dataesr/scientific-paragraphs-categorization huggingface dataset:
ATom In this work we use the NOAA Picarro G2401-m in situ measurements of CO, CH 4 and CO 2 from ATom. More information on the ATom NOAA Picarro data can be found at https://espo. nasa. gov/atom/instrument/NOAA_Picarro. Because the 10 s average ATom Picarro data are given in mixing ratios in ppm (or dry air mole fractions) at a vertical resolution comparable to the RTM/RTA layering, we simply interpolate these data to the RTA levels (layer boundaries), then convert to layer abundances (molecules/cm 2 ) for the statistical assessment of the NUCAPS EDRs. This is in contrast to the molecule-conservation approach required for high-resolution data (e. g. , AirCore) as briefly alluded to in the previous section. Relevant to the JPSS requirements, the ATom statistics on total columns in Section 4 are computed as follows.
Conclusions and Future Work This work has presented the formal validation of NOAA-20 and SNPP NUCAPS IR atmospheric carbon trace gas profile EDRs (CO, CH 4 and CO 2 ), in continuation of the validation of the T, q and O 3 profile EDRs described in earlier papers  [21] [22] [23] [24]. Because of the NUCAPS cloud-clearing methodology, the NUCAPS atmospheric profile EDRs, including trace gases, are retrieved under global, non-precipitating conditions, allowing the benefit and advantage of twice-per-day (per satellite) global yields on the order of 40-70%. The NUCAPS IR sounder validation strategy employs a "hierarchical" approach drawing upon multiple independent baseline truth datasets  [26] , including TCCON ground-based spectrometers, AirCore profiles, and ATom aircraft-based in situ profiles.
Acknowledgments: The TCCON data were obtained from the  TCCON Data Archive  hosted by CaltechDATA at https://tccondata. org. Atmospheric Tomography (ATom) Mission data were obtained at https://doi. org/10. 3334/ORNLDAAC/1613. We are grateful to  M. Kopacz  ( NOAA/UCAR ), and other contributors to the NUCAPS development/validation effort, especially  C. D Barnet ,  N. Smith  and  R. Esmaili  (STC),  X. Xiong  ( NASA/LaRC ),  E. Maddy  ( Riverside Technology, Inc. ), and  W. W. Wolf  and  A. K. Sharma  ( NOAA/NESDIS/STAR ). Finally, we express our appreciation to the 4 anonymous reviewers who provided constructive feedback that we used to improve the quality of these papers. The views, opinions, and findings contained in this paper are those of the authors and should not be construed as an official NOAA or U.
Funding: This research was supported by the  NOAA Joint Polar Satellite System (JPSS-STAR) Office and Cal/Val Program  ( M. D. Goldberg ), along with the  NOAA/NESDIS/STAR Satellite Meteorology and Climatology Division. The Atmospheric Tomography Mission (ATom)  is a  NASA Earth Venture Suborbital 2 project  funded by  NASA's Earth Science Division ; NOAA Picarro measurements on ATom were supported by  NASA  grant # NNX16AL92A  to the  University of Colorado. The Paris TCCON site has received funding from  Sorbonne Université , the  French research center CNRS , the  French space agency CNES , and  Région Île-de-France. The TCCON stations at Rikubetsu, and Burgos are supported in part by the  GOSAT  series project. Local support for Burgos is provided by the  Energy Development Corporation  (EDC, Philippines).
NMD is funded by  ARC   Future Fellowship   FT180100327. Darwin and Wollongong TCCON stations are supported by  ARC  grants  DP160100598 ,  LE0668470 ,  DP140101552 ,  DP110103118  and  DP0879468. The TCCON stations Garmisch and Zugspitze have been supported by the  European Space Agency (ESA)  under grant  4000120088/17/I-EF  and by the  German Bundesministerium für Wirtschaft und Energie (BMWi)  via the  DLR  under grant  50EE1711D.
To our knowledge, there are currently only three genome size estimates reported for species belonging to section Oncocyclus  [14] : I. lortetii Barbey ex Boiss. (2C = 15. 46 pg), I. sofarana Foster (2C = 11. 5 pg) and I. sofarana subsp. kasruwana (Dinsm. ) Chaubhary, G. Kirkw. & C. Weymouth (2C = 16. 36 pg). These values rank average to low compared with the C-value estimates for 39 Iris species (c. 15%) reported in the Plant DNA C-values database release 7. 1. [15, 16]  which range from 2. 1 to 56. 4 pg/2C, with a mean of 18. 11 pg/2C (calculated with the "prime estimate" search option that gives one value per species and cytotype). The growing pool of evidence aiming to link changes in genome size to speciation and speciation rate (see  [17]  for an overview) and to species radiation  [18] [19] [20] [21]  makes a closer study of genome size dynamics within section Oncocylus worthwhile.
Iris Phylogenetic Reconstruction Nucleotide sequence data for 20 Iris species and subspecies have been deposited in the DDBJ/EMBL/GenBank (accessions MW110365-MW110415 for trnL-trnF, MW110416-MW110469 for matK-trnK; Table  S1 ). The phylogenetic tree resulting from the Bayesian analysis of the combined dataset is presented in Figure  1. Separate analyses of the different markers (data not shown) did not reveal any incongruences with branch support. Our results are consistent with the subgeneric and sectional divisions of the genus Iris, although in subgenus Iris most accessions fell in an unresolved polytomy (Figure  1 ). The analysis of multiple accessions per species yielded no conflict, except for the Lebanese populations from Yammouneh and Quaa attributed to Iris persica L. These appeared more closely related to I.
regis-uzziae Feinbrun, I. aucheri (Baker) Sealy, I. nusairiensis Mouterde and I. galatica Siehe than to the two Turkish accessions of Iris persica (accessions "Usta T02-15",  [22] ; "Chase 13045",  [23] ; Figure  1 ). For sequences gathered from GenBank, the voucher reference is given in brackets after the species name. Infrageneric classification follows  [24]. Upper left: boxplots with individual jitter values for genome size accessions, depicting the distribution of mean 2C-values per species (and cytotypes when genome size data suggest different ploidy levels) throughout the Iris subgenera and sections which have genome size data available (Table  S2 ). For sequences gathered from GenBank, the voucher reference is given in brackets after the species name. Infrageneric classification follows  [24].
Upper left: boxplots with individual jitter values for genome size accessions, depicting the distribution of mean 2C-values per species (and cytotypes when genome size data suggest different ploidy levels) throughout the Iris subgenera and sections which have genome size data available (Table  S2 ). [25]. 3 Buffer: (1) GPB  [28]  with 3% PVP-40 and 8% Triton added, (2) Galbraith's buffer  [29]. 4 Chromosome number: asterisk indicates data determined in  [13]  on the same individuals measured for genome size; other data are from the Chromosome counts database  [30] , and, for I. persica, from  [31, 32]. 5 Pollination strategy: (1) shelter mimicry, (2) sexual deception, (3) other pollination strategy, from  [4].
5 pg) than any other Iris from section Oncocyclus, we reassessed this value measuring the exact same population of Dahr El-Baydar with the same calibration standard and extraction buffer, and obtained 2C = 16. 22 pg. With this new value, the genome size in section Oncocyclus varied only 1. 27-fold, ranging from 2C = 15. 13 pg in I. camillae Grossh. to 2C = 19. 24 pg in I. bismarckiana Damman & Sprenger. We confirmed the overall genome size variation of Oncocyclus irises by co-processing leaves from two individuals with genome sizes that cover nearly the full range of C-values estimated for this section (Figure  2 ). In addition to the 50 populations whose genome sizes were assessed in this study, we gathered published data for 53 species (  [14, [33] [34] [35] [36] [37] [38] [39] [40] [41] [42]  from the Plant DNA C-values database release 7.
Purified PCR products were sequenced by Eurofins MWG, France using the Sanger method on an ABI 3730xL platform. The dataset, comprising the new sequences generated here together with sequences obtained from GenBank, was aligned using the default settings on the Guidance webserver  [55]  and manually adjusted with BioEdit software  [56]. It comprises the matK-trnK region for 117 accessions (representing 1842 characters) and the trnL-trnF region for 94 accessions (1028 characters). Dietes robinsoniana (F. Muell. ) Klatt was included in the analysis as an outgroup species. Bayesian inference (BI) was carried out using MrBayes version 3. 1. 2 [57]  on the CIPRES server  [58]  with partitions by region (trnL-trnF, matK, and trnK) and, when applicable, by codon position. The best-fit model of nucleotide Plants 2020, 9, 1687 9 of 13 substitutions selected with jModelTest 0.
Tempo and Mode of Character Evolution Box-plots showing the distribution of genome size values in the nine subgenera of Iris and four sections within subgenus Iris were generated with the package ggplot2  [64]  implemented in R v. 3. 2. 2  [65]  using the new genome size assessments together with previously published data (Table  S2 ). Analyses focused on section Oncocyclus were conducted using the phylogenetic inference of  [8] , pruned with BayesTrees v. 1. 3 [66]  to the set of species with available genome sizes, and made ultrametric using the chronos function in the package ape  [67]  implemented in R v. 3. 2. 2  [65]. Subspecies of I. iberica Steven and I. sofarana were kept in the pruned tree since they do not group with their conspecifics in the phylogeny of  [8]. Ancestral character states were reconstructed with the Phytools package of R  [68]  implemented in R v.
3. 2. 2  [65]  under ML using the fastAnc and contMap commands. The strength of the phylogenetic signal for genome size was estimated using Pagel's λ and Blomberg's K (phylosig function of Phytools). The Pagel's λ method is used to transform the original phylogeny (using the λ parameter) so that it best predicts the distribution of a given trait on the phylogeny under a Brownian Motion model of trait evolution  [69]. The null hypothesis is λ = 0, indicating no phylogenetic signal. Blomberg's K method quantifies the degree to which variation in a trait is predicted by the structure of a given phylogeny under a Brownian Motion model of trait evolution  [69]. The continuous-character model evaluation and testing (CoMET) module  [70]  implemented in MESQUITE V. 3. 61  [71]  was used with default parameters to determine which model of evolution best fitted our data.
Acknowledgments: We thank  Frédéric Dépalle  for providing materials,  Georges Tohmé  and  Nasser Chreif  for their help in field collection,  Mickael Bourge  and  Mario Gomez Pacheco  for technical support in flow cytometry, and  Benjamin Coquillas  for help in figure processing. The DNA bank and the living collections of the  Royal Botanic Gardens, Kew , are also acknowledged. We thank the reviewers for their comments and suggestions.
Therefore, before determining the epicenters listed in the TXAR catalog (Data Set S1), we adjusted the TXAR-determined back azimuths by adding 3°. This relationship removes the azimuth bias for events in the Pecos Cluster and thus may not be correct for events elsewhere in West Texas. However, the correction is nearly identical to the back azimuth correction factor developed for TXAR by  Tibuleac and Herrin (1997)  for sources situated due north of the array. The ellipsoid fitting the location differences for the 140 epicenters (see Figure  5 ) suggests the 2σ location accuracy for TXAR catalog is about ±27. 4 km EW and ±12. 4 km NS. This is approximately comparable to the location accuracy for small West Texas earthquakes reported by the USGS prior to the installation of TexNet  (Frohlich et al.
, 2019). Because eight of the stations were at similar distances (55-110 km) and distributed nearly uniformly around the epicenter, the epicenters were minimally influenced by the choice of the velocity model used for location. As discussed further in section 3. 2, the focal depths depend critically on (S-P) intervals at the closest station 327A. Comparison of epicenters determined using (S-P) intervals and apparent direction at TXAR with epicenters determined by the TexNet seismic network using conventional location methods. Data are for 140 earthquakes within Pecos Cluster Circle S3b; all plotted events had P and S picks that the analyst assigned quality 1 or 2 (red circles in Figure  3 ). The ellipsoid indicates the 2σ fit for these data; the green dot at -10. 9 km is the center of the ellipsoid-this indicates that before back azimuths for TXAR locations are adjusted by adding 3°(see text), they average 10.
Data Concerning Injection and Production of Fluids The Texas Railroad Commission (RRC) archives information about both production and injection wells operated in support of petroleum production in Texas. This information includes well locations, depths, permitting history, and monthly rates of injection/production of fluids and gases. The RRC database is publicly available online and is mostly complete for the past two decades. In addition, data for treatment fluids used for hydraulic fracturing are available from FracFocus; current regulations do not require reporting these data and they are incomplete, especially prior to about 2014, but when the data are available they do provide information about hydraulic fracturing activity. For this investigation, we compiled RRC and FracFocus data for wells in West Texas counties, removing some obvious data blunders (e.
Epicenters 2000-2017 Determined Using Data From the Lajitas TXAR Seismic Array Evaluation of certain event groups within the TXAR catalog suggests that the detection capability of TXAR does not change significantly over time. For example, for the 244 TXAR catalog events within Circle 7a (see Figure  1a ) surrounding the 1995 Alpine M6 earthquake, a time-of-day versus year-of-occurrence plot (Figure  7a ) shows that events occur approximately uniformly throughout the day, while the rate of occurrence decreases only slowly between 2000 and 2017. This decrease likely represents a slow cessation of aftershock activity following the 1995 earthquake, roughly consistent with an Omori 1/t decay rate (e. g. , the 13-18 events/year rate observed in 2005-2006, 10 years after the mainshock, is about twice the 7-9 events/year observed in 2015-2016 after 20 years).
In comparison, TexNet detected 160 nighttime events in Circle 7c over this same time period, of which 134 (84%) were in the TXAR catalog. We presume that the 16% missed rate for TXAR is attributable to various kinds of noise and interfering events that affect the single-array F-K event detection/identification algorithm. Individuals using the TXAR catalog (Data Set S1) should be aware that it misses some earthquakes, even some with magnitudes of 1. 5 and greater, but it contains considerably more seismic events than other available regional catalogs. Both the TexNet and TXAR catalogs report to lower magnitudes and exhibit lower completeness magnitudes than the USGS catalog. During the same time period in late 2017, the USGS catalog reported nine nighttime earthquakes, all greater than magnitude 2, with epicenters in Circle 7c, for example.
1 and 9. 7 km relative to the surface (7. 3-8. 9 km BSL). We favor phase 1 as it is the first arriving, more direct S arrival, traveling 10. 1029/2019JB017737 Journal of Geophysical Research: Solid Earth the shorter path from source to station. The depths determined using phase 1 (4. 0-5. 2 km BSL) are supported by independent data from the TexNet catalog. In 2018 TexNet located six earthquakes with M L ≥ 1. 2 within 3 km of the 2009 earthquakes and all reported focal depths between 3. 3 and 6. 1 km BSL. To assess whether these 2009 earthquakes were the first events in this location, we cross correlated the threecomponent TX31 seismograms for these five earthquakes with seismograms for all prior events in the TXAR. Time-of-day versus year-of-occurrence for selected seismic events. The three panels describe temporal properties of seismic events within Circles 7a, 7b, and 7c in Figure  1a.
Relationship of Seismicity to Oil and Gas Industry Activity Maps of oil production, gas production, saltwater disposal volumes, and hydraulic fracturing fluid volumes (see Figures 9, 10, 11, S5, S6, S7, and S8) provide no obvious explanation as to why Pecos Cluster activity commenced in Reeves County, either in September 2007 or May 2009. When the seismicity began, petroleum production and injection had been ongoing for years along the county line between Reeves and Ward Counties. RRC monthly data on oil and gas production, and salt water disposal (SWD) and FracFocus data do not differentiate between conventional and unconventional production or source of disposed fluids. Within 10 km of the 2009 epicenters, RRC data indicate that more than 25,000 bbl of oil and 100,000 mcf of gas were produced for every month, back to before 2000.
Figure5. Comparison of epicenters determined using (S-P) intervals and apparent direction at TXAR with epicenters determined by the TexNet seismic network using conventional location methods. Data are for 140 earthquakes within Pecos Cluster Circle S3b; all plotted events had P and S picks that the analyst assigned quality 1 or 2 (red circles in Figure3). The ellipsoid indicates the 2σ fit for these data; the green dot at -10. 9 km is the center of the ellipsoid-this indicates that before back azimuths for TXAR locations are adjusted by adding 3°(see text), they average 10. 9 km west of TexNet locations.
A list of primary antibodies is provided in Supplementary Table  1. For each antibody, optimal dilution was determined by titration. Cell sorting was carried out using a FACSAria III (BD Biosciences, San Jose, CA, USA). Data were analyzed using FlowJo 9. 9. 4 (RRID: SCR_008520). Dead cells were excluded from analyses using propidium iodide and non-pancreatic lineages were excluded using the pan-hematopoietic marker CD45, the erythroid marker TER119, and the endothelial marker CD31. Cells sorted for qPCR or transcriptomics were collected directly in Buffer RLT for qPCR (Qiagen, Hilden, Germany) or Buffer RL (#51800, Norgen BioTek Corp. , Thorold, ON, Canada) for next generation sequencing.
EndoC-ßH1 cell culture and treatment The female human beta-cell line EndoC-ßH1 (RRID: CVCL_L909) was cultured as previously described and authenticated using short tandem repeats  [21]. Twenty hours before FACS staining and conjugated-transferrin incorporation, cells were cultured in DMEM at 0. 5 mM or 5. 5 mM glucose, and then serum-starved in KRBH buffer (NaCl 115 mM, KCl 5 mM, NaHCO3 24 mM, CaCl2 2. 5 mM, MgCl2 1 mM, HEPES 10 mM, BSA 0. 1% w/v) supplemented with 20mM glucose for 30 min, followed by TF-CF568 incorporation for 30 min as described above. Cells were then rinsed in 1x PBS and detached with 0. 05% trypsin-EDTA for 3 min at 37°C, rinsed in 1x PBS + 20% FBS before subsequent FACS staining, as described above. Primary antibodies and their optimal working dilutions are listed in Supplementary Table  1.
Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, a primary analysis based on AOZAN software (Genomic Paris Centre, Ecole Normale Supérieure, Paris, France) was applied to demultiplex and control the quality of the raw data (based on FastQC modules / version 0. 11. 5). Sequencing reads were mapped to the mouse genome version GenCode M23 (RRID: SCR_014966, GRCm38. p6 release 93) using STAR v2. 5. 2b  [22]  (RRID: SCR_015899). Transcripts were quantified using the RSEM software tool  [23]  (RRID: SCR_013027). Heatmaps were constructed using the pheatmap R package (RRID: SCR_016418). Gene set enrichment analysis was done with GSEA v4. 0 software  [24; 25]  (RRID: SCR_003199).
CD71 surface levels on the beta-cellenriched fraction are at ~30% of the level on FL TER 199+ cells, while only at ~8% on the alpha-cell-enriched fraction (Fig  1c ). We next analyzed the expression of endocrine and exocrine genes by qRT-PCR in the four FACS-sorted subsets. The CD24 low CD71 -and CD24 low CD71 + subsets were consistently enriched in Glucagon (Gcg) and Insulin (Ins1) transcripts, respectively, while the CD24 high CD49f -subset was highly enriched in Somatostatin (Sst) transcripts (Fig  1d ). Ghrelin (Ghrl) transcripts were expressed at similarly low levels in all sorted subsets (data not shown). Low levels of acinar (Cpa1) and ductal (Sox9) transcripts originating from contaminating exocrine cells in the islet preparations were confined to the CD24 high CD49f + subset which was devoid of endocrine transcripts (Fig  S1b-c ).
Next, we compared the dataset generated by DiGruccio et al. (2016) that used hormone reporter mice with ours and found both to be highly comparable in terms of relative expression of the main hormone transcripts in the different endocrine subsets, indicating similar purity (Fig  S3c ). Collectively, the expression pattern of these gene sets confirmed the enrichment of bona fide betacells in the CD24 low CD71 + fraction, of alpha-cells in the CD24 low CD71 -fraction, and of delta-cells in the CD24 low CD49f -fraction. To investigate whether iron metabolism was higher in the beta-cell-enriched CD24 low CD71 + fraction, we performed an enrichment analysis for iron-related gene sets. We found that three such gene sets were enriched in the beta-cell-enriched fraction compared to the alpha-cell-enriched fraction (Fig  6b-d , Supplementary Tables  4 5 6 ).
Color code as in (a). (f) Schematic of the major proteins for which the iron metabolism genes analyzed in (e) encode, colored red when more expressed in the beta-cell-enriched fraction, and blue when less expressed compared to the alpha-and delta-cell-enriched fraction. (g-h) Tfrc and Ftl1 expression was assessed by RT-qPCR in (g) adult CD24 low CD71 + (blue), CD24 low CD71 -(purple), and CD24 high (red) fractions (h) P1 and adult CD24 low CD71 + fractions. Data is a pool of 3 independent experiments. One-way-ANOVA for (g) and unpaired t-test for (h). (i) Tfr1, Ins1, Iapp, Pdx1, Nkx6-1, Mafa and Gcg transcript expression in control and DFO-treated islets. Mann-Whitney test for Tfrc and unpaired t-test for all other genes. (j) Glucosestimulated insulin secretion (left) and insulin content (right) from control and DFO-treated islets.
(g-h) Tfrc and Ftl1 expression was assessed by RT-qPCR in (g) adult CD24 low CD71 + (blue), CD24 low CD71 -(purple), and CD24 high (red) fractions (h) P1 and adult CD24 low CD71 + fractions. Data is a pool of 3 independent experiments. One-way-ANOVA for (g) and.
Acknowledgments We thank  L Rachdi ,  P Launay ,  C Peyssonnaux ,  H Heimberg ,  N De Leu ,  Y Heremans , and  J van den Ameele  for discussion and comments on the manuscript;  S Pechberty  and  C Lourenço  for assistance in some experiments;  H Heimberg  and  G Leuckx  ( Vrije Universiteit Brussel, Belgium ) for providing the Ins-YFP mice; the Genom'ic platform at  Institut Cochin  for the transcriptome analyses;  G Gu  ( Vanderbilt University Medical School, USA ) for sharing their protocol for isolating neonatal pancreatic islets. The  CRT platform at Institut Pasteur (Paris, France)  for access to their single cell qPCR set-up.
2 min to 5. 7 min in negative mode at a mass resolution of 140,000 (at m/z 200), automated gain control (AGC) of 3 × 10 6 and with a maximal injection time of 3000 ms. Free cholesterol was quantified by parallel reaction monitoring FT MS/MS within runtime 1. 51 to 4. 0 min in positive ion mode. For FT MS/MS (PRM method), the micro scans were set to 1, isolation window to 1 Da, stepped normalized collision energy to 15, 25, 35, AGC to 2 × 10 4 and maximum injection time to 650 ms. All acquired data was filtered by PeakStrainer that can be found in gitlab https://git. mpi-cbg. de/labShevchenko/PeakStrainer/wikis/About (Schuhmann, et al. , 2017). Lipids were identified by LipidXplorer 1. 2. 7 software  (Herzog, et al. , 2012). Molecular Fragmentation Query Language (MFQL) queries were compiled for TAG, DAG, Cholesterol, CE, SM, PC, PC O-, LPC, LPC O-, PE, PE O-, LPE, PI, LPI lipid classes.