dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of rate constants Figure  3a  shows the computed rate constant for the H-atom abstraction reaction by H from H2S in comparison with the available literature data  [30, [77] [78] [79] [80] [81] [82] [83]. The present calculations generally agree with experimental estimates, with the exception of the dataset of Roth et al  [83]. Previous theoretical estimates obtained with different methodologies also support the theoretical accuracy of our result. In particular, we found less than 50% discrepancy with the recently calculated rate of Qi et al  [79] , who performed quantum dynamical calculations on the FC-UCCSD(T)-F12a/aug-cc-pVTZ PES derived by Lu et al  [84]. The less recent CTST results of Peng et al. [30]  are larger than the present calculated rate by a maximum factor of 2 at 300 K.
Such differences are reasonably within the uncertainties of the theoretical methodologies used. The comparison between the calculated H-atom abstraction rate constant by OH from H2S is found in Figure  3b. With maximum differences of a factor of 2. 3 with respect to the experimental estimates of Westenber et al  [85] , our calculated rate generally agrees with the experimental data within a factor well below 1. 8. The slight underestimation of most datasets at lower temperatures might be easily explained by assigning an uncertainty of 0. 5 kcal/mol to the energy barrier of this reaction, which we computed as 0. 7 kcal/mol at CCSD(T)/CBS level. A closer agreement at lower temperatures was in fact obtained by  Ellingson and Truhlar [86] , who calculated a M06-2X/MG3S barrier of -0. 24 kcal/mol.
Because our CASPT2(20e,14o)/aug-cc-pVTZ barrier was calculated relatively to SH + HO2 and then rescaled with respect to the CCSD(T)/CBS reaction energy (see Section 2. 2), the large difference with Wang calculations might be related to the energy of the reactants, which they used as reference. Therefore, we also calculated the ΔER at MR level, however obtaining 43. 96 kcal/mol. The experimental ΔER of 41. 32 kcal/mol estimated from NIST database (https://cccbdb. nist. gov/) supports the values obtained in this work. As expected, the large variations of the literature values in the ΔETS directly affect the corresponding rate constants (Figure  4b ). Nevertheless, they are still insufficient to fully account for the discrepancies found. For instance, Starik et al. [99]  refined the energy barrier of Montoya et al.
Experimental results and kinetic modeling The combustion behavior of H2S was experimentally investigated by following a hierarchical methodology. As shown in Table  1 , in all of the three reactors, pyrolysis experiments were first carried out, and the availability of different experimental configurations allowed to explore the widest possible temperature (T) range, and accordingly residence times (τ). Afterwards, oxidation campaigns were performed with the same approach: in this case, in addition to variable T and τ, the effect of the progressive oxygen addition and H2S amount was also analyzed. All data sets are provided under the form of spreadsheets in Supplementary Material. The use of the kinetic model allowed to interpret the experimental data, and to explain H2S conversion throughout the full range of operating conditions.
Pyrolysis After coating the three reactors, pyrolysis experiments were performed in each of them, by using 500 ppm H2S in a helium atmosphere. Results are shown in Figure  6  for both H2S and H2 mole fractions. For Figure  6a , equilibrium data are also added, and point out that the system is kinetically governed, far from equilibrium conditions. In the JSR, no fuel consumption can be observed before ∼1000 K, and the final conversion at the maximum allowed temperature (1200 K, due to the mechanical resistance of the quartz material) is slightly higher than 10%. On the other hand, complete conversion can be observed at high temperatures in FR2 and FR1, where a 50% conversion is reached for T = 1300 K and T = 1450 K, respectively (mostly due to the different residence times). The kinetic model is able to reasonably predict the fuel consumption, in terms of both reactivity onset and conversion rate with temperature.
Alumina was the only material considered in this case, and only coated data are shown here. Results show a similar qualitative behavior for the two datasets, with the only macroscopic difference being the onset temperature: under the leanest conditions, the mixture starts reacting at a temperature 100 K lower. In this case, the experimental trends are also reproduced fairly well by the kinetic model, with a slight underprediction of the onset temperature (50 K). In order to understand the differences in the controlling reaction steps, with respect to what is observed at lower temperatures, in perfectly stirred conditions, reaction flux analysis was performed for a sample condition (Φ = 0. 1, τ = 0. 25 s for FR1). This was coupled to the sensitivity analysis to the H2S mass fraction, carried out for the 3 datasets obtained for FR2 and FR1.
Of the 39 patients, 10 (25. 6%) presented a positive RT-qPCR result. The median faecal viral load was 3. 7 log 10 copies per mg (IQR 4. 5-2. 7), as estimated by RT-qPCR performed in parallel with standards. A viral load of 1. 7 log 10 copies per mg (50 copies mg À1 ) was arbitrarily chosen as the cut off value to group samples into high SARS-CoV-2 RNA   Peptides were identified by searching tandem mass spectra in a two-step cascaded search, with the second search performed against a sample dedicated database. This strategy allowed 31 AE 6% of spectra to be assigned per sample. A total of 88 135 proteins and 60 179 protein groups were listed in the dataset. Overall, a higher number of proteins was identified in samples with lower levels of SARS-CoV-2 RNA or negative compared to positive samples with a viral load >50 copies mg À1 of faecal material (Supplementary Fig.
The flowchart of the study is presented in Fig. 1A. Among the 41 stools received during the indicated period, two were excluded from the analysis due to the low quantity. For each patient, a NP swab was collected and sent to the Department of Microbiology which is accredited to perform RT-PCR assay for SARS-CoV-2. RNA was extracted from clinical samples using the chemagic viral DNA/RNA kit special 96 on the chemagic platform (PerkinElmer, Waltham, MA, USA). The RT-PCR was performed using the kit SARS-CoV-2 R-Gene ® (bioMérieux, Marcy-l' Etoile, France) which targets three genes of the virus [the N gene of the nucleocapsid, the RNA-dependent polymerase gene (RDRP) and the E gene which is common to all the SARS viruses], following the manufacturer's recommendations. Data including demographics, laboratory results and imaging results were extracted from the electronic medical records of the University Hospital management system (Clinicom, Intersystems SAS, France).
Primers targeting E gene were: E_Sarbeco_Fw (ACAGGTACGTTAATAGTTAATAGCGT), E_Sarbeco_Rv (ATATTGCAGCAGTACGCACACA), and E_Sarbeco_ Probe(+) (ACACTAGCCATCCTTACTGCGCTTCG[5 0 ]Fam [3 0 ]BHQ-1). Standard curves were created using in vitrotranscribed RNA derived from the BetaCoV_Wuhan_ WIV04_2019 strain (EPI_ISL_402124), which contains the amplification regions of the RdRp and E gene as positive strand. Standard samples were prepared with 1011 copies of target sequences diluted in yeast tRNAs to facilitate recovery, and lyophilised.
Assigning peptides and analysing metaproteomics data The Mascot Daemon 2. 6. 1 search engine (Matrix Science) was used to match MS/MS spectra to peptides in a multiround search process. The search parameters were as follows: full trypsin specificity, maximum of two missed cleavages, mass tolerances of 5 ppm on the parent ion and 0. 02 Da on the MS/MS, carbamidomethylated cysteine (+57. 0215) as fixed modification, and oxidized methionine (+15. 9949) and deamidation of asparagine and glutamine (+0. 9848) as variable modifications. The NCBInr database (National Center for Biotechnology Information, NIH, Bethesda) was downloaded as ftp://ftp. ncbi. nlm. nih. gov/ blast/db/FASTA/nr. gz (55 GB, downloaded on 2nd January 2020). Protein accession numbers were mapped to taxids using files from ftp://ftp.
ncbi. nlm. nih. gov (downloaded on January 2020). Files included: assembly_summary_refseq. txt and assembly_summary_genbank. txt downloaded from ftp://ftp. ncbi. nlm. nih. gov/genomes/ASSEMBLY_REPORTS/ to map taxids to RefSeq (GCF) and GenBank assemblies (GCA), GCF/GCA *_assembly_report. txt files downloaded from ftp://ftp. ncbi. nlm. nih. gov/genomes/all/ to map GCF/GCA to nucleotides and the GCF/GCA *_genomic. gff. gz files downloaded from the same directory to map GCF/GCA to protein accessions. Python SQLite databases were created to perform fast protein-to-taxid matching. The file prot. accession2taxid. gz, downloaded from ftp://ftp. ncbi. nlm. nih. gov/pub/taxonomy/accession2taxid/ was used to supplement databases, especially for Eukaryota. Files containing the NCBI taxonomy, names. dmp and nodes.
dmp were downloaded as ftp://ftp. ncbi. nlm. nih. gov/pub/ taxonomy/taxdmp. zip to directly assign Peptide-spectrum matches (PSMs) to the 'canonical' taxonomical levels (species, genus, family, order, class, phylum and superkingdom) of the NCBI taxonomic tree. A first-round Mascot search was performed on a reduced NCBInr-based database, termed NCBInrS, containing 45 925 selected taxa. This database comprises 76 112 114 sequences and a total of 31 417 968 972 amino acids. This first round was performed with a selection of the 50 000 best MS/MS spectra, as determined using the open-source algorithm ScanRanker. DirectTag version 1. 4. 66  (Ma et al. , 2011)  was used with the following parameters to apply the algorithm: PrecursorMzTolerance 0. 1, FragmentMzTolerance 0. 5, StaticMods 'C 57. 0215', UseChargeStateFromMS true, NumChargeStates 4.
3 according to their Mascot homology threshold; multiple PSMs were allowed per MS/MS spectrum. Each PSM associated with peptide sequences by Mascot was mapped to taxa using in-house SQLite databases built from NCBI data, as described in  Gouveia et al. (2020a). The phylopeptidomics procedure  (Gouveia et al. , 2020a)  was applied to identify taxonomies. The raw PSMs for each taxon (hereafter TSMs), the numbers of matching peptide sequences, specific peptide sequences and corresponding specific PSMs were determined for the species, genus, family, order, class, phylum and superkingdom 'canonical' taxonomical levels. Acceptance criteria for genera selection were adjusted for each individual dataset. Genera validated in the firstround search, and all their descendants, were extracted from the full NCBInr database for a second Mascot search.
All acquired spectra were used at this stage, and the Mascot settings were the same as for the first-round search, except that the maximum number of missed cleavages was set to 2, and 5 ppm mass tolerance was allowed on the parent ion. Proteins validated with a Mascot p-value of 0. 05 during the second-round search were extracted from NCBInr, and added to a selection of proteins specific to SARS-CoV-2, as detailed below, for a third-round search. SARS-CoV-2 viral genomic sequences were downloaded (2020/05/11) from the GISAID database  (Elbe and Buckland-Merrett, 2017) , and proteins were extracted after genome synchronization against the reference genome from the corresponding GFF file GCA_009858895. 3_ ASM985889v3_genomic. gff. Both these resources were downloaded from ftp://ftp. ncbi. nlm.
nih. gov/genomes/all/ GCA/009/858/895/GCA_009858895. 3_ASM985889v3/. The 186 non-redundant sequences from the N protein were added to the FASTA file used for the third-round search. This final Mascot search was performed using the same spectra and settings as those used in the second-round search. Peptides and proteins were identified with an FDR of 0. 01 calculated from the relevant decoy database search. PSMs validated with a Mascot p-value of 0. 05 during the second-round search were filtered using an FDR < 1% and subsequently used to infer peptide and protein identifications. Proteins were grouped if they shared at least one peptide. Label-free quantification was performed based on PSM counts for each protein, applying the principle of parsimony. For each phylum, spectral counts for all the proteins in a group were summed to assign abundance values to each protein group.
Acknowledgements We thank the  Nîmes University hospital  for its structural, human and financial support through the award obtained by our team during the internal call for tenders 'Thématiques phares'. C. D. -R. , D. L. , A. S. and J. -P. L. belong to the  FHU InCh (Federation Hospitalo Universitaire Infections Chroniques, Aviesan). We also express our gratitude to  CEA  for its long-term support for developing the phylopeptidomics methodology, as well as  GIS IBISA ('Infrastructures en Biologie Santé et Agronomie ') and  Région Occitanie  for their support to the 'ProGénoMix' mass spectrometry facility. J. -P. L. was supported by  Montpellier University d'Excellence (MUSE)  (MicroCOVID grant  2020 ).
Alternating H and O abundances in the upper atmosphere In addition to the gradual hydrogen rise and rapid oxygen decline in the upper atmosphere, the hydrogen and oxygen airglow exhibited periodic variations. Figure  3a, b  show the residual brightness and variation percentages of the Ly-β and 1356 airglow with respect to their four-day rolling averages (green lines in Fig. 2g, i , respectively) as a function of time. Clear periodic variations appear both in the hydrogen and oxygen residual brightness, exhibiting an alternating relationship. Using the Lomb-Scargle periodogram method  38, 39  , we obtained a periodicity of 6. 6 days in Ly-β (Fig. 3d ). A peak was observed at ~8 days for the OI 1356 emission, although this peak was smaller than the confidence level. Yet, a 6. 8-day periodicity was observed in NGIMS O density data at 180-210 km altitude (Supplementary Fig.
Hisaki Space Telescope dataset In this study, we analyzed the extreme ultraviolet (EUV) spectra obtained from the Hisaki Space Telescope  19, 20. The Martian upper atmosphere was observed from the end of August to the end of September 2016, during which a regional dust storm occurred. We obtained Mars EUV spectra every two days (except for September 28). Examples of daily-averaged spectra of HI Ly-β (1025 Å), OI 1304 Å, and OI 1356 Å airglow are shown in Supplementary Figs. 4 5 6. We calculated the average disk brightness and its error using the following equations: B Mars = ðC tot À C BG Þ Á G Á Ω Ω d ð1Þ 4B Mars = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4C 2 tot + 4C 2 BG q Á G Á Ω Ω d ð2Þ where C tot and C BG are count rates of the entire Mars disk and background observations (integrations of data in panels d and e in Supplementary Figs.
In this study, we used the solar ultraviolet irradiance of EUVM level 3 data 49 , obtained from the Laboratory for Atmospheric and Space Physics Interactive Solar Irradiance Data Center. We selected the 305 Å line as a proxy for photoionization of the CO 2 atmosphere to produce photoelectrons in the Martian ionosphere  50  and the 1025 Å and 1305 Å lines as sources of resonantly scattered HI Ly-β and OI 1304 Å airglow  13  , respectively. To display the three irradiance data in the same panel in Fig. 2a , the solar 1025 Å and 1305 Å irradiances were scaled to the 305 Å irradiance value on August 31, 2016.
Mars We also used O number density of NGIMS level 2 data. In this study, we only used inbound measurements near periapsis (150-300 km altitude range). Supplementary Fig. 3a  shows binned (5 km by 5 h) O densities between 150-300 km, which is identical to Fig. 2b. Supplementary Fig. 3b-d  show O density variations of three different altitude ranges (180-190 km, 190-200 km, and 200-210 km), which are also identical to Fig. 2c-e. To analyze their average diurnal variations, we calculated their running mean values using a 1-day window (blue dots). Supplementary Fig. 3e-h  show MAVEN geometry data. We can see that MAVEN positions developed from the nightside to the dayside across the terminator in this period. Mars Express (MEX)/Spectroscopy for the Investigation of the Characteristics of the Atmosphere of Mars (SPICAM) dataset MEX is orbiting Mars since December 2003 and has collected comprehensive dataset, including atmospheric water contents with the SPICAM instrument  22.
Curiosity/ Rover Environmental Monitoring Station (REMS) dataset The Curiosity rover landed in the Gale crater at the longitude of 137. 4°E and latitude of 4. 6°S in early August 2012  51. Since then, the rover has performed various experiments and observations to study habitable environment on Mars, including its meteorology with the REMS instrument  25. When the Hisaki telescope observed the Martian upper atmosphere, the rover was traveling over a region called Murrays Buttes, ~8 km southwest of the landing site, and the REMS instrument monitored atmospheric conditions. In this study, we used a dataset of air temperature and pressure of the REMS-reduced data stored in the NASA Planetary Data System. We averaged these measurements within the local time of 6 h and 18 h to focus on their dayside variations as shown in Fig.
21O is orbiting Mars since March 2006 to study the geology and climate of Mars47, and the MCS instrument has measured thermal emission of the Martian atmosphere21. In this study, we used a dataset of dust opacity, H 2 O ice opacity, air temperature, and air pressure measurements retrieved by MCS, which were stored in the MRO/MCS derived data records Version 5 in the NASA Planetary Data System. To study the dynamics of Mars' dayside lower and middle atmospheres, these measurements were averaged within the local time of 11 h and every 20 km altitude between 0 km and 80 km every Martian day. The dataset at an altitude of 0-20 km during Mars Year 33 is shown in Supplementary Fig. 1, and MCS geometry information during Hisaki's observation period is shown in Supplementary Fig. 7.
Acknowledgements K. M. is supported by  JSPS KAKENHI  (Grant numbers  JP21K20387  and  JP22K03708 ) and the  JAXA Hisaki  and MMX projects. N. T. is supported by  JSPS KAKENHI  (Grant numbers  JP18H05439 ,  JP18KK0093 ,  JP19H00707 ,  JP20H00192 , and  JP22H00164 ). N. Y. is supported by  JSPS KAKENHI  Grant number  JP21J13710  and the  International Joint Graduate Program in Earth and Environmental Sciences, Tohoku University  (GP-EES). Y. N. is supported by  JSPS KAKENHI  Grant Number  JP22J14954  and the  International Joint Graduate Program in Earth and Environmental Sciences, Tohoku University  (GP-EES).
Data availability The Hisaki level 2 data are publicly available in the JAXA Data Archives and Transmission System (DARTS, https://data. darts. isas. jaxa. jp/pub/ hisaki/euv/l2/). The Mars Atmosphere and Volatile Evolution Extreme Ultraviolet Monitor l3 data are publicly available at the LASP Interactive Solar Irradiance Data Center (https://lasp. colorado. edu/lisird/). Neutral Gas and Ion Mass Spectrometer l2 data are publicly available at MAVEN Science Data Center (https://lasp. colorado. edu/maven/sdc/ public/pages/datasets/ngims. html). Mars Climate Sounder and Curiosity data are publicly available in the NASA Planetary Data System (https://atmos. nmsu. edu/data_and_services/atmospheres_data/MARS/ mcs. html and https://atmos. nmsu. edu/PDS/data/mslrem_1001/, respectively). The Mars Express/Spectroscopy for the Investigation of the Characteristics of the Atmosphere of Mars data derived from ref.
21O is orbiting Mars since March 2006 to study the geology and climate of Mars47, and the MCS instrument has measured thermal emission of the Martian atmosphere21. In this study, we used a dataset of dust opacity, H 2 O ice opacity, air temperature, and air pressure measurements retrieved by MCS, which were stored in the MRO/MCS derived data records Version 5 in the NASA Planetary Data System. To study the dynamics of Mars' dayside lower and middle atmospheres, these measurements were averaged within the local time of 11 h and every 20 km altitude between 0 km and 80 km every Martian day. The dataset at an altitude of 0-20 km during Mars Year 33 is shown in Supplementary Fig. 1, and MCS geometry information during Hisaki's observation period is shown in Supplementary Fig. 7. Article https://doi.
org/10. 1038/s41467-022-34224-6 a b c Fig. 3 | Variations in hydrogen and oxygen airglow, air temperature, and air pressure during dust storm. Time series of a Ly-β and b OI 1356 airglow residual brightness according to rolling average data (the error bars denote 1σ standard deviation); and c average air pressure and temperature at local times of 6 h and 18 h, as observed by Rover Environmental Monitoring Station (REMS). Normalized Extreme Ultravioet Monitor (EUVM) and Neutral Gas and Ion Mass Spectrometer (NGIMS) dataset MAVEN is orbiting Mars since September 2014 to investigate the Martian atmospheric escape processes controlled by solar and atmo-Nature Communications | (2022) 13:6609 13 h for Mars Atmosphere and Volatile Evolution (MAVEN)/Solar spheric forcings Reconnaissance Orbiter (MRO)/Mars Climate Sounder (MCS) dataset d e power spectrum of d airglow emissions and e air temperature and pressure cal-culated using the Lomb-Scargle periodogram method.
Data collection Data for this study were collected via ve 90-minute in-depth workshops. Four initial workshops were held in December 2020 to generate initial co-design data. One additional workshop was held in September 2021, in which participants from the rst four workshops were invited to participate. In this workshop, participants were asked to elaborate on the tensions that had been identi ed in the initial workshops. Workshops were conducted via videoconference, and included interactive activities using a collaborative Google Slides (www. google. com/slides/about/) document (e. g. a 'brainwriting' activity involved participants writing quick thoughts on digital 'sticky notes' on a shared slide), and utilisation of videoconference chat functions. Participants were invited to share their views on the interactive slides, or use the chat function.
Data analyses Three forms of data were analysed: workshop audio recordings, written 'chat' data, and screen captures of interactives slides. Audio recordings of workshops were transcribed verbatim using a professional transcription service. Chat data and slide screen captures were exported as PDF les. All data were imported into NVivo  (30)  for coding. Our approach to analysis was informed by Iterative Categorisation  (31)  and re exive thematic analysis  (32) (33) (34). We followed Iterative Categorisation procedures from coding through to descriptive analysis, as a method of Familiarisation and Coding per re exive thematic analysis  (34). We developed an initial coding frame deductively according to the project research questions and questions from the workshop schedule, which we elaborated based on open coding of the rst workshop transcript as well as eld notes and observations from the workshops  (31).
The coding frame was discussed and approved by the IV, RB, MB and JG, and evolved throughout the coding process. IV and RB each coded approximately half of the interviews independently, and then reviewed the other's coding to check for consistency and generate additional insights [see Additional File 1 for the nal coding framework). For the initial descriptive analysis, we exported the coding les to Microsoft Word and summarised key ideas and sentiments in each coding extract, periodically grouping similar ideas together, and reviewing and re-grouping ideas after each code was analysed  (32) (33) (34). Finally, we collated all summary les in order to review the summarised data in its entirety, and to permit connections across different codes. At the point of descriptive analysis, our method diverged from that outlined in  Brien et al.