analyses are sent to the DACs regularly, where the anomalies are flagged or adjusted by expert examination. Since 2013, regional reanalysis of delayed-mode salinity data has been performed regularly at Coriolis. For each float that has been processed in delayed-mode, the OWC method  (Owens and Wong, 2009; Cabanes et al. , 2016)  is run with four different sets of spatial and temporal decorrelation scales and the latest available reference dataset. If the salinity adjustments obtained from the four runs all differ significantly from the existing adjustment, then the salinity data from the float are re-examined and a new adjustment is suggested if necessary. This step has been proven to be effective in increasing consistency of delayed-mode salinity adjustments for floats in the North Atlantic Ocean.
Some APF8 controller boards were updated specifically to remove this "truncating" feature. All later series of controller boards on APEX floats, APF9 and above, return raw SP values with no truncation of negative values. Another pressure problem that has affected Argo data results from processing errors onboard the floats. In 2007, it was discovered that some SOLO floats from the Woods Hole Oceanographic Institution (designated as SOLO-W) returned incorrect pressure values because of a bin-average error in the firmware. As a result, profile data from these SOLO-W floats are offset upward by one or more pressure levels, resulting in a cold bias at depth from these instruments  (Willis et al. , 2009). Data from the affected instruments have been identified and flagged as bad in the Argo dataset, and account for about 1% of all Argo CTD profiles.
Sensor Response Correction Attaining the most accurate salinity from conductivity, temperature, and pressure measurements requires considerable processing and a number of corrections for various sensor response issues (e. g. ,  McTaggart et al. , 2010). For 1 Hz or more frequently sampled data, the mismatch between the 0. 5 s response time of the SBE-41CP thermistor and the faster response of the conductivity cell must be taken into account. The combined effect of the difference in sampling time between conductivity and temperature by the CTD, plus the time required for water to flow from the thermistor into the cell must also be accounted for (e. g. ,  Johnson et al. , 2007; Martini et al. , 2019). However, for bin-averaged data (on order of 10 s per dbar) or spot-sampled data, these adjustments, which amount to fractions of a second, are not possible.
Since conductivity is a strong function of temperature, the temperature of the water in the cell must be estimated (and used) to attain an unbiased salinity measurement. Although the most obvious manifestation of this error is a "spike" at the base of the mixed layer, this error, left uncorrected, also causes a bias in the thermocline, and can exceed 0. 01 PSS-78 in some cases. The conductivity cell thermal mass error can be corrected in a statistical sense, in spot-sampled data and 2-dbar bin-averaged data, assuming the temperature gradients are well-characterized at the telemetered data resolution, if the ascent rate of the float is known  (Johnson et al. , 2007). The correction coefficients depend on the CTD type, with different coefficients for the SBE-41 and the SBE-41CP because of their different pumping strategies.
Long-Term Sensor Stability The long-term stability of float salinity data is evaluated in delayed-mode by comparing time series of data from each float with nearby high-quality reference data on potential temperature surfaces. The differences between float-measured and reference values over several years are treated by statistical methods and represented by a piecewise linear fit to discern any observable trends over time  (Wong et al. , 2003; Bohme and Send, 2005; Owens and Wong, 2009; Cabanes et al. , 2016). The observed trends are then evaluated by oceanographic experts to determine whether they are due to sensor drift or due to ocean variability. If the observed trends are determined to be due to sensor drift, then the salinity data from the affected floats are adjusted to the reference data according to the piecewise linear fit over time.
The salinity adjustment is computed as a multiplicative correction in potential conductivity, which is equivalent to an additive correction in salinity, with slight variations as a function of pressure due to the non-linearity of the equation of state for seawater. This model assumes that the changes in reported salinities are due to changes in the measurement volume of the conductivity cell  (Lueck, 1990). In practice, this model works well for any salinity sensor drifts that can be adjusted with an offset correction with no significant vertical variations. The effectiveness of this statistical method relies on availability of contemporaneous reference data and/or the existence of water masses that have stable temperature-salinity characteristics for comparison. In Argo delayed-mode salinity analysis, two separate reference databases are used: a first one that is based on shipboard CTD data, and a second one that is based on Argo profiles that have been judged as accurate and needing no adjustment.
Both databases are updated periodically to include more recentlyacquired reference data, so as to account for temporal changes in the global ocean. Analysis of delayed-mode salinity data from 10,048 Argo floats showed that for the first 2 years after deployment (about 72 cycles), < 10% of the floats required any kind of sensor drift adjustment (Figure  10 ). After 280 cycles, about 40% required salinity adjustments > 0. 01 PSS-78 in magnitude, while 30% required adjustments > 0. 02 PSS-78 in magnitude. With adequate reference data and stable water masses, the statistical technique used in delayed-mode can usually produce adjusted float salinity data with about 0. 01 PSS-78 uncertainty. In most cases, when the magnitude of sensor drift exceeds 0. 05 PSS-78, the data will become erratic or will exhibit significant vertical variations in the amount of sensor drift.
Many of these SBE CTDs were still active as of the time of this writing and, as a result, a higher than normal portion of Argo real-time salinity data were subject to errors that were larger than 0. 01 PSS-78. The best estimate was that, at the time of this writing, about 25% of real-time profiles might be subject to this salinity error. In the real-time data stream, the Argo national DACs have flagged salinity from the SBE CTDs in the serial number range 6000-7100 as questionable data. In the delayed-mode data stream, the adjustment of these affected SBE CTDs has been treated with high priority. As a result, the residual salinity bias in the Argo dataset due to this sensor drift is now small. The cause of this conductivity drift is presently still under investigation.
Assessment of Argo Pressure and Salinity Bias Against GO-SHIP Shipboard CTD systems, used with water sampling bottle salinities and recently calibrated sensor sets, deliver the highest possible accuracy data presently available. Here we have attempted to quantify any possible bias in Argo pressure and salinity by comparison with data from the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP). GO-SHIP grew out of the WOCE Hydrographic Program and adopted and built upon WOCE data standards. CTD data from 280 post-2000 GO-SHIP cruises were selected. Nearby Argo and GO-SHIP profile buddies, or pairs, were defined as profiles deeper than 1,300 dbar that were collected within 300 km and 30 days of each other. Individual Argo and GO-SHIP profiles could (and typically did) appear in multiple pairs, but each pairing was unique.
In total, 294,373 Argo/GO-SHIP pairs were found with these search criteria, involving 31,056 unique Argo profiles. Only the highest-quality data have been selected (WOCE QC flag "2"; Argo QC flag "1"). The Argo profiles consist of both real-time profiles and delayed-mode profiles. Argo/GO-SHIP differences were then examined on a density surface: specifically, potential density anomalies relative to 1,000 dbar (σ 1 ). In order to account for the wide variation in σ 1 stratification across latitudes, a separate σ 1 grid was defined for each 20 • latitude band (-90 • to -70 • , -70 • to -50 • , etc. ). These σ 1 grids were determined by averaging GO-SHIP σ 1 profiles in each latitude band and on 10-dbar pressure levels from the surface to 2,000 dbar. Salinity and pressure from each Argo/GO-SHIP profile pair were then interpolated onto the respective σ 1 grid based on their locations.