dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the prospective nature of the data collected on prescription fillings, information on medication use do not suffer from recall bias, and appropriate medication filling algorithms can limit bias resulting from drug non-compliance. Because of the administrative nature of the databases used, data on smoking, alcohol and illicit drug use as well as caffeine intake, and maternal weight and weight gain during pregnancy are missing. Although this is a limitation, using appropriate study designs and medication filling algorithms can circumvent it. This study suffers from some limitations inherent in the use of reimbursement databases. As drug dispensation was used as a proxy for drug exposure, we never know whether patients are actually taking their medications. However, De Jonge et al. [34]  found more than 90% compliance during pregnancy, in particular for chronic treatment.
Furthermore, Zhao et al. [35]  have found that prescription fillings during pregnancy in administrative database have good positive predictive values  [35]. In addition, register data on maternal health outcomes is a 'proxy' measure, and probably underestimating of the real world situation. Finally, the exact admission, discharge, and medical procedure dates are routinely recorded in the PMSI database for 97. 0% of all births and 91. 7% of all inpatient induced abortion  [36]. In our study the proposed algorithm to identify pregnancies was based on ICD-10 diagnostic codes and medical procedure codes that may be subject to coding errors. However, as the PMSI database is used for planning and funding purposes and is subject to coding quality control, coding errors should therefore be limited  [10] [11] [12] 37].
Conclusion In conclusion, the FPC has the advantage of including a representative sample of French pregnant women, and study medications only available in France in addition to others available worldwide. The large number of pregnancies in the cohort provides the power needed to measure maternal pregnancy outcomes. The FPC provides information to measure potential confounding variables, especially valid gestational age, which ensures accurate timing of drug exposure. We believe that the FPC, built with data from a representative sample of the French population (EGB), is a good tool to study medication exposure during pregnancy and its effect on the mother and children. Given the observational nature of studies on the impact of medication use during pregnancy, replication of findings is key to causality assessment.
All targets in the XL-assisted modeling category were solved by X-ray crystallography and provided to the XL-MS labs as purified protein samples. CASP organizers asked some of these X-ray crystallography groups to share purified protein samples. The primary focus was on difficult-to-model protein targets, for which there were no trivial templates available in structural databases. The samples were shipped to two research groups specializing in chemical crosslinking and mass spectrometry: Alexander Leitner's group (Zurich) and Juri Rappsilber's group (Berlin, Edinburgh). Some proteins were shipped to both groups, while some to only one (A. L. ). The two groups used different experimental approaches to generate the crosslinking data. The data were released to modelers after the prediction window for the corresponding regular target (modeling without data assistance) was closed.
| Targets Purified protein samples of 8 regular CASP13 targets-H0953, H0957, H0968, T0975, T0981, T0985, T0987 and T0999-were provided by Matthew Dunne (ETH Zurich, target H0953), Karolina Michalska (Argonne National Lab, H0957 and H0968), Chi-Lin Tsai (UT MD Anderson Cancer Center, T0975), Mark van Raaij (Centro Nacional de Biotecnologia of Spain, T0981), Jose Henrique Pereira (Lawrence Berkeley Lab, T0985), Lindsey Spiegelman (UCSD, T0987), and Marcus Hartmann (Max Planck Institute, T0999), and shipped to the crosslinking laboratories. Three of these targets were heteromeric complexes (those starting with 'H'), two homomultimers (T0981 and T0999), and the remaining threemonomers. Alexander Leitner's group generated crosslinking data sets for all eight targets, including three heterocomplexes (names of the released data-assisted targets start with the uppercase "X", and referred to as "BigX" group in the following), and Juri Rappsilber's group did so for four of the targets, including two complexes (targets start with the lowercase 'x', and referred to as "Smallx" group).
| Evaluation units (domains) As it is customary in CASP, prediction results were evaluated at different levels of protein structural organization, with emphasis on domain-based evaluation. Similarly to regular targets, crosslinking-assisted targets were split into evaluation units. 5 even different prediction sequences (subunits) were split into 19 distinct-sequence tertiary structure evaluation units (Table  1 ). Since models were built with the assistance of different crosslinking data sets separately (ie, "x" and "X" targets), these models were evaluated separately, which brought the total number of evaluation units to 27. The oligomeric targets were evaluated as whole complexes.
| MS data acquisition LC-MS/MS analysis was performed on a Thermo Easy nLC 1000 LC system coupled to a Thermo Orbitrap Elite mass spectrometer equipped with a nano-electrospray source. The instrument was operated in data-dependent acquisition mode (DDA). MS data were acquired in the Orbitrap at resolution 120 000, followed by fragmentation of the 10 highest intensity ions by CID, before mass analysis in the ion trap. The samples were analyzed in three technical replicates, where a single run included ions with a charge state ≥ +2, while the rest only included ions with a charge state ≥ +3.
| Data analysis Thermo raw files were converted into the mzMXL format using msconvert (ProteoWizard version 3. 0. 7494). MS/MS spectra were searched using xQuest 7 (version 2. 1. 4), against the target protein sequence(s) as provided and including contaminants identified from a search with Mascot (v. 2. 1. 5, MatrixScience) against the SwissProt database. xQuest search settings were as follows: Enzyme: trypsin, maximum number of missed cleavages: 2, MS mass tolerance: 5 ppm, MS/MS mass tolerance: 0. 2 Da for "common"-type fragment ions and 0. 3 Da for "xlink"-type fragment ions. All putative identifications were manually assessed.
| Data deposition in PRIDE All mass spectrometry data have been deposited in the PRIDE Archive 8 with the following data set identifiers and are accessible at https://www. ebi. ac. uk/pride/archive/projects/PXD######, the targets and corresponding web links are as follows: X0953: PXD010094; X0957: PXD010003; X0968: PXD010004; X0975: PXD010385; X0981: PXD010384; X0985: PXD010483; X0987: PXD010410; X0999: PXD010479. 0] [11]  Briefly, target proteins were crosslinked separately using sulfosuccinimidyl 4,4 0 -azipentanoate (sulfo-SDA) (Thermo Scientific Pierce, Rockford, IL) in a two-stage reaction (using eight different crosslinker-to-protein ratios: 0. 13:1, 0. 19:1, 0. 25:1, 0. 38:1, 0. 5:1, 0. 75:1, 1:1 and 1. 5:1 [w/w], a protein concentration of 0. 5 mg/mL and using 20 μg protein aliquots), with reaction of the NHS-ester firstly, subsequently followed by UV photoactivation at 365 nm, from a UVP CL-1000 UV Crosslinker (UVP Inc.
). Following crosslinking, reaction conditions were mixed and resulting crosslinked proteins separated by electrophoresis using NuPAGE 4-12% Bis-Tris gels, with MES SDS running buffer and staining using InstantBlue (Expedeon). Protein gel bands were digested using trypsin via standard protocols. 12 Resulting peptides were desalted using StageTips. MS data were acquired in the Orbitrap at resolution 120 000, using the top-speed data-dependent mode. Selected precursor ions were fragmented using higher energy collisional dissociation (HCD), using a normalized collision energy of 30%. Fragmentation spectra were then recorded in the Orbitrap at resolution 30 000, AGC target set to 5 x 10 4 and maximum injection time of 70 ms.
| Data processing Raw files were processed into mgf files using ProteoWizard msconvert (3. 0. 9576), with the inclusion of a MS2 peak filter for the 20 most intense peaks in a 100 m/z window. 15 The resulting peak lists were searched against FASTA sequence files using Xi  16  (https://github. com/Rappsilber-Laboratory/XiSearch) version 1. 6. 731, using the following settings: MS accuracy, 3 ppm; MS/MS accuracy, 15 ppm; missing mono-isotopic peaks, 2; enzyme, trypsin; maximum allowed missed cleavages, 4; crosslinker, SDA; fixed modifications, none; variable modifications, carbamidomethylation on cysteine, oxidation on methionine, SDA-loop (SDA crosslink within a peptide that is also crosslinked to a separate peptide, mass modification: 82. 041865). The linkage specificity for sulfo-SDA was assumed to be at lysine, serine, threonine, tyrosine, and protein N-termini at one end, with the other end having specificity for any amino acid residue.
A single crosslinking experiment with the best conditions was performed per target (for DSS and PDH in combination with DMTMM, respectively). Data analysis was performed using the in-house software xQuest,  7  and results were provided to the CASP participants with an expected error (false discovery) rate of <5%, although accurate FDR estimation is difficult if only very few crosslinks are identified. The final reports were published on the CASP website and listed the crosslinked residues along with the xQuest identification score (the higher, the better), so that participating groups could adjust their stringency thresholds, if desired. The main score of xQuest is a weighted composite score from several subscores that reflect the similarity of the experimentally observed and the predicted MS/MS spectrum (eg, cross-correlation of fragment ions, percentage of cumulative intensity that in the spectrum that is assigned to fragment ions), much like score of conventional proteomics search engines.
22  contrast, the other source for crosslinking experiments, the Rappsilber group (a. k. a. Smallx), used heterobifunctional, photoactivatable crosslinking chemistry, where the reaction occurs firstly on (predominantly) lysine residue side chains (but also the side chains of serine, tyrosine, and threonine), and following photoactivation, completes crosslinking by inserting non-specifically into vicinal bonds. This semispecificity has been shown to allow greater data density, which can be beneficial for protein structure prediction. 9 This approach provided the first experimental data in CASP history, in CASP11, in the form of high-density XL-MS (HD-XL-MS) data  4, 10, 23  and has been subsequently reused for targets in CASP12  11  and in the present study for CASP13.
When exploring the fraction of informative crosslinks, which were formed between residues 50 positions or more apart, we found that about 40-60% of all crosslinks satisfy this condition (Figure  3 ). If one combines these two requirements, it appears that about 23% and 27% (Smallx: 277/1184 and BigX: 73/272) of crosslinks fall into this combined category, respectively. However, from a practical point of view, the informativeness of crosslinks is known to all users, because the sequence separation is easy to check; therefore, a more practical measure is the fraction of valid and informative crosslinks over all of the known-to-be-informative ones, which results in 58% and 44% of crosslinks for the Smallx and BigX data sources, respectively.
| Assessing complexes There will be a separate article devoted to assessing the modeling of complexes with data assistance in this issue of the journal. We just briefly summarize here the narrow category of chemical-crosslinks-assisted complex modeling. In general, we followed the same evaluation as before for single chain targets, but in adjusting to the presence of multiple chains, we do not define informative crosslinks. Also, distances were measured directly in Euclidean space as opposed to considering the accessible protein surface as before. The number of targets was very limited, at 7. We compared the improvement to models in terms of LDDT measure  20  with and without crosslinking assistance within the subcategory of assisted modeling and also against the entire CASP general category (Figure  11 ).
Out of the seven targets, two had no valid crosslinks, that is, 0% on the figure, (interchain crosslinks were connecting distances longer than 30 Å), and one had no interchain crosslinks determined, that is, No, on the figure. Out of the remaining four targets, three improved upon adding crosslink information (Figure  11 ). F I G U R E 1 1 Accuracy of protein complex modeling with and without XL-MS data. Accuracy (LDDT) of best XL-MS-assisted model (vertical axis) vs the best TS model (without XL-MS information) from the corresponding assisted group (gray) or all structure modeling groups (blue). Gray data were selected from a subset of assisted modeling groups that submitted models both with and without crosslink assistance. Information about Xlinks are added to blue points: % valid or NO suitable crosslinks available F I G U R E 1 2 Accuracy of structure modeling with XL-MS data utilizing different sources.
ACKNOWLEDGMENTS We thank  Dr. I. Anishchanka  and  Dr. S. Ovchinnikov ,  University of Washington , for assistance in preparing contact-distance restraints for group 492. This work was supported by the following grants and agencies:  National Institutes of Health (NIH)  grant  GM118709 ,  GM100482 , and  AI141816 ; L' Agence Nationale de la Recherche  (grant number  ANR-15-CE11-0029-03 );  National Science Center of Poland (Narodowe Centrum Nauki ) (NCN), grants  UMO-2017/25/B/ST4/01026 ,  UMO-2017/26/M/ST4/00044 , and  UMO-2017/27/B/ST4/00926 ; and the  Benzon Foundation. Computational resources were provided by (a) the  Interdisciplinary Centre of Mathematical and Computer Modeling (ICM)  at the  Uniwersytet Warszawski (b) the Centre of Informatics-Tricity Academic Supercomputer & networK (CI TASK ) (c) the  Polish Grid Infrastructure  (PL-GRID), and (d) our Beowulf cluster at the  Faculty of Chemistry, University of Gda nsk.
Funding information Benzon Foundation; L' Agence Nationale de la Recherche , Grant/Award Number:  ANR-15-CE11-0029-03 ;  National Institute of Allergy and Infectious Diseases , Grant/Award Number:  AI141816 ;  National Institute of General Medical Sciences , Grant/Award Numbers:  GM100482 ,  GM118709 ;  National Science Center of Poland , Grant/Award Numbers:  UMO-2017/25/B/ST4/01026 ,  UMO-2017/26/M/ST4/00044 ,  UMO-2017/27/B/ST4/00926.
Introduction The Earth Observation Copernicus Sentinel program, funded by the European Union, Iceland, and Norway, has driven the development of new space-borne sensors, with new ground segments and data processing chains. Of particular interest to oceanographers is the acquisition of high-quality sea surface Published by Copernicus Publications on behalf of the European Geosciences Union. temperature (SST) data. Over short timescales, this essential ocean state variable provides important information on the spatial distribution and intensity of dynamic structures, such as eddies, coastal currents and upwelling regions, in near-real time (within a few hours after acquisition). Over the long term (multi-decade), it describes the distribution of heat within the Earth system. Long time series of SST datasets (e.
As a result, concomitantly to the SLSTR development, the Copernicus program aims to develop fiducial reference measurement (FRM) initiatives. Among them is the deployment of an array of temperature-measuring surface drifters, covering several SST regimes. The operational nature and climate quality of Sentinel-3 datasets are expected to deliver long-term data records  (Donlon, 2011). For consistency, this implies that the surface references used for calibration and validation must also be homogeneous over time. This FRM initiative complements others started lately, such as under the European Space Agency (ESA) project Fiducial Reference Measurements for validation of Surface Temperature from Satellites (FRM4STS), which has conducted in particular a comparison of infrared radiometers with radiation thermometers in a laboratory setting  (Theocharous et al.
no. 2 and -0. 005 K year -1 for lab. no. 3. Note that it cannot be ruled out that the probes, once removed from the buoys, did respond differently than during the initial calibration setup. Indeed, the temperature variations being looked at are very small, and any influence of the acquisition electronics may affect the results. The exact environment used for housing the electronics during calibration of the initial probes, as well as during the verifications, even if specified in the initial calibration sheets, cannot be replicated with certainty. Consequently, these results are to be taken with caution, and the importance of the calibration apparatus stands out as being an important part of the traceability. However, should the negative trend (cooling) be confirmed, it would have an impact on the exploitation of the SST drifter data for satel-lite cal/val, as well as corrections that are made to global datasets.
To investigate the influence of the drogue, the SVP-BS data record is revisited. These buoys used submergence sensors, whereas drifters nowadays use strain gauges, e. g. , as indicated by  Rio (2012) , who developed a advanced method to identify drogue loss using drifter currents, satellite altimetry, and wind reanalysis data. The submergence (or tether strain gauge) readings are neither straightforward to interpret nor fully reliable on their own  (Rio, 2012). However, the SVP-BS drifter data considered here (available from the Coriolis In Situ Thematic Assembly Center) are not found in the drifter dataset of  Rio and Etienne (2018) , which includes drogue presence flags. Consequently, for this analysis, we use the submergence and GPS TTFF data. A visual inspection indicates that 10 of the 20 buoys in Table  2  have lost their drogues during their mission.
Consequently, they may both be worth considering in the process of satellite SST cal/val. In addition, a fairly standard analysis, where ocean dynamics behavior can be inferred from the buoy data, suggests that the high-resolution SST data hold a wealth of information. Properly analyzed and interpreted, these data can provide a useful insight of the dynamics of the sampled area, especially when the Supplement is brought into the picture to consider sea state and ocean surface circulation. Even more interesting may be to collect full samples of 1 Hz data, when possible, in addition to the summaries of the distribution with five percentiles. Such a high-frequency HRSST dataset (HFHRSST) may serve other applications beyond satellite SST ca/val, such as fine-scale model developments and enhanced understanding of SST variability.
Data availability. The HRSST-2 SVP-B and SVP-BS data are available from the Copernicus In Situ Thematic Assembly Center (http://marine. copernicus. eu/situ-thematic-centre-ins-tac/, Copernicus Marine Environment Monitoring Service, 2019). The SVP-BRST prototype drifter data used in this publication are available in open access: https://doi. org/10. 5281/zenodo. 1410401 (Poli et al. , 2018). Reanalysis ERA5 data are available from the Copernicus Climate Change Service. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-15-199-2019-supplement. Author contributions. PP drafted the main text of this paper, prepared several of the figures and corresponding scientific analysis, and prepared the manuscript for submission. ML drafted the introduction and, along with GC, contributed results and scientific analysis.
METHOD DETAILS Protein homology searches Rv1989c-Rv1990c-like TA systems were identified in bacterial genomes using NCBI's standard protein BlastP searches against the non-redundant protein sequence (nr) database. Hits from M. tuberculosis genomes were excluded. From the resulting top 100 hits, only those homologs were withheld for which the hypothetical toxin and antitoxin were encoded by adjacent genes. Selected protein sequences were retrieved from the UniProt database and re-aligned using MAFFT  (Katoh and Standley, 2013)  within the Jalview software package  (Waterhouse et al. , 2009). Pairwise protein sequence identities were calculated using the Pairwise Alignment tool in Jalview. Conserved protein were identified with InterPro  (Finn et al. , 2017). The N-terminal HTH domain of the antitoxin, which is not detected by InterPro, was identified using the HTH motif prediction program available from the NPS@ web server  (Combet et al.
, 2000). E. coli viability assays E. coli strain W3110 containing p29SEN or p29SEN-Rv1990c was co-transformed with empty vector (pMPMK6) or pMPMK6-Rv1989c (Table  S2 ), grown to mid-log phase, serially diluted and spotted on agar plates supplemented with appropriate antibiotics. 1% arabinose or 5 mM IPTG or were used to induce expression of Rv1989c and Rv1990c, respectively. Images were taken after overnight incubation at 37 C. Raw images are available on Mendeley Data (https://doi. org/10. 17632/y6ynjm5sf3. 1).
Labeled cells were either observed by confocal microscopy using an Andor/Olympus spinning disk microscope with an Olympus 100x oil immersion objective or by fluorescence-activated cell sorting using a BD FACS Aria Fusion flow cytometer. Image analysis was performed using ImageJ software and flow cytometry data analysis using FlowJo software. Raw images are available on Mendeley Data (https://doi. org/10. 17632/ y6ynjm5sf3. 1).
Double-stranded cDNA was reverse-transcribed using the superscript III Reverse Transcriptase kit (Invitrogen), according to the manufacturer's protocol. For real-time qPCR, specific primers were designed and PCR reactions were performed using SYBR Green Premix Ex Taq (Ozyme), according to the manufacturer's protocol. All real-time qPCR reactions were carried out using a 7500 Real-Time PCR System and data were analyzed using the 7500 Software version 2. 3 (Applied Biosystems). PCR array data were calculated by the comparative cycle threshold method, normalized with the rpoB housekeeping gene, and expressed as mean fold change in experimental samples relative to levels in Mtb WT grown in 7H9 ADC tween medium.
Data for Sulfur Single-wavelength Anomalous Dispersion (S-SAD) phasing were collected with an X-ray beam of 70 mm in diameter at an energy of 5. 0 keV (l = 2. 48 A ˚) on three different positions of a rod-shaped crystal with approximate dimensions of 300 3 70 3 70 mm 3. At each position, 3600 frames of 0. 1 per 40 ms exposure time were collected. High-resolution native data were collected at an energy of 12. 7 keV (l = 0. 976 A ˚) on a crystal with approximate dimensions of 500 3 100 3 100 mm 3 with a beam of 100 mm in diameter employing a helical scan between two centring points ca. 400 mm apart. 1800 frames of 0. 1 per 40 ms were recorded. Data were integrated with XDS and further processed with XSCALE  (Kabsch, 2010)  and POINTLESS and AIMLESS from the CCP4 suite of programs  (Evans, 2011; Winn et al.
, 2011). The crystal structure was solved using the SHELX suite  (Sheldrick, 2008)  of programs via the HKL2MAP user interface  (Pape and Schneider, 2004). Unmerged data collected at low and high energy were supplied to SHELXC as SAD and NATIVE datasets respectively. For substructure solution, the anomalous differences determined by SHELXC were truncated at 3. 0 A ˚. In 100 trials, 16 anomalous sites with occupancies higher than 0. 6 were identified by SHELXD with a CFOM of 58. 8. Phases calculated based on the substructure, after ten alternating cycles of density modification (assuming a solvent content of 44%) and main-chain auto-building as implemented in SHELXE resulted in 550 residues being placed into the experimentally phased electron density with correlation coefficient between the structure factors calculated for the partial structure and the experimental data of 46.