dataesr/scientific-paragraphs-categorization huggingface dataset:
CycADS annotation and OncfaCyc database generation We used the Cyc Annotation Database System (Cyc-ADS  [113] ), an automated annotation management system, to integrate protein annotations from different sources into a Cyc metabolic network reconstruction that was integrated into the ArthropodaCyc database. Using our CycADS pipeline, Oncopeltus fasciatus proteins from the official gene set OGS v1. 1 were annotated using different methods-including KAAS  [150] , PRIAM  [151] , Blast2GO  [152, 153] , and InterProScan with several approaches  [154] -to obtain EC and GO numbers. All annotation information data were collected in the CycADS SQL database and automatically extracted to generate appropriate input files to build or update BioCyc databases  [155]  using the Pathway Tools software  [156].
Acknowledgements We thank  Dorith Rotenberg  ( Kansas State University, currently North Carolina State University, USA ) and  Michael Sparks  ( Agricultural Research Service, United States Department of Agriculture, USA ) for generously making available the unpublished genome assemblies of the fellow hemipteroid i5K species Frankliniella occidentalis and Halyomorpha halys, respectively, for use in specific analyses presented here. Similarly, we thank  Hans Kelstrup  and  Lynn Riddiford  ( Janelia Farm Research Campus, HHMI, USA ) for sharing unpublished data on Of-E75A RNAi. We thank  George Coupland  ( Max Planck Institute for Plant Breeding Research, Cologne, Germany ) as well as  Lisa Czaja ,  Kurt Steuber , and  Bruno Huettel  ( Max Planck Genome Centre Cologne, Germany ) for conducting the PacBio sequencing and providing support with data handling.
Genome sequencing, assembly, annotation, and official gene set overview Library preparation, sequencing, assembly, and automatic gene annotation were conducted at the Baylor College of Medicine Human Genome Sequencing Center (as in  [12, 30] ). About 1. 1 billion 100-bp paired-end reads generated on an Illumina HiSeq2000s machine were assembled using ALLPATHS-LG  [125] , from two paired-end (PE) and two mate pair (MP) libraries specifically designed for this algorithm (Additional file 1: Supplemental Note 1). Three libraries were sequenced from an individual adult male (180-and 500-bp PE, 3-kb MP), with the fourth from an individual adult female (8-10-kb MP). The final assembly, "Ofas_1. 0" (see metrics in Table  1 ), has been deposited in GenBank (assembly accession GCA_000696205. 1). Automated annotation of protein-coding genes was performed using a Maker 2.
0 annotation pipeline  [126]  tuned specifically for arthropods (Additional file 1: Supplemental Note 3). These gene predictions were used as the starting point for manual curation via the Apollo v. 1. 0. 4 web browser interface  [127] , and automatic and manual curations were compiled to generate the OGS (see also Additional file 1: Supplemental Note 4). The current version of the gene set, OGS v1. 2, is deposited at GenBank as an "annotation-only" update to the Whole Genome Shotgun project (accession JHQO00000000). Here, we describe version JHQO02000000. The annotations can be downloaded from NCBI's ftp site, ftp:// ftp. ncbi. nlm. nih. gov/genomes/all/GCA/000/696/205/GCA_ 000696205. 1_Ofas_1. 0/. The annotations are also available through the i5K Workspace@NAL  [128] , https://i5k. nal.
usda. gov/data/Arthropoda/oncfas-(Oncopeltus_fasciatus)/ Ofas_1. 0/2. Official%20or%20Primary%20Gene%20Set/GCA_ 000696205. 1_Ofas_1. 0/. Databases of the genome assembly (definitive Illumina-only: Table  1 , Additional file 1: Supplemental Note 1. 3; provisional hybrid Illumina-PacBio: see below, Additional file 1: Supplemental Note 2. 3), Maker automatic gene predictions (Additional file 1: Supplemental Note 3), and OGS v1. 1 (Table  1 , Additional file 1: Supplemental Note 4) are available through the i5K Workspace@NAL, and the Ag Data Commons data access system of the US Department of Agriculture's (USDA) National Agricultural Library as individual citable databases  [129] [130] [131] [132].
To study the structural interactions of these G6P analogues with αPMM/PGM, we utilized the protein PGM from Xanthomonas citri (XcPGM),  13  a phytopathogen, known for being the causative agent of citrus canker. XcPGM is 34% identical overall in amino acid sequence to P. aeruginosa PMM/PGM, and active site residues in the two proteins are very highly conserved (Figure  S5  in the Supporting Information). Crystals of XcPGM diffract to high resolution and are amenable to the formation of ligand complexes. 37 rystal structures of XcPGM as apo-enzymes and in complexes with G1P and G16BP were previously published (PDB IDs: 5BMN, 5BMP, and 5KLO, respectively). 37 These structures are used for comparisons with 1:XcPGM below. A high-resolution structure (1. 7 Å) was obtained for XcPGM in complex with compound 1 (PDB ID: 6N1E).
26 To a solution of compound 9 (100 mg, 0. 18 mmol) in CH 3 OH was added 20% wt Pd(OH) 2 /C (30 mg). The suspension was degassed under vacuum and saturated with H 2 in a Parr Shaker at 60 psi pressure. This reaction mixture was shaken overnight. When the starting material disappeared completely, as determined by TLC monitoring, the catalyst was removed by filtration. The filtrate was concentrated under reduced pressure to give 12 (35 mg, 100%). Data for Compound 9 (Figure  S7 ). (m, 1H), 2. 79 (s, br, 1H, OH), and 1. 47 (s, 3H). This is consistent with the reported data. 39 ata for Compound 12 (Figures  S8  and S9 ). 1  H NMR (500 MHz, D 2 O): δ 3. 79 (dd, J = 11. 5 and 2. 0 Hz, 1H), 3. 76-3. 68 (m, 2H), 3. 61 (t, J = 9. 5 Hz, 1H), 3. 35 (t, J = 9. 5 Hz, 1H), and 3. 24 (d, J = 9. 5 Hz, 1H).
13  Following the general procedure, 1 (18 mg, 86%) was obtained from compound 12 (15 mg, 31 μM), MgCl 2 (63 mg, 310 μM), ATP (200 mM, 310 μL), Glk (28 mg mL -1 , 72 μL), and Tris-HCl (50 mM, 3618 μL, pH 7. 5). Data for Compound 1 (Figures  S10  and S12 ). 1-Deoxy-1-monofluoro-α-D-gluco-heptulose 7-Phosphate (2). Subject to a two-step transformation to generate compound 10 in 62% yield over two steps. 27 Data for 10 (Figure  S13 ):  19  F NMR (470 MHz, DMSO-d 6 ): -226. 2 (t, 2 J F,H = 47. 3 Hz). This is consistent with the reported data. 27 o a solution of 10 (92 mg, 0. 16 mmol) in CH 3 OH was added 20% wt Pd(OH) 2 /C (20 mg). The suspension was degassed under vacuum and saturated with H 2 under 1 atm pressure (balloon). This reaction mixture was stirred overnight. When the starting material disappeared completely, as determined by TLC monitoring, the catalyst was filtered off and the filtrate was concentrated under reduced pressure to give 13 (34 mg, 100%).
The residue obtained was used in the next step without further purification. An analytic amount of pure sample was obtained by silica gel flash chromatography (nhexane/EtOAc = 4) for characterization purposes. Data for 6 (Figures  S19-S21 ). Hz, 1H), 3. 84 (t, J = 9. 6 Hz, 1H), 3. 78 (dd, J = 11. 0 and 1. 4 Hz, 1H), 1. 46 (t, J = 7. 1 Hz, 3H), and 1. 38 (t, J = 7. 1 Hz, 3H). 19 F NMR (470 MHz, CDCl 3 ): δ -119. 5 (ABX, 2 J F,F = 303. 7 Hz, 2 J F,P = 95. 5 Hz), -120. 3 (ABX, 2 J F,F = 303. 7 Hz, 2 J F,P = 99. 1 Hz). 31  P{ 1 H} NMR (202 MHz, CDCl 3 ): δ 6. 6 (t, 2 J P,F = 96. 2 Hz). This data is consistent with those reported. 30 rude 6 and anhydrous K 2 CO 3 (1. 40 g, 10. 18 mmol) were dissolved in anhydrous DMF (25 mL). The mixture was stirred at 55 °C under N 2. When the reaction was finished (about 3 h), as determined by TLC analysis, it was cooled to room temperature, H 2 O (15 mL) was added, and the product was extracted with diethyl ether (4 × 50 mL).
4 (d, 2 J Fa,Fb = 283 Hz) and -136. 4 (d, 2 J Fa,Fb = 283 Hz). 19 F NMR (470 MHz, CDCl 3 ): δ -133. 4 (dd, 2 J Fa,Fb = 283 Hz, 2 J Fa,H-1 = 55 Hz) and -136. 4 (dd, 2 J Fb,Fa = 283 Hz, 2 J Fb,H-1 = 56 Hz). These data are consistent with those reported. 31 o a solution of 8 (50 mg, 0. 085 mmol) in CH 3 OH was added 20% wt Pd(OH) 2 /C (10 mg). The suspension was degassed under vacuum and saturated with H 2 in a 1 atm balloon. The reaction mixture was stirred overnight. When the starting material was consumed, as determined by TLC analysis, the catalyst was filtered off. The filtrate was concentrated under reduced pressure to give 14 (19 mg, 100%). Data for 14 (Figures  S28-S30 ). 1 H NMR (500 MHz, D 2 O): δ 5. 94 (t, 2 J H-1,F = 54 Hz, 1H, H-1), 3. 93-3. 70 (m, 4H), 3. 68 (d, J = 10 Hz, 1H), and 3.
28 Data for 11 (Figure  S35 ):  19  F NMR (470 MHz, D 2 O): δ -83. 4. This data is consistent with that reported. 28 o a solution of 11 (110 mg, 0. 181 mmol) in CH 3 OH, Pd/ C (20 mg) was added. The suspension was degassed under vacuum and saturated with H 2 in a Parr shaker at 60 psi pressure. This reaction mixture was shaken for 3 days until the starting material disappeared, as determined by TLC analysis. The catalyst was filtered off, and the filtrate was concentrated under reduced pressure to give 15 (44. 4 mg, 100%). Data for 15 (Figures  S36-S38 ). Following the general procedure, 4 (15. 7 mg, 98%) was obtained from compound 15 (12. 5 mg, 50 μM), MgCl 2 (102 mg, 500 μM), ATP (200 mM, 500 μL), Glk (28 mg mL -1 , 110 μL), and Tris-HCl (50 mM, 3390 μL, pH 7. 5). Data for 4 (Figures  S39-S42 ).
Crystals were removed from the drop, dipped quickly into the ligand solution, and immediately flash-cooled and stored in liquid N 2. X-ray Diffraction Data Collection and Refinement. Diffraction data were collected at a wavelength of 1. 00003 Å from single crystals on beamline 4. 2. 2 of the advanced light source using a Taurus-1 CMOS detector in the shutterless mode. Data were processed using XDS  41  and AIMLESS  42  in CCP4i. 43 Data processing statistics are listed in Table  S1  (see the Supporting Information). Values of CC 1/2 > 0. 30  44  and R pim  45  were used to determine the high resolution cutoff due to the large number of images (900-3600 per data set) and high redundancy obtained with the shutterless data collection. Crystallographic refinement calculations were initiated using coordinates of apo-XcPGM (PDB ID: 5BMN).
Refinement was performed with PHENIX;  46  progress was monitored by following R free , with 5% of each data-set set aside for cross validation. The B-factor model consisted of an isotropic Bfactor for each atom; TLS refinement was used as automated in PHENIX. Occupancy of the ligand was also refined and converged at 0. 82. COOT  47  was used for model building. The structures were validated using MolProbity,  48  and the refinement statistics are listed in Table  S1  (see the Supporting Information). Likely hydrogen bond criteria and lengths were determined by CONTACT in CCP4. 43 Structural figures were prepared with PYMOL. 49 The coordinates and structure factor amplitudes have been deposited in the PDB under the accession numbers listed in Table  S1  (see the Supporting Information). Kinetic Inhibition Assays (IC 50 Determination) of Analogues 1-4 Against αPMM/PGM and βPGM.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsomega. 9b00008. 19 F{ 1 H} NMR and 31 P{ 1 H} NMR spectra comparison of phosphonate 6 and phosphate 7, the activity (plots) of αPMM/PGM and βPGM in response to inhibitors (1-4), sequence alignment of X. citri PGM and P. aeruginosa PMM/PGM, X-ray data collection and refinement statistics for 1 complex, polder omit map, characterization data, and NMR spectra of new compounds (1-4, 7, 12, 14 and 15) (PDF) ■ AUTHOR INFORMATION Corresponding Authors *E-mail: beamerl@missouri. edu (L. J. B. ). *E-mail: David. Jakeman@dal. ca (D. L. J. ).
Abstract. We present the Copernicus in situ ocean dataset of temperature and salinity (version 5. 2). Ocean subsurface sampling varied widely from 1950 to 2017 as a result of changes in instrument technology and the development of in situ observational networks (in particular, tropical moorings for the Argo program). Thus, global ocean temperature data coverage on an annual basis grew from 10 % in 1950 (30 % for the North Atlantic basin) to 25 % in 2000 (60 % for the North Atlantic basin) and reached a plateau exceeding 80 % (95 % for the North Atlantic Ocean) after the deployment of the Argo program. The average depth reached by the profiles also increased from 1950 to 2017. The validation framework is presented, and an objective analysis-based method is developed to assess the quality of the dataset validation process.
Objective analyses (OAs) of the ocean variability are calculated without taking into account the data quality flags (raw dataset OA), with the near-real-time quality flags (NRT dataset OA), and with the delayed-time-mode quality flags (CORA dataset OA). The comparison of the objective analysis variability shows that the near-real-time dataset managed to detect and to flag most of the large measurement errors, reducing the analysis error bar compared to the raw dataset error bar. It also shows that the ocean variability of the delayedtime-mode validated dataset is almost exempt from randomerror-induced variability.
Introduction Estimating the temperature and salinity ocean state is critical for documenting the evolution of the ocean and its role in the present climate. To do so, the scientific community relies on in situ measurements at a global scale and from global datasets. Among the global datasets, one can cite the World Ocean Database  (Boyer et al. , 2013; hereafter WOD)  and the EN4 database  (Good et al. , 2013 ; http://www. metoffice. org/, last access: May 2018) distributed by the UK Meteorological Office. Here, we present CORA (Coriolis Ocean dataset for ReAnalysis), a dataset distributed by the Copernicus Marine Environment Monitoring Service (hereafter CMEMS) and produced by Coriolis. CORA differs from these earlier datasets in terms of choices in the construction and the production of the dataset.
Indeed, WOD is validated with the highest quality control methods at 102 vertical levels, whereas the EN4 profiles are limited to a maximum of 400 vertical levels and are automatically validated  (Ingleby and Huddleston, 2007). CORA conversely retains data at the highest vertical resolution. The choice of reducing the number of levels in the data validation and in the dataset construction helps to quickly cluster new measurements in the dataset and provides easy-to-handle datasets. On the other hand, these methodologies result in a loss of measurements potentially available for the scientific community through the vertical sampling of the profiles or in the data validation. In the construction of CORA, all the measurements available are kept, and then an automatic validation is first performed, followed by a manual and/or individual check  (Gaillard et al.
, 2009; Cabanes et al. , 2013). This validation framework Published by Copernicus Publications on behalf of the European Geosciences Union. requires the production of two datasets: a near-real-time validated dataset distributing the profiles within days after collection and a delayed-time validated dataset covering in year n the historical period up to year n -1. This choice, made in the early versions of CORA, has been retained in the latest one that we describe here. A global ocean heat content (GOHC) increase has been observed on decadal timescales, whether it is in the upper layers of the ocean  (Domingues et al. , 2008; Ishii and Kimoto, 2009; Levitus et al. , 2009) , below the thermocline  (Von Schuckmann and Le Traon, 2011) , or in the abyss  (Purkey and Johnson, 2010). In addition to the influence of the mapping method and the baseline climatology  (Abraham et al.
, 2007). The validation of a quality control method is thus a critical task to ensure that the dataset flags are accurate enough to flag erroneous measurements without biasing the dataset. The uncertainty surrounding the quality assessment of a large oceanographic dataset being a critical topic in ocean climate studies, we propose here a method of global dataset quality assessment and apply it to near-real-time validated and delayed-time-mode validated datasets. We will first list the data sources of the CORA measurements in Sect. 2. A description of the CORA data space and time repartition will be reported on Sect. 3. Then, the quality control procedure will be described in Sect. 4. Lastly, gridded temperature and salinity fields are calculated using an objective mapping that is presented in Sect.
Data providers The CORA 5. 2 dataset is an incremental version of the previous CORA datasets, covering the period 1950 to the present and distributed by CMEMS. Most of the CORA profiles are first collected by the Coriolis data center and validated in near-real-time mode. Coriolis is a Global Data Assembly Center (DAC) for the Argo program  (Roemmich et al. , 2009). It collects Argo profiles from the regional Data Assembly Centers (DACs) and distributes them to the community. Coriolis also collects XBT, CTD (conductivity, temperature, depth), and XCTD measurements from French and European research programs as well as from the Global Telecommunication System (GTS), Voluntary Ship System (VOS), and subtropical mooring networks (TAO/TRITON/RAMA/PIRATA programs from the Pacific Marine Environmental Laboratory -PMEL).
A major effort has also been made to include smaller datasets in the Coriolis dataset that are available in delayed-time mode, such as ice-tethered profiler (ITP) and CTD profiles from the ICES program, sea mammal measurements from MEOP (http://www. meop. net, last access: May 2018), and validated surface drifter data. Delayed-time-mode measurements have also been downloaded from the Word Ocean Database (WOD13) and the French Service Hydrographique de la Marine (SHOM). It should be noted that in the case of a profile distributed by Coriolis in real-time mode and by one of these datasets in delayed-time mode, the delayed-timemode validated profile replaces the real-time-mode profile in the CORA database. Last, recent comparisons of the CORA profile positions with the EN4 dataset (https://www.
metoffice. gov. uk/, last access: May 2018) have shown that some of the profiles distributed in EN4 were not in the CORA previous versions. A partnership with the EN4 teams allowed us to detect and to import most of those profiles. A total of 5 069 864 profiles have been imported in this way, covering the period 1950-2015. However, contrary to the other measurements, the profiles from the EN4 database are not reported with a pressure measurement, but instead with depth and with a maximum number of reported levels in an individual profile set to 400. The issue of the inhomogeneity in the dataset with respect to the vertical sampling will be discussed.
Dataset description CORA aims to provide a comprehensive dataset of in situ temperature and salinity measurements from 1950 to 2017. The oceanic temperature and salinity measuring instruments have, however, radically changed during the last 70 years. As a result, the origin and characteristics of data distributed in the CORA dataset widely vary in time (Fig. 1 ). Most of the profiles collected prior to 1965 are mechanical bathythermograph (MBT) measurements or Nansen casts. From the late 1960s to 1990, the most common profiles are from the expendable bathythermographs (XBTs) developed during the 1960s and widely used by navies. Most of the XBT profiles collected during this period are T-4 type sensors, measuring temperature above 460 m of depth. The development of the Sippican T-7 instrument with a maximum depth of 1000 m slowly increased the number of measurements between 460 and 1000 m during the 1980s (see Fig.
2  for the dataset measurement distribution with depth). An instrument capable of measuring conductiv- ity, temperature, and pressure (CTD) was developed in the 1960s, allowing for an accurate estimation of sea salinity and temperature. The yearly number of CTD profiles in the CORA dataset then slightly increased, reaching a plateau of about 20 000 profiles in the early 1990s. During this period, the largest density of profiles is found in the North Atlantic Ocean, with a coverage ratio, calculated on a 3 • per 3 • grid with a 1-year time step, increasing from 30 % in 1950 to a plateau of 60 %-70 % in the 1970s (Fig. 3 ). The North Pacific mean sampling rate is lower than 10 % before 1965, with the largest portion of the collected profiles located close to the Japanese and North American coasts and along a transect connecting the US west coast to the Hawaiian archipelago (not presented).
It quickly increases from 1965 to 1970 to reach about 50 % in the early 1980s with a more homogeneous spatial resolution. Before 1974 in the other ocean basins, most of the collected profiles are found in the coastal zone and along a few ship tracks. The coverage then slightly increases in the western part of the Indian Ocean and in the eastern part of the South Pacific Ocean, increasing the associated basin sampling rate from 10 % in 1965 to 20 %-25 % in 1990. The Austral Ocean sampling rate remains, however, around 5 % during the whole period. During the 1990s, the yearly number of XBT profiles strongly decreased, while the number of bottles and CTD profiles slightly increased. The counterintuitive behavior is mostly caused by a lack of XBTs in the Coriolis database during the 1990s. The yearly number of XBTs should indeed decrease slowly during the 1990s and reach the CORA level by the end of the decade.
This problem should, however, be fixed in the next version of CORA. The measurements pro-vided are, however, deeper than in the previous decade, leading to better coverage below 500 m of depth (Fig. 2 ). The profile number then exponentially increases since the development of the TAO/RAMA/PIRATA equatorial mooring program throughout the 1990s. During this time, the North Atlantic and the North Pacific Ocean spatial sampling rates decrease, and the global ocean sampling rates reach a plateau at 20 %. The ocean sampling rate rapidly increases in the early 2000s thanks to the development of autonomous profilers and the worldwide Argo program. The global ocean sampling rate reaches 70 % before the mid-2000s with a maximum of 85 % in the northern Atlantic Ocean. Notice the simultaneous growth of the autonomous profiler measurements (Fig.
The dataset structure we retained is,  however, easier to handle by the ocean reanalysis community and leads to a more homogeneous dataset file structure. This dataset structure is also adopted for the mooring measurements, which in some cases also collect data at high frequency. This large number of mooring data induces a large increase in measurements such as at 250 and 500 m depths, whereas at the surface, the large increase is due to data from TSGs and drifting buoys.
Data quality control The measurements collected by the Coriolis data center are distributed to the scientific community with a near-real-time quality control flag within days of the data reception and with a delayed-time-mode validation quality control within a year. The Coriolis data center validation workflow scheme is given in Fig. 4. The quality control flags applied on the CORA dataset are associated with a measured or a calculated variable (TEMP_QC, PSAL_QC, DEPTH_QC, PRES_QC), with on the date and position variable (POSITION_QC and JULD_QC), and with the corresponding adjusted variables when they exist. The QC flag values applied during the quality control process vary from 1 to 4, with 1: good data, 2: probably good data, 3: probably bad data, and 4: bad data. Numerous measurements distributed by Coriolis have, however, been validated by scientific teams prior to integration into the Coriolis streamflow.
The most important of these Ocean Sci. ,  15, [1601] [1602] [1603] [1604] [1605] [1606] [1607] [1608] [1609] [1610] [1611] [1612] [1613] [1614] 2019  www. ocean-sci. net/15/1601/2019/ In such cases, the current practice at Coriolis is to retain the flags from the imported database and to run the delayed-timemode tests afterwards.