dataesr/scientific-paragraphs-categorization huggingface dataset:
Dual Band Receivers In the framework of the Dual Band Receiver (DBR) project, it is planned to equip all antennas with DBRs for simultaneous observations in Band 1 and Band 3 by the end of 2024. Concurrent operation in the two bands, coupled with the use of a second PolyFiX correlator to cover the entire 62 GHz bandwidth, will open a new era of unprecedented spectral coverage for NOEMA. Not only will this improve the observatory's scientific productivity by accelerating the completion of projects requiring observations in the two bands, but the DBRs will also allow for an overall improvement in data quality. They will pave the way for band-to-band phase referencing to improve the dynamic range of astronomical images, significantly reduce systematic calibration uncertainties, facilitate the completion of multiple transition line surveys, and last but not least, open up the possibility of observing transient and time-critical phenomena that evolve on short time scales.
For NIKA2, the commissioning of the polarimetric mode at 1. 2 mm was completed by the end of 2021, and first observations of B-FUN, a Large Program aimed at probing the magnetic field in star-forming regions, were started in the winter semester of 2021/2022. The next updates will include a new, improved dichroic filter, planned for late 2022, and the adaptation of the PIIC data reduction software for polarimetry. In the longer term, new KID arrays with higher sensitivity and higher pixel yield will be installed. The pointing and tracking capabilities of the IRAM 30-meter are outstanding  [3] , although improvements are underway (see section 3. 2). In particular, the maximum tracking speed, which is 150"/s due to limitations in the control system, limits the maximum observable elevation to 83 •.
3) [11, 28, 34]. Until recently ancestral Beijing included 3 asia ancestral lineages (asia ancestral 1, 2 and 3, AAnc1, AAnc2, AAnc3)  [28] , until a new asia ancestral 4 (AAnc4), was discovered in northern Thailand  [35]. Modern Beijing strains are responsible of most but not of all of the recent MDR-TB outbreaks  [4, 15, 36]. The large databases built with WGS data allow to develop a precise and comprehensive knowledge of L2 diversity  [4, 26, 31, 37]. Computational genomics now allows to study in-depth both the global and the local L2 history  [6, 24, 26, 32, 35, [38] [39] [40] [41] [42]. The evolutionary history of L2 isolates is still debated as is their precise dating of emergence and their geographical origin  [26, 34]. Luo et al. suggested that L2 could be as ancient as 30 000 years old  [8].
We studied a set of L2 isolates from central Japan Honshu prefecture, Tochigi  [42]. The 169 clinical isolates of M. tuberculosis we studied were from TB patients diagnosed in 2007 and in 2013  [42]. WGS data on these isolates was released after analysis using a bioinformatical pipeline, described as 'CAST' by Iwai et al. [53]. Since a comparative genomic analysis of these isolates with other ancestral L2 had not been performed, we included these genomes in our database. We also included SRAs labelled as asia ancestral 4  [35]. Using our pipeline, we described the genetic characterisation of a new L2 sublineage from Japan, named asia ancestral 5 (AAnc5), that appears to be exclusive from Japan for the time-being. We also assessed the characteristics of the AAnc4 sublineage described in Thailand and provide an updated global evolutionary framework of the L2 lineage  [28].
Brief description of the TB-annotator pipeline The full version of TB-Annotator is going to be released in another article (Senelle et al. in preparation). In brief, regarding the set-up of the platform, the processes are the following: SRAs of interest are selected and kept only provided a certain number of conditions that together reinforce the reliability of the data: they must have read length>75 bp, clean reads file must be at least 100 Mo, and CRISPR could be reconstructed using CRISPRbuilder-TB  [54]. For each SRA, in addition to reconstructing the CRISPR-Cas region using CRISPRbuilder-TB and apart from collecting NCBI information on the genomes, two scripts successively perform the following tasks: (1) search for SNPs according to reference catalogues (Supplementary Table  S1 ) totalling more than 50 000 SNPs, including drug-resistance related SNPs, phylogenetic SNPs as per 26 studies contributing to SNP-based classification  [26, 28-30, 33, 55-75] ; (2) search for additional SNPs in each isolate based on H37Rv reference sequence; (3) look for the presence/absence of H37Rv genes as annotated in mycobrowser (https://mycobrowser.
epfl. ch/), (4) look for the presence/absence of deletion regions;  (5)  identify insertion sites of all known insertion sequences in MTBC. The CRISPR locus is rebuilt semi-automatically using a dedicated and previously published script (43/68/360 Spacers format) with an assignment of a Spoligo-International-Type (SIT) tag; the application produces an ordered list of spacers/repeat with variants and IS6110 insertion sequences if present  [54, 76, 77].
Selected genomes We downloaded a set of 680 SRAs; these samples were selected to represent TB genomic diversity (L1 to L9) described so far, including recent landmark papers  [29, 30]. The list of these SRAs is shown in Supplementary Table  S2  (list of 680 SRA including 190 L2 SRA); the database was built to represent all L2 sublineages except for the Pacific RD150 sublineage. From  [35] , we selected 28 SRAs labelled AAnc4. From  [42] , we initially included 158 SRAs, however 57 SRAs for which the coverage was either too weak or for which it was impossible to rebuild the spoligotype using CRISPR-builder-TB were discarded  [54, 77].
Bioinformatics and phylogenetical methods Final scripts allow to produce a phylogeny based on the list of studied characters and using RAxML and SplitsTree  [78, 79]. All computations were performed on the 'Mésocentre de Franche-Comté' supercomputer facilities (141 nodes, 2292 cores, 9,27 To memory, 74,2 CPU Power TFlops, 66,4 GPU TFlops), using adequate commands. Apart from the results shown in the Supplementary Table  S3 , a final phylogenetic tree is displayed graphically and proprietary python scripts allow interactive queries to be made and results to be shown  [79]. The current TB-Annotator version includes 6009 genomes and confirmed our results (Senelle et al. in preparation).
Methods and dataset used to define a new Asia ancestral 5 sublineage (AAnc5) After the assessment of existing known MTBC sublineages (Fig. 2a  and b ), the unknown branch (Fig. 2c ) was investigated in detail: in Silico CRISPR locus reconstruction using CRISPR-builder TB  [54, 77] , in Silico MIRU-VNTR using CAST  [53] ; SNPs assessment and intra-cluster distance with TB-Annotator; AAnc5 was shown to originate only from SRAs from Japan isolates (Bioproject PRJDB3875). The bioinformatical pipeline, based on its graphical user interface, allows to select and display new exclusive or shared SNPs and specific genetics characteristics, that were further investigated. Previous genomic markers extracted from Wada et al. 2012 were compared to our results and are found in Supplementary Table S4  [39].
A second list of 46 exclusively shared SNPs, shared between the two most distant isolates on a specific subbranch of AAnc5 (DRR034381 and DRR130203, pairwise distance: 417 SNP) is also shown in Supplementary Table  S5. In silico spoligotyping and reconstruction of the CRISPR locus structure of AAnc 5 using TB-annotator, IS6110 copy number and insertion sites The twenty studied AAnc5 isolates showed 6 different spoligotype patterns as reconstructed by CRISPR-builder, which was an unexpected result for an L2 sublineage (Supplementary Table  S6 ); most of these patterns have been previously described in the SITVIT  4  database (SIT1, SIT190, SIT269, SIT1364, SIT1674), however one remained undefined as SIT"X". No SNP variants were found in spacers and repeats, but three isolates exhibited duplications: a duplication of sp65 for DRR034478 and SRR130160, and of sp50 for DRR034476 (Supplementary Table  S6 ).
Positive and negative dsDNA controls (100% and 0% methylated, respectively) were titrated (5, 10, 20, and 100 ng) in Tris-EDTA buffer pH 8 to 10 μL and used for reference. EPIC arrays, RNA-Seq, and bisulfite Sanger sequencing DNA was extracted from patient samples as described previously  (50) , and bisulfite conversion was performed using EZ DNA methylation kit (Zymo) according to the manufacturer's instructions, followed by Sanger sequencing using the same primers and protocols as described in Lauss et al. (21). Melanoma cell lines were analyzed using Illumina Infinium methylation EPIC BeadChip array. Methylation data for tumor samples have been deposited to GEO with accession number GSE144487. RNA-Seq data were processed as previously described using TopHat2 and Cufflinks v2. 1. 1. Isoform fragments per kilobase million were summed up to obtain gene-level expression  (51).
Data are deposited in GEO under accession number GSE211906 (Supplemental Table  1 ). Preprocessing of DNA methylation data from patient samples and melanoma cell lines Raw idat files were processed using R package ChAMP  (52) , and background correction was performed using ssNoob  (53, 54)  from package minfi  (55). Furthermore, type I/II probes were normalized using BMIQ  (56)  and filtered for polymorphic and off-target probes  (57). For patient samples, samples with a JCI Insight 2022;7  (19) :e156577 https://doi. org/10. 1172/jci. insight. 156577 large number of failed probes (probe detection P > 0. 01; sample cutoff > 4% of total probes) and probes that failed in more than 10% of the remaining samples were removed. Next, methylation β values of probes that failed in 10% or less samples were imputed using the impute.
knn function in R and default settings. After preprocessing, the patient tumor methylation data set contained 788,174 probes and 196 samples. The processed methylation data for the Lund cohort is available in the GEO database, under accession number GSE144487. Preprocessing of DNA methylation profiles from 14 MITF promoter-methylated melanoma cell lines was done for the patient samples, except that any probe above the P value cutoff in 1 or more samples was removed, instead of imputing the methylation β value.
Data sets Microarray expression data using Illumina HT12 arrays, as previously described in Cirenajwis et al. (1)  and Rizos et al. (29) , were derived from GEO, GSE65904 and GSE50509. Mutation data from 1,697 cancer-associated genes were derived from Cirenajwis et al. (1). Methylation data for The Cancer Genome Atlas (TCGA) skin cutaneous melanoma melanoma samples (n = 475) from the TCGA data portal (https:// tcga-data. nci. nih. gov/tcga/) consisting of level 3 β values from the Illumina HumanMethylation450 array, were processed as described previously  (58). Briefly, for the Infinium I and II assays of each sample, the data were scaled to move the unmethylated β value peak to β value of 0 and the methylated peak to 1; values below 0 and above 1 were set to 0 and 1, respectively.
NanoString gene expression analysis Core punch samples were taken from paraffin-embedded mouse melanoma primary tumors for RNA extraction and profiling. The NanoString nCounter PanCancer Pathway Panels assay, containing 770 cancer pathway genes, was performed according to the manufacturer's instructions. The obtained count data were normalized using the NanoString nSolver software: background thresholding was applied to set the minimum value to 20 (default), and a scaling factor, derived from the geometric mean of the positive ERCC control probes, was applied to each sample. The data were log-transformed as log 2 [data] -log 2  [20] , and positive and negative control probes were removed (Supplemental Table  2 ).
Acknowledgments We thank the  MultiPark FACS Core facility  and particularly the research engineer  Anna Hammarberg  for the FACS analyses. This work was supported by  Swedish Research Council  ( Vetenskapsrådet Dnr 2018-02786 ),  Swedish Cancer Society  ( 19 0458 Pj ),  Berta Kamprad Foundation  (GJ),  Stefan Paulsson   foundation  (GJ),  Mats Paulsson  foundation (GJ) and the governmental funding for healthcare research (ALF and GJ), and  European Community 's  Horizon 2020 Framework Programme for Research and Innovation  ( H2020-MSCA-ITN-2014 ) under grant agreement  641458. Address correspondence to:  Göran Jönsson , Division of Oncology,  Department of Clinical Sciences, Lund University , Lund 221 00, Sweden. Phone: 46. 703210353; Email: Goran_b. jonsson@med. lu. se. AS's present address is:  Melanoma Laboratory, Molecular Oncology Programme, Spanish National Cancer Research Centre, Madrid, Spain.
Our knowledge of sea-level and its variations in the 19th century and up to the present is primarily based on observations made by tide gauges. These provide the first half of Equation  (3) , that is [R ss (P,t) -R ss (P,t r )]. One of the longest series comes from the Brest (France) tide gauge, established in 1807 and still in function. Le Mouël et al. [26]  recently analyzed the Brest data and in parallel Earth's pole positions (from the IERS, 1845-2019), submitting both of them to singular spectral analysis (SSA). The first SSA components of both series, i. e. , the trends, are very similar, with a major acceleration event near 1900 and a sea-level lagging pole motion by 5-10 years. SSA components with periods 1 yr, ∼11 yr and 5. 4 yr are common to the two series. An important feature is a 0.
5 yr component that is present in sea-level but absent from pole motion. The remarkable similarity of the two trends and their phase lag suggests a causal relationship opposite to what is generally accepted. The primary aim of the present paper is to apply the same analysis to the global database of tide gauges. The tide gauge data are maintained by the Permanent Service of Mean Sea Level (PSMSL, https://www. psmsl. org/data/obtaining/complete. php (accessed on 5 July, 2020). Measurements of coastal sea level are available at 1548 sites; the raw data at all sites are shown in Figure  1. The second half of Equation  (3) , that is, vertical land motion VLM = [R se (P,t) -R se (P,t r )], can be accessed through GPS measurements. These allow far better coverage of Earth surface but cover a much shorter span of time, generally 30 yr at most.
For that reason, only "recent" trends covering that period can be accessed. A very thorough analysis of these data has recently been published by  [8]. The database is maintained by the Nevada Geodetic Laboratory, with 19286 sites scattered throughout the globe, and is called Median Interannual Difference Adjusted for Skewness (MIDAS, http://geodesy. unr. edu/velociti es/ (accessed on 5 July 2020). Since the beginning of the 1990s, the sea level has also been monitored by series of altimetric satellites that have allowed much denser global coverage but (as is the case for GPS measurements) over a short time range of 30 years. A number of recent papers have attempted to combine these very different data sets, resulting in various global sealevel (GSL) curves (e. g. ,  [8, 27] ). Interest in these GSL curves has focused on their trends, acceleration, annual and inter-annual variations, and on the mechanisms responsible for these variations.
SSA of Tide Gauge Records Time Series (i. e. , [R ss (P,t) -R ss (P,t r )] Drawing on a recent paper  [26] , in which we analyzed the record from the Brest tide gauge since 1807, using SSA to extract its main components, we proposed to build a global sea-level curve covering the past 200 years using data from tide gauges only (GSLTG). We are aware of the uncertainties due to geography and tectonics but are interested in subjecting the original data to as little manipulation as possible. We have selected the recordings from 31 stations (Table  1 ) on the basis of two criteria: the longest possible span of time and the best possible-if still too restrained-spatial coverage (Figure  2 ). We have also performed the analyses that follow on the full database (1548 tide gauge stations): this results in increased dispersion without changing what can be drawn from the selection of 31 stations.
The locations and values of the tide gauges and the VLM are plotted, respectively, in Figure  7a, b. Over a large part of the coastal areas, the tide gauge signal and the VLM appear to be opposite in sign, that is, anti-symmetrical (e. g. , North America, the Mediterranean region). The results of this analysis are in full agreement with the thorough study of  [8] , in which water rises as the land tends to subside and vice versa. If we assume that the recent (30 yr) VLM in Brest remained the same from 1807 to the present, then we can evaluate the mean sea level rise with respect to the Earth's center of mass (Equation (  3 )) as 1. 5 mm/yr. This rough estimate is in full agreement with a series of recent studies (e. g. ,  [8, 26, 57, [61] [62] [63] [64]. The MIDAS database provides the slopes of the GPS data over their duration (life time), which can range from 2 to over 20 years.
Components Shared by GSLl, GSLTG and SSN Despite the fact that one is included in the other, the GSLl and GSLs series do not share many characteristics (cf. Table  2 ). Their trends are similar and they both have significant 1-year and 6-month components, but the two series do not share other quasiperiodic components (unless the 15-year component of GSLs can be considered to be the same as the 20 yr component of GSLl, given uncertainties). Actually, the span of the GSLs data is only 27 yr and should not be used to identify components with periods longer than, say, 27/2 or ∼15 yr. In stark contrast, GSLl shares seven components with GSLTG: a trend of 60, 30, 20, 10, 1 and 0. 5 yr components cf. Table  2 ). Le Mouël et al. [48]  and Courtillot et al. [39]  have calculated the SSA components of the sunspot number SSN, a characteristic of solar activity.
Summary and Concluding Remarks In the present paper, we have first analyzed variations in the geoid, using tide gauge data for the time span 1807-2020 (GSLTG) and variations in the global sea level combining tide gauges and satellite data (GSLl  [27] ); then, with much better spatial coverage but a much more restricted time span (1993-2020), satellite-only data (GSLs,  [65] ). Our main goal has been to determine the trends and successive periodic or quasi-periodic components of these time series, using the SSA method (SSA) that we have used, with interesting results, in a series of previous papers  [26, 31, 39, 47, 48, 51]. Since satellite data are first corrected using tide gauges, we analyzed the tide gauge data at 71 gauge stations separately (series GSLTG). We see all kinds of behavior, with increasing, stable or decreasing mean values (trend), giving the complete data set a funnel shape.
Data Availability Statement: The used data are freely available at the following address: Permanent Service of Mean Sea Level: https://www. psmsl. org/data/obtaining/complete. php accessed on 5 July 2020; Median Interannual Difference Adjusted for Skewness: http://geodesy. unr. edu/velocities/ accessed on 5 July 2020; NASA: https://podaac-tools. jpl. nasa. gov/drive/files/allData/merged$_$alt /L2/TP$_$J1$_$OSTM/global$_$mean$_$sea$_$level/GMSL$_$TPJAOS$_$5. 0$_$199209$_$202008. txt accessed on 5 July 2020; Met Office Hadley Centre: http://www. metoffice. gov. uk/hadobs/hadslp 2/data/download. html accessed on 5 July 2020; International Earth Rotation and Reference Systems Service: https://www. iers. org/IERS/EN/DataProducts/EarthOrientationData/eop. html accessed on 5 July 2020.
Once combined with a pre-earthquake image acquired on November 6, 2019, several groups applied the Interferometric SAR (InSAR) technique to map the co-seismic surface deformation field induced by the earthquake: (1) the BRGM group used the GAMMA software on the European Space Agency (ESA) Grid Processing On Demand (GPOD)  [De Michele et al. , 2013] , (2) the IPGP group and the Geoazur group used the NSBAS software  [Grandin et al. , 2017] , while (3) others used the DIAPASON and SNAP softwares. These rapid analyses showed that the displacement field of the earthquake in the line-of-sight (LOS) direction of the satellite, between those two dates, was characterized by up to 5 cm displacement away and towards the satellite (Fig ure  2 ). The unwrapped interferograms clearly indicated a shallow rupture about 4 km long reaching the surface, in the NE-SW direction, with an uplift of the SE compartment and subsidence of the NW one, in close agreement with the NE-SW trending and SE dipping nodal plane of the reverse faulting focal mechanism.
Deployment of seismological and geodetic stations This Mw 4. 9 earthquake is a contemporary rare event in southern France, an area of moderate seismicity, in terms of damage produced in several villages, especially Le Teil and Viviers, and above all because it produced surface rupture. However, similar size and shallow depth events are known to have occurred in the enlarged region in the past. Seismic swarms oc curred in  1773, 1873, 1933-36 and 2002-2003  in the Tricastin region, about 20 km to the south-east of Le Teil. Significant damages were reported in nearby villages during seismic swarms in 1773 and 1873, as documented in the SisFrance database of historical earthquakes (http://www. sisfrance. net, last accessed February 4, 2020) (Figure  1a ). Thouvenot et al. [2009]  found that the 2002-2003 swarm (Mmax = 1.
The first two broadband stations were installed in the evening of November 11 by a team from Geoazur, in a public building at the north of Montélimar and in the townhall of Le Teil. The stations started collecting data at 21:00 and 22:30, respectively. Two other stations were installed the next morning at Saint-Thomé (recording started on November 12 at 10:00) andAlba-La-Romaine (11:15). During the four days following the earthquake and with insight on the fault location gained from the InSAR interferograms, Geoazur, ISTerre, CEREMA and IRSN installed a total of 13 broadband and 8 accelerometric sta tions from the RESIF-RAP, Geoazur, IRSN, CEREMA and RESIF-SISMOB mobile pools. The deployment was complemented by 28 autonomous short-period sensors (Fairfield nodes) from the RESIF-SISMOB mobile pool, 16 nodes being installed on November 12 and 13, and 8 more on November 18, especially in the vicinity of La Rouvière fault (Figure  4 ).
After one week, the acqui sition interval was switched to the standard measure ment interval used for highly precise daily positioning (30 s) to avoid memory limitations at these autonomous stations without data link transmission. The stations will be maintained for at least one year, to be able to distinguish any transient motions related to the earthquake sequence from the long term behaviour of these stations that might comport significant seasonal signals. These data will be made available through the RESIF-RENAG data archive.
Scientific potential of the collected seismological data three months after the earth quake Due to the low seismicity after the mainshock, most of the temporary seismological stations have been dismantled between January 14 and February 7,2020. Only 3 broadband stations (STIL, CLAU, THOM) and 1 accelerometer (XX01 colocated with THOM) were still in place at the end of February 2020 (see www. resif. fr, network code 3C for the exact location). We present in this section some scientific perspectives related to the seismological data recorded by the very dense network deployed after the mainshock, and operated for 3 months.