dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensitivity analysis We use a Bayesian belief network (BBN; e. g. ,  Hincks et al. , 2014)  related to each model to characterize uncertainties on the MinMass and MinVol estimates (Figs. 6 and S12), including the existing correlation coefficients (marked with linking arc arrows) between the input variables and the calculated MinMass and MinVol. This analysis was performed with the UNINET software  (Ababei, 2016). The histograms in each BBN also illustrate the shape of the uncertainty distributions for the intermediate variables (g c , g p and φ cr ) and for the minimum mass (MinMass) and volume (MinVol) needed to reach the first marker site, based on the elicited distributions of the relevant input variables. Larger correlation values highlight variables to which our probability calculations are most sensitive.
, 2010; Brown et al. , 2014)  suggest a minimum global average recurrence rate of ∼ 1-2 times per 1 kyr  (Newhall et al. , 2018). Once record incompleteness is corrected for  (Kiyosugi et al. , 2015; Rougier et al. , 2016) , the value approaches 2 events per 1 kyr. At individual volcanoes, > M 7 recurrence rates range from 1 per 10 kyr to 1 per 100 kyr  (Newhall et al. , 2018). For eruptions > M 8, Rougier et al. (  2018 ) estimated a return period of 17 ka (95 % confidence interval 5. 5-48 ka) from analysis of the LaMEVE database  (Crosweller et al. , 2012). More recently,  Aspinall et al. (2021) , implementing a comprehensive stochastic uncertainty analysis, estimated the probability in the next 100 years of an eruption from the Aso volcano on the same scale as Aso-4. They set up a Bayesian belief network informed by multiple strands of evidence from volcanology, petrology, geochemistry and geophysics, together with reviews of published data, models, and inputs from expert elicitation.
Acknowledgements. We thank  Charles Connor  for contributing as one of experts in the elicitation sessions together with the authors  Andrea Bevilacqua ,  Antonio Costa ,  Augusto Neri ,  Brittain Hill  and  Stephen Sparks. We are very grateful to  Shinji Takarada  for the scientific discussions that stimulated this research initially and for the useful comments that he and an anonymous reviewer provided during the review process. A preliminary version of this study was presented in Aspinall et al. (2019). Financial support. Alvaro Aravena  was financed by the  French government (IDEX-ISITE  initiative  16-IDEX-0001 ,  CAP 20-25 ). This work was supported in part by  Aspinall & Associates. Review statement. This paper was edited by  Amy Donovan  and reviewed by  Shinji Takarada  and one anonymous referee.
Data acquisition and improved processing Before the interferograms are recorded (either with or without the cell in the path), the pre-gain and gain settings of both detectors are checked. The manufacturer's data acquisition software OPUS is used to perform the measurements and to process the DC-coupled interferograms. Ten doublesided full-resolution scans recorded with 10 kHz scan speed are co-added into one averaged interferogram, and 30 to 40 averaged interferograms of this kind are recorded in total to achieve a spectral signal-to-noise ratio in the range of 2000 to 3000; see Sect. 6. 3. As the DC level of the EM27/SUN is slightly variable as a function of optical path difference, a DC correction is applied (because the solar observations also undergo a DC correction). The resulting spectra are normalized to about unity in the spectral range required for the ILS analysis and are stored with a zero-filling factor of 8 to support the visual inspection of the spectral fit quality.
Required auxiliary data In order to correctly derive the H 2 O column and ILS parameters, pressure and temperature need to be measured, both inside the instrument and outside in the laboratory. The temperature inside the spectrometer is recorded using the sensor built into the spectrometer by the manufacturer. The tempera-  ture of the laboratory air is recorded using digital thermometers offering 0. 8 • C accuracy (Lutron MHB-382SD data loggers or thermo hygrometer barometers of type PCE-THB 40 were used). While in the previous data analysis the pressure readings of the aforementioned portable sensors were used, we migrated for the analysis of new measurements as well as for the re-analysis of previous measurements to the pressure record from the nearby meteorological tall tower. This tower is operated by the Institute for Meteorology and Climate Research -Department Troposphere (IMK-TRO), see  Kohler et al.
Empirical update of H 2 O spectroscopic data For the previous ILS analysis, the H 2 O line list provided by HITRAN version 2008 (including the corrections introduced in 2009) with some minor empirical adjustments was used. The work presented here uses a considerably revised line list. The HITRAN 2016 H 2 O list served as a starting point for fitting empirical H 2 O line parameters in the two relevant spectral regions using a pair of high-resolution open-path spectra recorded with the Bruker IFS125HR spectrometer of the TCCON station Karlsruhe. The air conditioning system of the laboratory container housing the spectrometer was used to adjust the air temperature to 15 and 30 • C, respectively. We assume that this span largely covers the conditions of laboratory ILS measurements. The pair of spectra were then used for a multi-spectrum fit of empirical H 2 O line parameters using the LINEFIT software with a wrapper for adjusting the line parameters.
If we assume that the TCCON spectrometer used to calibrate the empirical C 2 H 2 cell parameters can be regarded as an ideal reference (see Sect. 3. 2), this finding suggests that the OP MEA results indeed suffer from a systematic low bias of about 0. 015 (1. 5 %) and that the ILS performance of the EM27/SUN is on average closer to the nominal expectation than indicated by the OP measurements (see Fig. 16 ). This adjustment will be included in a future version of the PROFFAST software used by COCCON for the analysis of atmospheric spectra. The current version of the code uses the MEA values resulting from the OP measurements, so the currently incorporated values of the air-mass-independent and air-mass-dependent calibrations are partly mitigating the bias in MEA. Even though the cell method does not provide a significant improvement in the determination of MEA and PE values, we plan to maintain the cell measurements in the calibration procedure.
Long-term stability of reference unit In this section the historic time series of the COCCON reference instrument SN37 is assessed by comparing the main target gases: XCO 2 , XCO, XCH 4 and XH 2 O with the results obtained from the high-resolution spectrometer IFS125HR located at KIT Campus North (49 • 06 00. 8 N, 8 • 26 18. 6 E, 112 m a. s. l. ). This spectrometer contributes to the TCCON network. Two different kinds of measurements were collected with the IFS125HR spectrometer: standard TCCON measurements using a spectral resolution equivalent to max. OPD of 45 cm and double-sided low-resolution spectra for mimicking the EM27/SUN observations (maximum OPD 1. 8 cm). The COCCON and the low-resolution data recorded with the IFS125HR were analysed using PROFFAST, while the high-resolution spectra were used for generating the official TCCON product using the GGG software suite version 2014  (Wunch et al.
, 2015). Because it provides a very sensitive indication for instrumental drifts and operation problems, we also investigate results for X AIR here. This quantity compares the spectroscopically determined dry-air column as extrapolated from the observed vertical column of O 2 VC O 2 with the dry-air column calculated from ground pressure and spectroscopically observed water vapour col- umn VC H 2 O , as given in Eq. (  1 ). X AIR is not generated as a calibrated quantity by GGG2014, while PROFFAST attempts a normalization to unity. However, there is a change of X AIR apparent in the COCCON reference data during the first 4 years, which we investigate further in the next section. We will show that these changes are small enough not to detectably affect the results of the target gases apart from XCH 4.
The instrument-specific XCH 4 calibration factors provided in Sect. 6. 2 and in Table  S2  in the Supplement take these corrections of the reference unit into account. The variable bias of the reference unit's XCH 4 despite the fact that a time-dependent ILS is used in the data analysis might indicate that the ILS model currently used by PROFFAST is too simple or that the assumptions made on the wavenumber dependence of the ILS parameters are incorrect (the current version of PROFFAST assumes a linear wavenumber dependence for MEA and PE while the openpath analysis suggests a quadratic dependence for MEA; see  Sect. 4. 3. 1) or that additional influencing factors are affecting the trace gas results. ). After the measurements are taken, the data are processed using the PROF-FAST software. In this processing, the ILS parameters derived previously from OP measurements are included for both spectrometers, the spectrometer under test and the reference unit.
Spectral signal-to-noise ratio of the EM27/SUN spectrometers In order to assess the distribution of the spectral signal-tonoise ratio (SNR) of different instruments, these values were calculated for both solar and laboratory spectra. For both cases the SNR is calculated for several spectral windows covering both detector channels. The procedure applied is based on the formula described in the Bruker OPUS © software manual (Bruker, 2017); the SNR is calculated from the ratio of two consecutively measured spectra. A wavenumber region largely free of absorption gases lines is selected. A parabola is fitted to the ratio spectrum in the investigated spectral window and serves as a nominal signal. The rms of the fit residuals is calculated. This rms value is divided by √ 2 to deliver the SNR of a single spectrum (because a pair of spectra is used in the procedure).
Acknowledgement. We thank  Martin Kohler  for providing the meteorological data sets from IMK-TRO's tower. Dragos Ene wants to acknowledge the  Romanian National Core Program  (contract no. 18N/2019 ) for funding his activities. Diogo J. Medeiros acknowledges the  São Paulo Research Foundation (FAPESP)  for financial support (grant  2019/27079-2 ). The Wollongong team want to acknowledge the  Australian Research Council (ARC)  for supporting their activities under the grants  DP160101598  and  LE0668470.
Code availability. Linefit v14. 8 used for the ILS characterization can be obtained by contacting Frank Hase (frank. hase@kit. edu). The PROFFAST software is freely available using the following link: https://www. imk-asf. kit. edu/english/3225. php (last access: 14 April 2022; KIT-IMK-ASF, 2022). Data availability. All the data used for this study can be directly requested from the author: Carlos Alberti (carlos. alberti@kit. edu). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-15-2433-2022-supplement.
Material and Methods Phylogenetic Analyses. We assembled a database of 51 proteomes representing all Corynebacteriales diversity present at the GenBank database (45) as of August 2021. We selected five species per genus-if available-and we chose preferably the most complete assemblies (for a list of taxa see SI Appendix, Table  S1 ). We used HMM profile searches to identify all members of the NlpC/P60 superfamily in the database. First, we used the HMMER package (v3. 3. 2) (46) tool jackhmmer to look for homologs of Mtb RipA in all the proteomes using the GenBank sequence BAB98931. 1 as query. The hits were aligned with mafft (v7. 475) (47) using default parameters. The alignments were manually curated, removing sequences that did not contain the NlpC/P60 domain, and trimmed, keeping only the NlpC/P60 domain.
The hits obtained by jackhmmer might not include sequences that are very divergent from the single sequence query. For this reason, the trimmed alignment was used to create an HMM profile using the HMMER package (v3. 3. 2) tool hmmbuild. This specific and curated HMM profile for the NlpC/P60 domain was used for a second and final round of searches against the proteomes using the HMMER tool hmmsearch. The new hits were aligned with linsi, the accurate option of mafft (v7. 475), and trimmed using trimAl (v1. 2) (48) to keep only the positions with less than 50% of gaps (-gt 0. 5). NCBI GenBank accession numbers of the sequences used in the phylogenetic analysis are provided in SI Appendix, Table  S1 , and the alignment and phylogeny file are provided in SI Appendix. The trimmed alignment was used to reconstruct the phylogeny of the NlpC/P60 superfamily.
Data Collection, Structure Determination, and Refinement. X-ray diffraction data were collected at 100 K at the Synchrotron Soleil (Saclay, France). All datasets were processed using XDS  (54)  and AIMLESS from the CCP4 suite (55) (SI Appendix, Table  S2 ). Merged data from Cg1735 crystals were further subjected to anisotropy correction with STARANISO (56), the elliptical diffraction limits were d h00 = d 0k0 = 4. 8 Å, d 00l = 2. 9 Å for the trigonal dataset and d h00 = 4. 5 Å, d 0k0 = 6. 0 Å, d 00l = 4. 0 Å for the orthorhombic dataset. The structure of trigonal Cg1735 soaked in Cl 4 K 2 Pt was determined by SAD phasing: two major and several minor Pt sites in the asymmetric unit were found with SHELXD (57), and the experimental phases obtained with PHASER (58) were significantly improved by density modification with PARROT from the CCP4 suite, as the crystal has a high (85%) solvent content.
Coiled-coils were clearly visible in the resulting electron density map (SI Appendix, Fig. S6 ), and the CD was positioned by real-space molecular replacement using Mtb RipB (PDB code 3pbi) as search model. The structure of the orthorhombic form of Cg1735 was solved by molecular replacement methods, and that of SeMet-labeled Cg1604 by SAD phasing using SHELXD and automatic model building with BUCCANEER from the CCP4 suite. All structures were refined through iterative cycles of manual model building with COOT (59) and reciprocal space refinement with PHENIX (60) or BUSTER  (61). Non-crystallographic symmetry and secondary structure restraints were applied to both Cg1735 structures and, for the lower resolution orthorhombic structure, additional homology model (Mtb RipB) torsion restraints were applied to the CD.
Data are presented as delta epsilon (Δε) per residue (L. mol-1. cm-1. residue-1) calculated using the molar concentration of protein and number of residues. Analytical Ultracentrifugation. Cg1735 was diluted to 5 mg/mL in 25 mM HEPES, pH 7. 5, 150 mM NaCl, 5% glycerol, and centrifuged at 42,000 rpm in an Optima analytical ultracentrifuge (Beckman Coulter) at 20 °C in an eight-hole AN 50-Ti rotor equipped with 3-mm and 12-mm double-sector aluminum epoxy centerpieces. Detection of the biomolecule concentration as a function of radial position and time was performed by absorbance measurements at 280 nm and by interference detection. Sedimentation velocity data analysis was performed by continuous size distribution analysis c(s) using Sedfit software. All the c(s) distributions were calculated with a fitted fractional ratio f/f0 and a maximum entropy regularization procedure with a confidence level of 0.
95. Partial specific volume, buffer viscosity, and density were calculated using the Sednterp software. Analytical SEC. To obtain the chromatograms shown in Fig. 4 , protein samples were diluted in SEC buffer (25 mM Hepes, pH 7. 4, 150 mM NaCl, 5% glycerol; or 25 mM Succinate, pH 5. 0, 150 mM NaCl, 5% glycerol). Protein mixtures were incubated in an equimolar ratio (either 50 µM or 80 µM) on ice for 1 h, centrifuged, and eluted on a Superdex 200 Increase 5/150 GL (Cytivia) pre-equilibrated with SEC buffer at 4 °C. Fractions of 100 µL were collected for analysis by SDS-PAGE. BLI Assays. The affinity of Cg1735 CD for His_SUMO_Cg1735 CC1 , and that of Cg1735 CD and Cg1604 for biotinylated Cg1735 ΔCD were measured in real time using a BLI Octet-Red384 device (Pall ForteBio) at 25 °C. His_SUMO_Cg1735 CC1 was diluted at 3 μg/mL in buffer A (HEPES 25 mM pH 7.
pnas. org Specific signals were obtained by subtracting both non-specific signals measured on empty sensors and buffer signals on specific His-SUMO-Cg1735 CC1 or biotinylated Cg1735 ΔCD -loaded sensors. Assays were performed at least twice, and Kd values were obtained from steady-state signal versus concentration curves fitted with GraphPad Prism 9 assuming a one-site binding model. Data, Materials, and Software Availability. Atomic coordinates and structure factors have been deposited in the Protein Data Bank with accession codes 8AUC, 8AUD, and 8AU6 (SI Appendix, Table  S2 ). All data used to produce the phylogenetic analysis is provided as Supporting Data in the Mendeley Data repository (https://data. mendeley. com/datasets/2543ntd7pt/draft?a=6249d3ed-c5c2-404c-8049-c041ec488152). ACKNOWLEDGMENTS.
This work was funded by the Agence Nationale de la Recherche (ANR) through the projects PhoCellDiv (ANR-18-CE11-0017) granted to P. M. A. and MecaDiv (ANR-21-CE11-0003) granted to A. M. W. , and by institutional grants from the Institut Pasteur, the CNRS, and Université Paris Cité. Molecular graphics were done with ChimeraX, developed at UCSF with support from NIH (R01-GM129325) and NIAID. We gratefully acknowledge the core facilities at the Institut Pasteur C2RT (Centre for Technological Resources and Research), in particular B. Raynal, P. England (PFBMI), and P. Weber, C. Pissis, A. Mechaly (PFC). We also acknowledge the synchrotron source Soleil (Saint-Aubin, France) for granting access to the facility and the staff of Proxima 1 and Proxima 2A beamlines for helpful assistance during X-ray data collection.
Acknowledgements We thank the  Petzoldlab  and especially  Lorenzo Baronti  for inspiring discussions and the  Martin Hällberg group  for the generous gift of the inorganic phosphatase. We are indebted to the protein science facility (PSF) at the  Karolinska Institute  for reliable production of the T7 RNA polymerase. JS acknowledges funding through a  Marie Sklodowska-Curie IF  ( EU H2020 ,  MSCA-IF  Project No. 747446 ). KP acknowledges funding from the  Swedish Research Council  (grant number  2014-04303 , and  NT-2018-15 ), the  Swedish Foundation for Strategic Research  (project number  ICA14-0023  and  FFL15-0178 ),  Harald och Greta Jeansson Stiftelse  ( JS20140009 ),  Eva och Oscar Ahréns Stiftelse, Åke Wiberg Stiftelse  ( 467080968  and  M14-0109 ),  Cancerfonden  ( CAN 2015/388 ,  CAN 2018/715 ), the  Karolinska Institute  ( KID 2016-00062 ), the  Ragnar Söderberg Stiftelse  ( M91/14 ) and the  Knut och Alice Wallenberg foundation  [project grant  KAW 2016.
Statistics The SPSS software (20. 0; IBM, Champaign, IL, USA) was used for statistical analysis. All data are reported as mean ± SD, and 95% confidence interval. Normality distribution was confirmed using the Shapiro-Wilk test. A paired Student's t-test was used to compare data derived from the metabolic unit during the CPET protocols between upright vs supine cycling bouts. Bland-Altman plots were generated to evaluate the agreement between the V ̇O2 demands at GET, RCP, V ̇O2 peak and V ̇O2 peak verification bout during upright vs supine cycling exercise  (Bland, 2000). The level of statistical significance was accepted at p < 0. 05.
RESULTS Nineteen moderately-trained males (a self-reported average of 11 h of moderate-vigorous exercise per week) who had normal mean resting values for heart rate, arterial blood pressures and oxygen saturation completed all study procedures. The oxygen uptake kinetics data during moderate intensity step-transitions in both upright and supine position are given in Table  1. Briefly, no differences were observed between the positions for either the amplitude of V ̇O2 (p = 0. 114, 95% CI [-0. 01 to 0. 08]), time delay (p = 0. 054, 95% CI [-0. 05 to 6. 21]) or MRT (p = 0. 364, 95% CI [-9. 91 to 3. 80]). However, in the supine position baseline oxygen uptake was higher (p = 0. 001, 95% CI [-0. 24 to -0. 12]), and the phase II-time constant (V ̇O2p ) was 7 s slower, compared to the upright position (p = 0.
Study Population This retrospective observational study included patients treated at the Henri Becquerel Cancer Centre, Rouen, France, between April 2007 and December 2019. The study has been approved by the institutional review board. Patients were informed about the use of anonymized data for research and their right to oppose this use. The inclusion criteria were patients with (i) DTC confirmed by our pathologist; (ii) presence of neck recurrence or distant metastasis; (iii) presence of at least one radio-iodine refractory site; (iv) availability of an 18 FDG-PET/CT performed in the year of diagnosis of RAI-R DTC; and (v) absence of systemic treatment, but radioactive iodine, or local treatment before the reference 18 FDG-PET/CT. Data obtained from all patients included the following information: sex, age at disease onset, pathology, margin resection status, primary disease stage (8th edition of the AJCC/TNM staging system), cumulative RAI dose, site of metastasis, history of previous treatments, Tyrosine Kinase Inhibitor (TKI) treatment initiated during the 12 months after diagnosis of RAI-R, delay to death and cause of death.
PET/CT Acquisition and Reconstruction All patients underwent 18 FDG PET/CT scans, which were performed after a 6-h fasting period and when blood glucose level was less than 1. 7 g/L before the injection of the radiotracer. FDG PET/CT scans were performed under thyroxine treatment in all cases. Data from PET were acquired from the mid-thigh toward the base of the skull approximately 60 min after injection of a weight-adjusted dose of 3. 0-4. 5 MBq/kg. The PET system was normalized daily, and the calibration coefficient validated if the day-to-day variation remained below 0. 3%. The global quantification, from the dose calibrator to the imaging system, was measured internally on a quarterly basis and double-checked by the EARLs quality assurance program.
PET/CT Metrics The manual segmentation of lesions was performed using semi-automatic software (PET VCAR, General Electrics ® ). A volume of interest was set around each lesion on the PET images. Each FDG-avid lesion was subjected to segmentation, which allowed calculation of the SUVmax (maximum standardized uptake value) and the MTV (metabolic tumor volume). The SUVmax was measured by using a volume of interest with Standardized Uptake Value (SUV) being expressed using the following definition of SUV (g/mL) = (Tissue activity (Bq/mL)/[(injected activity (Bq)/body weight (g)]). The SUVmax represents the voxel with the highest intensity of uptake in each lesion and reflects the glucose avidity of the lesion. The MTV of each lesion, representing the volume measured in the volume of interest, was determined using margin thresholds set at 42% of the SUVmax, as recommended by the European Association of Nuclear Medicine  [33].
Influenza A subtype or B lineage classification was documented when available. When possible, next-generation sequencing (NGS) was then performed using local standardised protocols. The coordinating sites without NGS capacity sent RNA extracts on dry ice to the Centre national de référence des virus des infections respiratoires (dont la grippe) in France (Lyon) for genome sequencing to be performed with Illumina reagents, as previously described  [11]. After sequencing, nucleic acid alignment of the hemagglutinin (HA) segment was performed with the muscle alignment program and UPGMA cluster method on MEGAX software (version 10. 1. 8) [12]. Phylogenetic tree construction was performed using optimized substitution models according to Bayesian Information Criterion (K3Pu+F+G4 for A(H1N1)pdm09, TVM+F+I for A(H3N2), and HKY+F+G4 for B/Victoria) followed by ultrafast bootstrap analysis (10, 000 replicates) on the IQ-TREE web server  [13].