dataesr/scientific-paragraphs-categorization huggingface dataset:
com/sdss/apogee 144 https://github. com/callendeprieto/ferre justed so that stars in the solar neighborhood (within 0. 5 kpc of the Sun, according to Gaia parallaxes) with nearsolar metallicity (-0. 05 <[M/H]< 0. 05) are adjusted to have a mean [X/M] = 0. The reason for this choice is discussed in detail in  Jönsson et al. (2020). The procedure is described in significantly more detail, along with an assessment of the quality of the stellar parameters and abundances, in  Jönsson et al. (2020).
Open Cluster Chemical Abundances and Mapping The goal of the Open Cluster Chemical Abundances and Mapping (OCCAM) survey is to create a uniform (same spectrograph, same analysis pipeline) open cluster abundances dataset. We combine proper motion (PM) and radial velocity (RV) measurements from Gaia DR2 (Gaia Collaboration et al. 2018) with radial velocity (RV) and metallicity measurements from APOGEE to establish membership probabilities for each star observed by APOGEE in the vicinity of an open cluster. DR16 is the second VAC from the OCCAM survey. We do not impose a minimum number of reliable member stars as in the previous version (released in  DR15 Aguado et al. 2019; and described in detail in Donor et al. 2018 ), but we do enforce a visual quality cut based on each cluster's proper motion (PM) cleaned color-magnitude diagram (CMD).
Changes to the eBOSS Spectral Reduction Algorithms The data in DR16 were processed with the version v5 13 0 of the pipeline software idlspec2d  (Bolton et al. 2012; Dawson et al. 2013). This is the last official version of the software that will be used for studies of large-scale structure with the SDSS telescope. Table  4  presents a summary of the major changes in the pipeline during SDSS-IV (eBOSS) and we document the final changes to idlspec2d below. There were two major changes from DR14 to DR16 to the reduction algorithm. First, a new set of stellar templates is used for the flux calibration. This set of templates was produced for the Dark Energy Spectroscopic Instrument (DESI) pipeline and provided to eBOSS. These templates reduce residuals in flux calibration relative to previous releases through improved modeling of spectral lines in the F-stars.
eBOSS Value Added Catalogs There are two VACs based on eBOSS data which we release in DR16. These catalogs offer insight into galaxy physics with eBOSS spectra beyond the core cosmological goals. The catalogs are described below. • Classification eBOSS Emission Line Galaxies: This catalog gives the classification of 0. 32 < z < 0. 8 eBOSS ELGs into four types: starforming galaxies, composites, Active Galactic Nuclei (AGN) and Low Ionization Nuclear Emissionline Regions (LINERs). It also contains the parameters: [OIII]/Hβ, [OII]/Hβ, [OIII] line velocity dispersion, stellar velocity dispersion, u -g, g -r, r -i, i -z that are used for classification. The classification is based on a random forest model trained using z < 0. 32 ELGs labeled using standard optical diagnostic diagrams  (Zhang et al.
2019 ). The codes, data and data models are available at https://github. com/zkdtc/MLC_ELGs in addition to the standard location for VACs (see §3). • FIREFLY Stellar Population Models of SDSS Galaxy Spectra (single fiber): We determine the stellar population properties (age, metallicity, dust reddening, stellar mass, and star formation history) for all single fiber spectra classified as galaxies that were published in this release (including those from SDSS-I, II, III and IV). This catalog contains the newly completed samples of eBOSS LRG and eBOSS ELG and will be useful for a variety of studies on galaxy evolution and cosmology (e. g. Bates et al. 2019). This is an update of the calculation done by In the future, we will also present a catalog of more than 800 candidate strong galaxy gravitational lens systems discovered by the presence of higher redshift background emission-lines in eBOSS galaxy spectra (M.
Anticipated Cosmology Results from eBOSS The final eBOSS BAO and RSD measurements will be presented in a series of independent analyses for each target class. The measurements performed with LRG, ELG, and z < 2. 2 quasars will be performed in configuration space and Fourier space. Systematic errors will be assessed through the use of large N-body mock catalogs populated with galaxies according to a halo occupation distribution prescription that approximates the observed data, extending the work done in previous data releases (e. g. Gil-Marín et al. 2018). Consensus values of the angular diameter distance, the Hubble parameter, and f σ 8 will be provided for each tracer based on the two measurements. Measurements of the angular diameter distance and the Hubble parameter will be reported at z > 2.
1 using both the auto-correlation of the final Lyman-α forest sample and the cross-correlation of the Lyman-α forest with quasars. All eBOSS results will be combined with the lower redshift studies from SDSS and BOSS to offer new constraints on the cosmological model as was done in the DR11 sample for BOSS  (Aubourg et al. 2015). As part of the main cosmological goals of eBOSS, there will be a number of VACs based on the final eBOSS data released in DR16. VACs which are planned and will be publicly released in the future include: • Large Scale Structure (from ELGs, LRGs and QSOs). These large-scale structure (LSS) VACs will be based on all available eBOSS data used for the clustering studies. Covering the main target classes, this VAC provides the tools to map the three-dimensional structure of the Universe across 0.
6 < z < 2. 2 (A. Ross et al. in prep. ). • Lyman-α Forest Transmission VAC. This VAC will contain the estimated fluctuations of transmitted flux fraction used for Lyman-α forest BAO measurements. The catalog will provide the estimates over the Lyman-α and Lyman-β rest frame regions of high redshift quasars (H. du Mas des Bourboux in prep. ). • eBOSS Quasar Catalog. Beginning with SDSS-I, SDSS has maintained a tradition of releasing a visually-inspected quasar catalog alongside major data releases. The new SDSS-DR16Q catalog (DR16Q;  Lyke et al. 2020)  will represent the most recent and largest catalog of known unique quasars within SDSS.
The field has also been monitored photometrically with the Canada-France-Hawaii Telescope (CFHT) and the Steward Observatory Bok telescope in order to increase the observing cadence and the overall yield of RM timelag measurements. The SDSS-RM field is also coincident with the Pan-STARRS 1 (PS1  Kaiser et al. 2010 ) Medium Deep Field MD07, and thus has been monitored photometrically since 2010. Observations with SDSS and the Bok telescope will continue through 2020. The program has been largely successful in obtaining RM measurements:  Shen et al. (2016a)  reported several reverberation-mapping measurements from the program after analyzing the first year of spectroscopic data only, and  Li et al. (2017)  measured composite RM signals in the same dataset. Grier et al. (2017)  combined the first year of spectroscopy with the first year of photometry and recovered 44 lag measurements in the lowestredshift subsample using the Hβ emission line.
With the additional years of SDSS-IV monitoring included,  Grier et al. (2019)  reported 48 lag measurements using the Civ emission line; the addition of another year of SDSS spectroscopy and the inclusion of the PS1 photometric monitoring from 2010-2013 demonstrated the utility of longer time baselines in measuring additional lags  (Shen et al. 2019a). Homayouni et al. (2019)  measured interband continuum lags in many sources, allowing for investigations of accretion-disk properties. Additional studies based off of SDSS-RM data that aim to evaluate and improve RM and black hole-mass measurement methodologies have also been completed  (Wang et al. 2019; Li et al. 2019). The final SDSS-RM dataset, which will make use of the PS1 monitoring of the SDSS-RM field and seven years of SDSS spectroscopic monitoring, will span more than ten years and allow for the measurement of lags in the highest-luminosity subset of the quasar sample.
The SDSS-RM dataset is extremely rich and allows for many other types of investigations beyond RM and blackhole masses. The SDSS-RM group has also reported on many other topics, such as studies of quasar host galaxies  (Shen et al. 2015a; Matsuoka et al. 2015; Yue et al. 2018) , broad absorption-line variability  (Grier et al. 2015; Hemler et al. 2019) , studies of extreme quasar variability  (Dexter et al. 2019 ) and investigations of quasar emission-line properties  (Sun et al. 2015; Denney et al. 2016b; Shen et al. 2016b; Denney et al. 2016a; Sun et al. 2018). RM observing will continue through 2020 at APO. Building on this program in SDSS-IV an expanded multi-object spectroscopic RM program is included in the Black Hole Mapper program in the upcoming SDSS-V survey post-2020 (see §7). In addition to the dedicated RM program, there were several fields in SDSS-III and SDSS-IV that were observed multiple times and thus offer similar potential for time-domain spectroscopic analyses.
Those fields with at least four observations are as follows: • Plates 3615 and 3647: contain the standard BOSS selection of targets. These two plates have identical science targets and contain 14 epochs that are classified as "good" observations during SDSS-III. • Plate 6782: contain targets selected to be likely quasars based on variability from multi-epoch imaging data in Stripe 82  (York et al. 2000; Ivezić et al. 2007)   148. This plate contains four epochs that are classified as "good" observations during SDSS-III. • Plates 7691 and 10000: contain a standard eBOSS selection of LRG, quasar, SPIDERS, and TDSS targets. The two plates have identical selections and were observed nine times during SDSS-IV. • Plate 9414: contains ELG targets and TDSS targets from Stripe 82 and was observed four times to develop higher signal-to-noise spectra that could be used to test the automated redshift classification schemes.
SPIDERS Clusters In this section we describe the DR16 target selection, data scope, and VACs related to X-ray clusters. In DR16, 2,740 X-ray selected clusters (out of a total of 4,114) were spectroscopically confirmed by SPIDERS observing over the SPIDERS-Complete area. This constitutes the largest X-ray cluster spectroscopic sample ever build. It forms the basis of multiple studies of structure formation on cosmological times  (Furnell et al. 2018; Erfanianfar et al. 2019). The majority of SPIDERS clusters targets are galaxies selected via the red-sequence technique around candidate X-ray galaxy clusters  (Rykoff et al. 2012 (Rykoff et al. , 2014)). These systems were found by filtering X-ray photon overdensities in RASS with an optical cluster finder tool using SDSS photometry. The target selection process for these targets is described fully in  Clerc et al.
SPIDERS Target selection update New for DR16 is data from "chunk eboss20,26,27". In chunk 20, SPIDERS RASS CLUS targets are obtained by extending the red-sequence search up to five times the cluster virial radius in CODEX clusters detected through their weak-lensing signature  (Shan et al. 2014). The virial radius used in the target selection is provided in the value-added catalog. Moreover, in chunks 26 and 27, we introduce three new target subclasses, taking advantage of deeper optical datasets that enable cluster member measurements at higher redshifts. • SPIDERS CODEX CLUS CFHT: Following the procedure described in  Brimioulle et al. (2013) , pointed Canada France Hawaii Telescope (CFHT)/Megacam observations and CFHT-LS fields provide deep (u)griz photometry. A total of 54 (out of 462 targets) spectra were acquired and are labelled with the bit mask EBOSS TARGET2 = 2 6 ; • SPIDERS CODEX CLUS PS1: A sample of 249 highredshift (z λ > 0.
5) CODEX cluster candidates were searched for red-sequence counterparts in PanStarrs PS1  (Flewelling et al. 2016 ) using a custom algorithm. A total of 129 (out of 1142 targets) spectra were acquired, and are labelled with the bit mask EBOSS TARGET2 = 2 7 ; • SPIDERS CODEX CLUS DECALS: These targets are output of a custom red-sequence finder code applied to DeCALS photometric data 149 (5th data release  Dey et al. 2019). A total of 48 spectra (out of 495 targets) were acquired and are labelled with the bit mask EBOSS TARGET2 = 2 8.
Bioinformatic Analyses The B. subtilis genome that was used was NC_000964. The presence and numbers of isoforms were extracted from the GtRNA database (http://gtrnadb. ucsc. edu/GtRNAdb2/ genomes/bacteria/Baci_subt_subtilis_168/)  [26]. The RNA sequence data were extracted from Modomics (http://modomics. genesilico. pl/sequences/list/?type_field=tRNA&subtype=all&species=4& display=Display&nomenclature=abbrev)  [27]  from tRNAdb (http://trnadb. bioinf. uni-leipzig. de/) [28]  and TpsiC databases (http://tpsic. igcz. poznan. pl/info/start/) [29]. The information on all genes involved in tRNA modification in E. coli and B. subtilis (Tables  S1  and S2 ) were gathered from the literature and further checked using Subtiwiki  [24] , Uniprot Id mapping tools  [30] , and BlastP  [31]  searches at NCBI  [32] , as well as Pathosystems Resource Integration Center (PATRIC) feature group tools  [33].
The SEED database  [34]  was used to analyze physical clustering. Sequences were visualized using the integrated genome viewer (IGV) (http://software. broadinstitute. org/software/igv/) [35]. Clustergrammer (http://amp. pharm. mssm. edu/clustergrammer/) was used to visualize fitness and expression data as heatmaps  [36]. The Colombos v3. 0 database (http://colombos. net/) [37]  was used to mine the available B. subtilis expression data. Using the list of predicted B. subtilis tRNA modification genes from Table  S2  as input, an expression matrix comprised of 90 experiments (or contrasts) where these genes showed significant expression was generated from the data available in the database, and further analysis was conducted on this dataset. Each contrast is a single RNA-Seq or microarray experiment where RNA transcript levels for a test condition are compared to a reference culture.
LC-MS of Nucleosides Digestion of the purified tRNA fraction was performed as previously described  [39]. Separation was accomplished by reversed phase chromatography using an Acquity UPLC HSS T3, 1. 8 µm, 1 mm × 100 mm column, (Waters, Milford, MA, USA) on a Vanquish Flex Quaternary UHPLC system (Thermo Fisher Scientific, San Jose, CA, USA). Mobile phase A consisted of 5. High resolution accurate mass (HRAM) analysis was performed on an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) interfaced with an H-ESI electrospray source in positive polarity mode. Full scan data were acquired at a resolution of 120,000, mass range 220-900 m/z, AGC 7. 5e4, and IT 100 ms. Data-dependent top speed MS/MS spectra (1 s cycle, CID 40%) were acquired in the ion trap with rapid scan rate, AGC 1.
Data-dependent top speed MS/MS spectra (1 s cycle, CID 40%) were acquired in the ion trap with rapid scan rate, AGC 1. 0e5, and an injection time of 300 ms. Data-dependent top speed MS/MS spectra (1 s cycle, CID 40%) were acquired in the ion trap with rapid scan rate, AGC 1. 0e5, and an injection time of 300 ms. The other instrumental conditions were as follows: quadrupole isolation of 1. 6 m/z; RF 30%; sheath gas, auxiliary gas, and sweep gas of 40, 8, and 1 arbitrary units, respectively; ion transfer tube temperature of 275 • C; vaporizer temperature of 320 • C; and spray voltage of 2500 V. Data were analyzed using Xcalibur 4. 0 and RAMM 1. 0  [40].
Identity and Position of tRNA Modifications in the Model Gram-Positive B. subtilis 168 A total of 86 tRNA genes specifying 35 different iso-acceptors can be identified in the Bacillus subtilis 168 genome (Figure  2 ). The RNA sequences of 24 of these have been reported (black dots in Figure  2 ), but 11 tRNA isoacceptors remain to be sequenced (red dots in Figure  2 ). To fill this knowledge gap, different methods were combined. First, a comprehensive MS analysis of the bulk tRNA extracted from B. subtilis cells was performed. These data were complemented by analysis of the RT-arresting nucleotides and 2 -O-methylation by RiboMethSeq, as well as pseudouridines by PsiSeq. [48]. The codon sequence of each amino acid is read in the inside-out direction (1-2-3). The numbers of isoforms are shown by the number in parentheses and the data were extracted from the GtRNAdb 2.
For modification mapping by LC-MS/MS, an initial survey scan was conducted using lower resolution mass analyzers (i. e. , ion trap and q-TOF), and these data provided a rough map to guide higher resolution analyses conducted on the orbitrap. The mass spectrometry (4) (1) (5) (1) (3) (1) (2) (1) (2) (4) (1) (4) (1) (1) (3) (4) (3) (1) (4) (4) (6) (2) (1) (1)   [48]. The codon sequence of each amino acid is read in the inside-out direction (1-2-3). The numbers of isoforms are shown by the number in parentheses and the data were extracted from the GtRNA-db 2. 0  [26]. A red dot specifies that the tRNA has not been sequenced, while a black dot specifies that the tRNA sequence is available based on the information available in Modomics  [27]. The "e" and "i" specify the elongator or initiator tRNA Met CAU , respectively.
(3) (3) (1) (2) (4) (4) Two types of LC-MS/MS analyses were performed. The first consisted of a total nucleoside identification assay, where an aliquot of the bulk tRNA is taken to its nucleoside monomers by enzymatic digestion to identify which modifications are present in the sample within the detection limits. In the second assay, an aliquot of the bulk tRNA is digested with base specific nucleases to create oligonucleotides that can be separated chromatographically by length and whose fragmentation ladder allows the placement of the modifications detected in the nucleoside assay into their proper sequence context. For modification mapping by LC-MS/MS, an initial survey scan was conducted using lower resolution mass analyzers (i. e. , ion trap and q-TOF), and these data provided a rough map to guide higher resolution analyses conducted on the orbitrap.
The mass spectrometry data reported here resulted from final verification using the orbitrap, with a few exceptions that are noted. Next Generation Sequencing(NGS)-based analysis of the tRNA modifications (and, eventually, rRNA modifications) was performed using three complementary techniques. First, analysis of the RT-arrests and misincorporation profiles was performed using the TTO-based Illumina library preparation kit. However, random priming during the RT-primer extension step makes this kit more suitable for long RNAs (like rRNAs) and not for short tRNAs, thus these data were only used as a complement. The second and major approach used in the study was standard RiboMethSeq, initially developed for 2 -O-methylation detection. We also noticed that, in addition to 2 -O-methylations, moderate RiboMethSeq signals were frequently detected also for pseudouridines, dihydrouridines (D), and m 7 G residues, present in tRNAs.
These data were used only as supplementary confirmation of known sites. In addition, the same RiboMethSeq datasets were used for the detection of RT-misincorporations. Direct ligation of 3 -and 5 -adapters to RNA or RNA fragments makes this approach unsuitable for the detection of hard RT-stops, but strong RT misincorporation profiles (e. g. , Inosine, m 1 A, m 3 U, m ,6,2 A, m 2,2 G, s 4 U, etc. ) were still detected, albeit with a somewhat lower efficiency due to the robustness of SuperScript III, which was used in the RT step. The third deep sequencing-based approach used a modified pseudouridine-specific chemical modification protocol and allowed the detection of candidate sites in the tRNAs. A summary of the RiboMethSeq and PsiSeq analyses of the B. subtilis tRNAs is given in Table  S3.
System Analysis of tRNA Modification Genes in B. subtilis As B. subtilis is such a prominent model organism, many whole genome datasets are available. These include mutant collections, phenotypic screens, TnSeq datasets, and gene expression data  [24]. Many of the tRNA modification genes are poorly annotated and/or were only recently discovered, so we revisited several published genome-wide studies that used 42 B. subtilis tRNA modification genes as input. Seven out of the 42 B. subtilis modification genes are essential (Figure  3 )  [38]. FolE/mtrA is essential because of its role in the synthesis of the tetrahydrofolate cofactor, but not its role in Q synthesis, as it can be replaced by folE2/yciA only under zinc limitation  [69]. TilS is essential in most bacteria  [10] , with the rare exception of organisms such as Mycoplasma mobile that have changed the minor tRNA Ile anticodon from CAU to UAU, making the lysidine modification dispensable  [97].
We extracted from these data the scores for the 35 predicted tRNA modification genes that are not essential (Table  S4 ), and we visualized these data using clustergrammer, after transforming them into Z-scores. While transforming the fitness data into Z-scores may have exaggerated the differences between the ErmR and KanR datasets, it was noted in their original publication  [40]  that discrepancies between datasets could be caused by the downstream effects of the resistance cassettes that were used to prepare each library of mutants. Since we observed differences in the fitness data between the ErmR and KanR datasets for our list of genes, only the ErmR dataset was used to prepare Figure  5. Any phenotype observations from these datasets should be experimentally validated. As shown in Figure  5 , genes in same modification pathways, such as tsaC/tsaE (t 6 A37), tgt/queA (Q), queDCEF (Q precursor), and cmnm 5 U34 (mnmE/G), have similar or the same fitness profiles, suggesting a biological relevance that will need to be further studied.
We also looked at published B. subtilis Tn-Seq studies and found that three tRNA modification genes have been identified as being involved in sporulation (mnmE, mnmG, and miaA)  [100] , and mnmG was also important for germination  [101]. ErmR dataset was used to prepare Figure  5. Any phenotype observations from these datasets should be experimentally validated. As shown in Figure  5 , genes in same modification pathways, such as tsaC/tsaE (t 6 A37), tgt/queA (Q), queDCEF (Q precursor), and cmnm 5 U34 (mnmE/G), have similar or the same fitness profiles, suggesting a biological relevance that will need to be further studied. S4. We also looked at published B. subtilis Tn-Seq studies and found that three tRNA modification genes have been identified as being involved in sporulation (mnmE, mnmG, and miaA)  [100] , and mnmG was also important for germination  [101].
Using the list of locus tags as input, we analyzed the expression of 42 B. subtilis RNA modification genes in all transcriptomic data available in the Colombos database (41 tRNA modification and one involved in rRNA modification)  [37]  (Table  S5 ). Some conditions lead to reductions or increases in most tRNA modification genes and are logically conditions where all translation genes would be expected to be affected, such as those that affect growth rate or transcription inhibition. To further explore the conditions where the response was specific to just a few genes, we focused on the conditions that led to differences in expression for just a subset of genes, as described in the Methods section. As shown in Figure  6 , we found that twelve genes involved in the synthesis of Anticodon Stem Loop modifications (ψ31/32, Q34, ψ38/39, i 6 A37, t 6 A37, k 2 C34, s 2 U34) were up or down regulated in specific stress conditions, opening possible avenues for further exploration.