dataesr/scientific-paragraphs-categorization huggingface dataset:
Reads were then mapped onto the mm10 assembly of Mus musculus genome using STAR version 2. 7. 0f  42. Gene expression quantification was performed from uniquely aligned reads using htseq-count version 0. 9. 1  43  , with annotations from Ensembl version 96 and "union" mode. Read counts were normalized across samples with the median-of-ratios method  44  , to make these counts comparable between samples and differential gene analysis were performed using the DESeq2 version 1. 22. 2  45. All the figures were generated using R software version 3. 5. 3.
Repeat element analyses Data were processed as already described  46  using Bowtie1 47 instead of Maq. The repeatMasker annotation was used to identified the different types of repeat elements (Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-4. 0. 2013-2015 http://www. repeatmasker. org). Differential expression analyses were performed using DESeq2 (version 1. 22. 2)  45. All the figures were generated using R custom scripts (version 3. 5. 3).
8 x AMPure beads to sample ratio). The libraries were sequenced using HiSeq2500 Illumina platform in single-end, 50 bp mode (Genomics Facility, MRC, LMS). Sequenced SLIC-CAGE reads were mapped to the reference M. musculus genome (mm10 assembly) using the Bowtie2 41 with parameters that allow zero mismatches per seed sequence (22 nucleotides). Uniquely mapped reads were kept for downstream analyses using CAGEr Bioconductor package (version 1. 20. 0)  48 and custom R/Bioconductor scripts. Bam files were imported into R using the CAGEr package, where the mismatching additional G, if added through the template-free activity of the reverse transcriptase, was removed. Same samples sequenced on different lanes and biological replicates were merged prior to final analyses.
Promoter analyses In order to consider only the CAGE TSS dependent only on TBPL2, we removed all the P14 WT CAGE tags at position where CAGE tags were also present in the P14 Tbpl2 -/-mutant CAGE tags dataset: for the rest of the analysis, this dataset was called "TBPL2-specific-only" and we compared it to the P14 Tbpl2 -/-mutant CAGE data (hereafter called "TBPL2independent-only"). Briefly, a CAGE set object was created from the TBPL2-specific-only and TBPL2-independent-only CTSS files using CAGEr Bioconductor package (version 1. 20. 0)  48 , data were normalized using normalizeTagCount (fitInRange = c(5,1000), alpha = 1. 53, T = 1e6) and the powerLaw option. Cluster of CTSS were collected using clusterCTSS (threshold = 1, thresholdIsTpm = TRUE, nrPassThreshold = 1, method = "distclu", maxDist = 20, removeSingletons = TRUE, keepSingletonsAbove = 5).
01). TSSs corresponding to the MaLR-ERVS were identified after annotation using HOMER (version 4. 10)  49. Sequences analyses were performed using seqPattern (version 1. 14){Haberle:co} and R custom scripts. WW dinucleotides enrichment was computed using plotPatternDensityMap on -250/+250 regions centered on the dominant TSSs. TATA box position weight matrix (PWM) matches analyses was performed using the MotifScanScores function applied on the -35/-20 sequences centered on the dominant TSSs, using the TBP PWM provided in the SeqPattern package (derived from the JASPAR data base). Distribution of the best match for each sequence was then plotted. Sequence Logo were created using SeqLogo (version 1. 48. 0)  50.
08% FA in H2O) followed with 10 min at 99% B. The total duration was set to 280 minutes at a flow rate of 200 nL/min. Proteins were identified by database searching using SequestHT (Thermo Fisher Scientific) with Proteome Discoverer 1. 4 software (Thermo Fisher Scientific) a combined Mus musculus database (Swissprot, release 2015_11, 16730 entries) where 5 interesting proteins sequences (TrEMBL entries) were added. Precursor and fragment mass tolerances were set at 7 ppm and 0. 5 Da respectively, and up to 2 missed cleavages were allowed. Oxidation (M) was set as variable modification, and Carbamidomethylation© as fixed modification. Peptides were.
Acknowledgements We thank  D. Singer  and  A. Gegonne  for the gift of the Taf7 flox mouse line and  H. Stunnenberg  for TFIIA antibodies. We would also like to thank  D. Devys  for critically reading the manuscript, all members of the  Tora lab  for thoughtful discussions and suggestions throughout the course of the work. We are grateful to  I. Kukhtevich ,  M. Borsos ,  M. E. Torres Padilla ,  T. Gupta  and  L. Casini  for advice and preliminary experiments. We thank  C. Hérouard  and  M. Jung  from the GenomEAST platform for library preparation and preliminary analyses,  P. Eberling  for peptide synthsis,  F. Ruffenach  for proteomic analyses,  G. Duval  for polyclonal antibody generation, the histology platform, the IGBMC cell culture facility and  S. Falcone ,  M. Poirot  and  F. Memedov  of the IGBMC animal facility for animal care taking.
This work was supported by funds from  CNRS ,  INSERM , and  Strasbourg University. This study was also supported by the  European Research Council (ERC)   Advanced grant  ( ERC-2013-340551 ,  Birtoaction)  and grant  ANR-10-LABX-0030-INRT  (to LT) and a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. IB and FM acknowledge support by  Wellcome Trust Senior Investigator  awards ( 106115/Z/14/Z  and  106955/Z/15/Z , respectively).
For details about each SOOP and how it is going to address the Lastly, these SOOP names play a key role in identifying the resulting cohesive scientific datasets to the user in the Solar Orbiter ARchive (SOAR) 7. Once they have already been downlinked, processed, and checked for calibration, all datasets from a given SOOP, from all participating instruments, will be linked and discoverable by searching for that SOOP name. ). This will be the standard SOOP that will run during the whole cruise phase and during the nominal phase outside the RSWs. It will also run in combination with other SOOPs during the RSWs. It is designed to address all in-situ objectives for which no remote-sensing observations are needed. Within the SOOP, the four instruments will be in normal or in burst mode, together or individually.
SO/PHI will contribute with a 6-hour cadence synoptic mode, providing data suitable for coronal magnetic field extrapolations. SoloHI will measure the solar wind speed above the potential source region in coordination with Metis. In those circumstances when connectivity could be established between the coronal regions observed by Metis and EUI/FSI and the insitu plasma probed by Solar Orbiter, SWA will measure the α/p density ratio and composition. This SOOP will in principle be scheduled when the spacecraft is inside 0. 45-0. 5 au, which is an optimal distance for the EUI/FSI occulter, or at perihelion for a reduced rotation of the Sun relative to the spacecraft. This SOOP would also benefit from coordination with DKIST (Cryo-NIRSP instrument) when Solar Orbiter is in conjunction or opposition with Earth.
L_FULL_HRES_HCAD_Eruption-Watch This SOOP is designed to observe eruptive events and contribute to the understanding of coronal mass ejection initiation. The full solar disc will be observed with highest spatial resolution available to the full disc telescopes and temporal cadence for a typical duration of one day. EUI will be in the Global Eruptive Event mode during the whole day, but it will prioritise the data of one or two events due to telemetry limitations (for example data corresponding to two hours of the day). Metis' standard mode GLOBAL will be used together with the CMEOBS mode for a duration of at least one hour, when triggered with a CME flag. SO/PHI's FDT will also be observing at the highest spatial resolution with a cadence of two to five minutes and a data selection will be needed as for EUI.
A typical duration for this SOOP is three days. EUI Coronal Hole mode will be used at a cadence of one minute together with the synoptic mode of FSI. SO/PHI will acquire regularly spaced HRT data at medium to high resolution (600 s default cadence, but one hour cadence will also be marginally sufficient for studying interchange reconnection at high resolution). SO/PHI will also make use of the Low-Latency magne-tograms  (Solanki et al. 2020 ) in order to better identify the most interesting periods that would be selected for downlink. SPICE is central to this objective using a combination of the Dynamics mode and of Composition Mapping rasters. The raster area will be optimised to make sure that the open-closed field boundaries are captured and the lines will be chosen according to the type of target.
R_FULL_LRES_HCAD_Full-Disc-Helioseismology This SOOP is designed for all helioseismology techniques using full-disc data from the SO/PHI FDT. Solar Orbiter will provide the first opportunity to implement helioseismology to probe flows and structural heterogeneities deep in the Sun as well as active regions on the far side. Combining Solar Orbiter observations with ground-or space-based helioseismic observations from 1 AU (e. g. GONG, SDO, Lagrange) will open new windows into the Sun  (Löptien et al. 2015). Looking at the Sun from two distinct viewing angles will increase the observed fraction of the Sun's surface and will benefit global and local helioseismology techniques (e. g. Roth et al. 2016) , because the modes of oscillation will be easier to disentangle due to the reduction of spatial leaks.
Recent theoretical efforts have led to improvements of the method  (Gizon et al. 2018 ) and active regions of average sizes can now be imaged with only a few days of observations. Observations from Solar Orbiter will allow helioseismic holography to provide improved measurements of the remaining far side. In addition, SO/PHI magnetograms will help calibrate and validate the measurements of the far side made using holography based on data taken on the front side (from L1, the Earth, or L5), for improved space weather predictions. Large-scale flows, including the meridional flow, can be constrained using time-distance helioseismology, which consists of measuring the time of acoustic waves travelling between pairs of points on the surface through the solar interior. Inversions of travel times have led to different answers for the deep meridional flow.
2 Mm at 3 mHz. Seismic modes f and p 1 through p 4 are used to study the dynamics of the surface layers at spatial scales down to supergranulation  (Gizon et al. 2010). The Doppler data will be supplemented by an intensity image and a magnetogram every hour to provide context information. The full resolution of the CCD will be needed near the limb in the high-latitude regions to take account of foreshortening, while it may be degraded towards disc centre. In addition, lossy compression techniques can be applied to further reduce the telemetry  (Löptien et al. 2014).
R_SMALL_HRES_HCAD_AR-Dynamics This SOOP will study the dynamics of a complex active region and its link to the 3 He-rich SEPs production. It can also track a region for exploring the formation of an active region. It will make use of the highest resolution capabilities without binning. EUI will run its Active Region mode in coordination with SO/PHI/HRT. Due to telemetry limitations, only one hour of these data around the event will be downlinked. STIX will have its triggers active. EPD is also needed for detecting the 3 Herich SEP events (but this is not part of the SOOP, since all in-situ instruments will always be on in parallel with any other SOOPs).
Criterion 1: Best resolution remote-sensing data at different perihelia through the mission We schedule a remote-sensing burst SOOP (R_SMALL_HRES_HCAD_RS-burst) whenever we have a perihelion at a time of large telemetry downlink availability, that is close to Earth or when Solar Orbiter is moving towards Earth. We will aim at different types of targets or even plain disc centre to discover new physical phenomena in Solar Orbiter's highest cadence data, also possibly in unexpected locations. This campaign would serve several science goals that need very high cadence, need perihelion observations, and are aiming at different types of regions. Even if off-pointing is not possible, this campaign will still be useful to be run on the Sun-disc centre region. The SOOP under consideration is telemetry demanding, so we need large telemetry downlink availability at the time of the perihelion or right afterwards.
2020). In these periods, we will employ more Earth observations to get a well-constrained model of the field that Solar Orbiter is going to fly through. If SO/PHI data are restricted or not available, we mainly rely on Earth to produce the model four days in advance due to VSTP turn-around loop. For this to happen, we need Solar Orbiter in the GSE sector X<1 au and Y<0 au, that is similar orbits than the ones needed for Earth-directed transients above. The further Solar Orbiter moves away from that sector, the more we rely on SO/PHI data to model the most likely connection point. Due to the more complicated and less reliable magnetic field modelling, we may want to use pointing mosaics to establish the most likely connection point using the SOOP L_SMALL_MRES_MCAD_Connection-Mosaic. During later orbits, the concatenated perihelion and north windows will span a large range of latitudes over a short period of time.
Prior to this period we will have had seven other planning periods with perihelia around 0. 3 au. The previous period is the first one when the spacecraft would have reached a latitude higher than 15 degrees. All previous RSWs at perihelia will have encompassed quadratures between Solar Orbiter and Earth and there will have been six previous RSWs where at least 90 degrees in longitude of the far side of the Sun will have been visible to Solar Orbiter. Examination of the first criterion of the strategy described in the previous section (high-cadence remote-sensing observations) requires an assumption on the mass memory fill state at the beginning of the period as well as the telemetry resources. Given at the start of the period Solar orbiter is on the far side of the Sun from Earth (superior conjunction), it is reasonable to assume that the mass memory is half full since it would contain data from the previous period that did not have an opportunity to be downlinked.
Inversely, the SAP will be used by the community as a tool to work through detailed science activities that will lead us to the answers to the top-level mission objectives. It is important to stress that this is a dynamic and ever-evolving plan as new data and scientific knowledge become available. All decisions regarding the planning will be reviewed and re-considered regularly. For this reason the participation and input of all scientists are welcome to ensure the maximum scientific return from Solar Orbiter and to advance significantly humankind's understanding of our star.
Acknowledgements. Solar Orbiter is a space mission of international collaboration between  ESA  and  NASA  with contributions from national agencies of ESA member states. The spacecraft has been developed by Airbus and is being operated by  ESA  from the  European Space Operations Centre (ESOC) in Darmstadt, Germany. Science operations are carried out at  ESA 's  European Space Astronomy Centre (ESAC) in Villafranca del Castillo, Spain. SWA is an international collaboration which has been funded by the  UKSA ,  CNES ,  ASI ,  NASA  and the Czech contribution to the  ESA PRODEX  programme. UAH authors want to thanks the  Spanish MINECO-FPI -2016 predoctoral grant with  FSE , and its project  FEDER/MCIU-AEEI/Proyecto ESP2017-88436-R. The Spanish contribution to  SO/PHI  has been funded by the  Spanish Ministry of Science and Innovation through several projects , the last one being  RTI2018-096886-B-C5 , and by  "Centro de Excelencia Severo Ochoa" programme  under grant  SEV-2017-0709.
RESULTS Five clades of SARS-CoV-2 are characterised by 15 major mutational events across the globe. The SARS-CoV-2 genome is genetically most closely related (96%) to a bat SARSrelated coronavirus (SARSr-CoV) RaTG13 and also to SARSr-CoVs from pangolins  8  (Supplementary Figure  1 ). We analysed single nucleotide polymorphisms (SNPs) of 2,058 high-quality complete genomes downloaded from GISAID  9  (March 31 st ). We studied their chronological occurrences during the spread of the SARS-CoV-2 across human populations, which allowed us to define several major and minor clades of the virus that share unique SNPs (Supplementary Table  1 , Supplementary Figures  2, 3 ). We observed 1,221 SNPs in the current dataset with 753 missense, 452 silent, 12 nonsense and 4 intergenic substitutions. We defined five major clades (Figure  1 ) compared to the prototype (MN908947.
DISCUSSION In this study, we have defined 5 major clades (G, I, S, D and V) and 9 minor clades (H2, L2, G1110A, H, Y, C17373A, S2, I2 and K) which covers ~89% of the 2,058 high quality genomes available until March 31 st in GISAID database. The clustering of these genomes revealed the spread of clades to diverse geographical regions (Figure  1 , Figure  3 ). This pattern contrasts with those observed for other epidemic coronaviruses, such as MERS-CoV, which display distinct geographical clustering  17. For example, clade G, which was first detected on January 28 th , has reached 40 countries and 130 cities, within a span of 10 weeks (Supplementary Table  1 ). This pattern demonstrates efficient viral transmission through frequent intercontinental travel during the period when international travel restrictions were only present sporadically, which has enabled the virus to spread to multiple distant locations within a short period of time.
An important caveat of the present study is that the current sampling of available public genomes does likely not represent the extant genetic diversity of virus populations in circulation due to biases of genome data deposits from the sequencing laboratories based mainly in the northern hemisphere and new datasets may define new clades in the near future from regions, including Africa, the Indian subcontinent and Latin America with comparably few genomes available at present. In this case, additional identifiers within an evolving barcode scheme can be added to track and monitor future emerging clades with higher resolution. On the other hand, the genetic stability of SARS-CoV-2  18  may result in the continuing circulation of a limited number of clades until such time as mitigation measures including the isolation of vulnerable populations and the availability of efficacious antivirals and vaccines reduces the genetic diversity in circulation.
However, due to the bias in the representation of countries depositing the SARS-CoV-2 genomes with over-representation of North American and European genomes (28. 3% and 47. 2% respectively) and the available genome data representing only a minute proportion of the total COVID-19 positive cases from each of these regions (America, 0. 27%; Europe, 0. 31%; China, 0. 31%. Data collected from GISAID  9  and COVID-19 dashboard 1 ), the genetic barcode described hererin may need to be updated in order to be globally representative, once sufficient numbers of genomes covering less represented parts of the world are eventually sequenced and deposited to publicly-available database. We envisage a qPCR-based allelic discrimination approach, such as PCR allele competitive extension (PACE), which would enable rapid turnaround in real-time following the identification of a laboratory-confirmed case.
Phylogenomic Analysis 1,427 coronavirus genomes were downloaded from Virus Pathogen Database and Analysis Resource (ViPR)  23  on February 14 th 2020, including 329 SARS, 35 SARS-CoV-2, 61 NL63, 521 MERS, 52 HKU1, 170 OC43, 97 bovine coronaviruses and 61 mouse hepatitis viruses. Sequence alignment was performed using MAFFT(version 7. 407)  24  software and then trimmed by trimAL(version 1. 4. 1)  25. Phylogenetic analyses of the complete genomes were done with FastTree(version 2. 1. 10)  26 software with default parameters, and iTOL(version 5)  27  was used for phylogenetic tree visualisation. 2,127 complete SARS-CoV2 genomes were downloaded from the GISAID9 (March 31th 2020). 69 of those genomes were removed due to poor assembly quality resulting in 2,058 complete genomes that were subsequently used for analysis.
Protein Structural Analysis Experimentally determined protein structures were obtained from the Protein Data Bank (PDB). SwissModel 31 , I-Tasser 32 , RaptorX  33  and an in-house modelling pipeline was used to produce protein structure homology models. Phobius  34  was used for prediction of transmembrane regions. RaptorX was also used for predicting secondary structure, protein disorder and solvent exposure of amino acids. Pymol(version 1. 8. 6. 2) was used for visualization. The substitution A876T can be accommodated with only minor structural adjustments and is not expected to have a substantial influence on the proteins stability or function. (D) The structure shows the nsp12 in complex with nsp7 (magenta) and nsp8 (cyan and teal), based on PDB 7btf. P4720 (P323 in nsp12 numbering) is located in the 'interface domain' (black).
ACKNOWLEDGEMENTS This work was supported by funding from  King Abdullah University of Science and Technology (KAUST) ,  Office of Sponsored Research (OSR) , under award number  FCC/1/1976-25-01. Work in AP's laboratory is supported by the  KAUST faculty baseline fund  ( BAS/1/1020-01-01 ) and research grants from the  Office for Sponsored Research  ( OSR-2015-CRG4-2610 ,  OCRF-2014-CRG3-2267 ). We thank all laboratories which have contributed sequences to the GISAID database. We thank  Olga Douvropoulou ,  Raeece Naeem Mohamed Ghazzali  and  Sharif Hala  for their support during the work. We also thank  Richard Culleton  ( Nagasaki University, Japan ) and  Gabo Gonzalez  ( UCD, Ireland ) for their critical comments on the manuscript draft.
Fluorescence Activated Cell Sorting (FACS) Cells were pulse-labelled with 10μM BrdU and/or EdU for indicated times then collected by trypsinization and fixed in 70% ice-cold ethanol overnight at -20°C. For EdU and BrdU immunodetection, we followed the protocol described in Bradford and Clarke 2011  (38). Finally, after washing in PBS-BSA 0. 5%, then in PBS, cells were resuspended in PBS with propidium iodide (25 μg/mL, Invitrogen, p3566 ) and RNase A (100 μg/mL, Thermo Fisher Scientific, ENO531) or with DAPI (1/1000, Sigma-Aldrich, D9542). After 20 min of incubation at room temperature, cell cycle analysis was carried out by flow cytometry with a MACSQuant 10 or VYB cytometer (Miltenyi Biotec) and analysed with FlowLogic software.
Same amounts of early and late-labeled DNA were loaded on human DNA microarrays (SurePrint G3 Human CGH arrays, Agilent Technologies, G4449A). Hybridization was performed as previously described  (39). The following day, microarrays were scanned using an Agilent C-scanner with Feature Extraction 9. 1 software (Agilent technologies). To determine the replication domains and do the comparative analysis in different conditions, the online platform specific for replication timing data START-R (40) was used, with biological duplicates for each condition. The output bed file gave the list of significantly impacted genomic regions (ADVANCED or DELAYED) and the report of number and percentage of genomic regions impacted. We also used the output replication timing smooth files to identify the early (RT > 1) the mid (-1 < RT < 1) and late (RT < -1) replicating regions.