dataesr/scientific-paragraphs-categorization huggingface dataset:
As discussed below, this is mainly due to the retrieval of the traffic-related POC source by the AMS/ACSM based approaches especially during morning rush hours (Figs. 4  and 5 ). The highest correlation (r 2 =0. 64, n=92, p<0. 05) was observed between PMF-chemical data and EC-tracer method (Table  S7a ). The overall absolute uncertainties in the POC estimates were in the same range for all methods (about 0. 5 -1. 8 µg m -3 ) strengthening the good agreement between the different methodologies used. ECtracer and PMF off-line AMS showed low relative overall uncertainties (32% vs. 15%, respectively). However, larger uncertainties (50% vs 15 % as relative uncertainties) have been obtained for the PMF-chemical data approach (Fig. 3b ). It has already been shown that measurement uncertainties, linked to the chemical analysis of the PMF input species (Tables  S4  and S5 ), played a major contribution to the overall uncertainty  (Pachon et al.
, 2010). Here, we tried to consider all the possible sources of uncertainties from the chemical analysis procedures, notably applying a GUM (Guide to the Expression of Uncertainty in Measurement) approach for most of the organic markers  (González et al. , 2018; White, 2008). In the end, about 90 % of the total estimated uncertainty was associated to the measurement uncertainty and the PMF model uncertainty, estimated following the recommendations of  Brown et al. (2015) , accounted only for 10%. This highlights that the evaluation of analytical uncertainties must be considered very cautiously given that the input species in the PMF model are weighted by uncertainties. POC concentrations estimated from the PMF-ACSM analysis were significantly lower (datasets normally distributed, paired t-test; p-value < 0.
05) than the three other ones (about 2. 2 µg m -3 ) (Fig. 3a  and Table  S6a ). This result is mainly related to the difference in the sampling size cut-off between ACSM and filter samplings (PM1 for ACSM < PM10 for filter samples). The time series of POC fraction from the EC-tracer method correlated well with that from PMF-chemical data, while the POC variations of PMF-offline AMS were much similar with those of PMF-ACSM. These results are likely due to the similarity in chemical composition of the different datasets as the EC-tracer method and PMF-chemical data are based on filter measurements, and on the other side, offline AMS and ACSM both measured the NR-PM species including organics, sulfate, nitrate, ammonium and chloride. As presented in Figs. 2  and S12 , the average contribution of the submicron fraction to total OA concentrations was about 77%.
The remaining 23% were associated to the coarse OA fraction and possibly attributed to the dust/SCOA factors (20% and 11%, respectively) identified using PMF-chemical data and PMF-offline AMS methodologies. Considering only the fine OA fraction, total POC concentrations from PMF-ACSM showed a good agreement with both other PMF approaches namely, PMF-offline AMS and PMF-chemical data (Table  S7b ). However, a significant difference (datasets normally distributed, paired t-test; pvalues < 0. 05) was found between PMF-ACSM and PMF-offline AMS (so without SCOA) (Table  S6a ). On average for the whole period of the study, total fine POC concentrations ranged from 2. 2 µg m -3 (PMF-ACSM) to 2. 5 µg m -3 (PMF-offline AMS). As observed for the PM10 POC, overall uncertainties estimated for the PMF-chemical data method (78%) were much higher than those obtained for both other PMF approaches based on aerosol mass spectrometry measurements (8-19%).
1 -2. 5 µg m -3 ) strengthening the good agreement between the different methodologies for the estimation of these individual POC factors. Significantly higher uncertainty values were still observed for the PMF-chemical data (228% and 297% for traffic-related POA and for BBOA, respectively) (Fig. S13 ) suggesting again the significant influence of the measurement uncertainties especially considering all the potential sources of analytical uncertainties in that case. Large differences were also noticed in the relative overall uncertainties for both POA factors estimated using PMF-offline AMS and PMF-ACSM approaches (55% vs. 9% and 52% vs. 22% for HOA and BBOA, respectively) and probably linked to the measurement approaches (off-line vs. on-line measurements). Finally, for the primary fraction, time series of the PMF-chemical data dust factor and the PMFoffline AMS SCOA factor showed a good agreement for few peaks (Fig.
Comparison of the SOA factors Total SOC concentrations showed a good agreement between all the source apportionment methodologies, except for the SOA-tracer method (Fig. 6  and Table  S9 ). This observation was also confirmed as no statistical difference (datasets normally distributed, paired t-test; p-values > 0. 05) was noticed between the different approaches (Table  S6b ). Average SOC concentration values (3. 3 ± 0. 1 µg m -3 ) were similar for the EC-tracer method, PMFchemical data, PMF-offline AMS and PMF-ACSM (Fig. 6b ). Lower concentrations were derived from the SOA-tracer method (average value of about 1. 8 µg m -3 ), especially for the second half of the campaign. SOCSOA-tracer concentrations obtained here only accounted for SOA formed from the oxidation of isoprene, α-pinene, naphthalene, toluene and phenolic compounds (see section S2 in the SM).
6a ). This suggests relatively fair estimates of SOA-tracer individual factors considered in the present study and especially for biomass burning linked to the oxidation of phenolic compounds. This is further supported by the comparison between SOA-tracer method and PMF-chemical data outputs calculated over the yearlong (2015) measurement for the same site and showing a good agreement, especially for anthropogenic SOA  (Lanzafame et al. , 2020). Here, during this PM pollution event, the 3 PMF approaches especially PMF-ACSM and PMF-off-line AMS, were not able to capture the large biomass burning SOC peak observed with the EC-and SOA tracer methods at the beginning of the campaign (08 th March) and was considered as POC (or at least in part as for PMFchemical data). Note this SOC peak showed the same temporal variation as methyl-nitrocatechols, secondary photo-oxidation products of phenolic compounds (i.
S18 ). Both also showed a reasonable association with primary biomass burning factors resolved based on the mass spectrometry measurements (PMF-offline AMS and PMF-ACSM), indicating that these primary factors may contain some aged aerosols (r 2 =0. 44-0. 55, n=92, p<0. 05). SOC from the naphthalene oxidation obtained using the SOA tracer method agreed reasonably well with mixed secondary aerosols obtained by PMF-chemical data analysis (r 2 =0. 55, n=92, p<0. 05). In addition, highly oxidized OA factors (namely oxidized oxygenated OA (OOA1) and more oxidized oxygenated OA (MO-OOA)), from PMF-offline AMS and PMF-ACSM analyses, respectively) and, marine biogenic SOA and mixed secondary aerosols obtained by PMF-chemical data analysis showed satisfactory correlations (Fig. 7 ) highlighting the similar origin of the given SOA factors.
SOA derived from isoprene, α-pinene and toluene oxidation based on PMF-chemical data and SOA tracer method did not show any direct association with any of the secondary factors, probably due to their low contribution. Nevertheless, LO-OOA (from PMF-ACSM analysis) showed a substantial correlation with primary factors i. e. , biomass burning and traffic emissions (r 2 =0. 3-0. 5, n=92, p<0. 05), suggesting the influence of anthropogenic emissions. High concentrations of both highly oxidized OOA factors (OOA1, MO-OOA), marine biogenic SOA + mixed secondary aerosols and SOA from naphthalene oxidation were notably observed during the second half of the campaign (Fig. 8 ). Along with the significant inorganic loadings (Fig. 1 ), a probable predominant long-range transport influence might be expected during this period as reported before  (Petit et al.
Conclusions A comparison of five methodologies to apportion POA and SOA sources, during a PM pollution event in Northern Europe, has been performed. POA fractions (average POC = 2. 2-3. 7 µg m -3 ) were well resolved using all approaches. Only lower POC concentrations were estimated by the PMF-ACSM analysis due to the difference in the sampling size cut-off (PM1 vs PM10 for filter-based methods). PMF-chemical data POC estimates were associated with higher relative overall uncertainties (50% vs. 8%) due to the consideration of all the potential sources of errors in the sample chemical analyses. Approximately half of the OA fraction was secondary (average concentrations = 3. 3 ± 0. 1 µg m -3 ). However, the estimation of this fraction remains questionable during highly PM processed pollution events.
The SOA-tracer method was not able to capture signals from other precursors dominant during the studied period and significantly underestimated SOC (1. 8 µg m -3 ). PMFchemical data presented a good comparison with all other considered approaches and provided valuable information on the SOA contributions from different precursors. Similarly, SOC estimates from PMF-offline AMS and ACSM analysis were comparable, although they did not provide further insight on precursors and/or sources of highly oxidized fractions. The uncertainties of SOC estimates were broadly comparable in absolute value for all methods, but they were higher as relative values (14% vs. 52%) for the EC tracer, PMF-chemical data and SOA-tracer methods. These results showed that more comprehensive studies focusing on the identification of new SOA tracers from known/unknown class of precursors are needed to fully understand the origins of SOA fractions.
Acknowledgements This work has notably been supported by the  French Ministry of Environment  as part of the activities of the National reference laboratory for air quality monitoring (LCSQA). Aerosol in situ measurements have been performed as part of the  H2020 ACTRIS-2  project and partly funded by the  ACTRIS-Fr/CLAP  initiative at the national level. This work is also the part of the  COLOSSAL European COST Action   CA16109. The authors gratefully acknowledge François Truong (LSCE) and  Robin Aujay-Plouzeau (INERIS)  for taking care of samples and instrumentation, as well as.
The galaxy-catalog method was used in  Fishbach et al. (2019)  to infer H 0 from GW170817 without its optical counterpart. An estimate of H 0 from GW170814 and the photometric redshift catalog from the Dark Energy Survey (DES) Year 3 data was also obtained in  Soares-Santos et al. (2019). In this paper we report the first joint GW estimate of H 0 from detections during O1 and O2, the first and second observing runs of the Advanced LIGO and Virgo detector network. For our final result, along with the BNS GW170817, we choose the six O1 and O2 BBH detections which satisfy our selection criterion of a network signal-to-noise ratio SNR > 12 in at least one search pipeline for GW detections. The detections for which we have significant galaxy catalog support are GW150914, GW151226, GW170608, and GW170814.
The galaxy-catalog case In the absence of an EM counterpart, the analogous data comes from galaxy catalogs which provide a set of galaxies and their associated sky locations, redshifts, and apparent magnitudes. As we are in the regime where the detectability of GW sources extends beyond the distance to which current catalogs are complete, the possibility that the GW host galaxy is not contained in the catalog, because it is too faint, has to be taken into account. This is done by marginalizing over the cases where the host is in the catalog (denoted G), and where it is not (denoted Ḡ): p(x GW |D GW , H 0 ) = p(x GW |G, D GW , H 0 )p(G|D GW , H 0 ) + p(x GW | Ḡ, D GW , H 0 )p( Ḡ|D GW , H 0 ). (6) In the case where the host galaxy is in the catalog, the EM data enters in the form of information available in the galaxy catalog, as also in  Fishbach et al.
(2019)  and  Soares-Santos et al. (2019). This assumption is included in our conditioning on G. The EM information is used to modify our priors on galaxy redshift, sky location, and (apparent) magnitude, which are common among all GW events using the same catalog. This differs from the counterpart case where the EM data enters the expression as a likelihood term, x EM , a transient which is informative for only one GW event. In the case where the host galaxy is not in the catalog, the complementary condition Ḡ implies that we include the information about the limitations of the EM survey. Following  Gray et al. (2020) , we model the galaxy catalog as having an apparent magnitude threshold, m th , since galaxy catalogs are flux-limited. This magnitude threshold, alongside the intrinsic (absolute) brightness of a galaxy and its luminosity distance, determines the probability that the galaxy is inside or outside the galaxy catalog.
Gravitational-wave data The GW searches performed during the first and the second observation runs of Advanced LIGO and Virgo have led to the identification of ten BBH and one BNS mergers  (Abbott et al. 2019b ). The BNS event GW170817, well-localized and at a nearby distance of 40 +10 -10 Mpc, helped discover the electromagnetic transient from the merger, and was subsequently associated with host galaxy NGC4993. The BBHs span a large range of distances from 320 +120 -110 Mpc to 2840 +1400 -1360 Mpc and are distributed over the sky with 90% credible regions as low as 39 deg 2 to as high as 1666 deg 2. A summary of the relevant parameters of all the GW detections are given in Table  1. For the main results presented in Section 4 of this work, we choose the events which meet the selection criterion of network SNR > 12 in at least one of the two pipelines for modeled searches, namely PyCBC and GstLAL.
2020)  of SDSS (including curated versions such as the GWENS catalog,  Rahman et al. 2019) , could be used to improve the current analysis for events which fall in the SDSS footprint. In Table  1  we summarize the galaxy catalogs that we use for our analysis for each of the detections, along with the number of galaxies in the 90% error volume calculated from 3D skymaps constructed from posterior samples associated with the data release of  Abbott et al. (2019b) ,  4  and estimates of the probability that the host galaxy is in the catalog, evaluated at the median redshift for each detection assuming a Planck 2015 cosmology. In the following, we describe in more detail the galaxy catalogs that we use, quantify the probability that the host galaxy for each event is in the galaxy catalog that is used for its analysis and discuss the assessment of the completeness over the relevant localization volume for the best localized events.
Features we see in the DES-Y1 catalog are not as pronounced as the overdensity in the DES-Y3 data seen in  Soares-Santos et al. (2019). While the DES-Y3 survey is deeper, and may reveal finer features, a part of the above difference is likely also driven by the difference in the photometric redshift estimation algorithms, namely, template fitting methods such as BPZ  (Hoyle et al. 2018)  and machine learning based methods such as the ANNz2 algorithm  (Sadeh et al. 2016) , with the latter used for GW170814 in  (Soares-Santos et al. 2019). Only the former of the two has been used for the DES-Y1 catalog and a combination of both for the DES-Y3 catalog. The different selection criteria for choosing galaxies from the two catalogs, such as the stringent redshift cut placed in Soares-Santos et al.
ACKNOWLEDGMENTS Analyses in this paper made use of NumPy (Harris et al. 2020), SciPy (Virtanen et al. 2020), Astropy (Robitaille et al. 2013; Price-Whelan et al. 2018), gwcosmo (https://git. ligo. org/lscsoft/gwcosmo), and  IPython  (Perez & Granger 2007); plots were produced with Matplotlib (Hunter 2007), and Seaborn (Waskom et al. 2020). This research made use of the K-corrections calculator service available at http: //kcor. sai. msu. ru/.
Results Bioinformatic analysis of LdcF and the ldc genetic environment. A genomic survey of 4,467 prokaryote complete proteomes identified a single LAOdc sequence in Francisellaceae, which we termed LdcF. LdcF sequences found in Francisellaceae strains display a high level of sequence identity (83%), and contain four functional regions, corresponding to a wing domain (Pfam ID: PF03709), a PLP-binding domain and a AAT-like domain (both corresponding to Pfam ID: PF01276), and a C-terminal domain (Pfam ID: PF03711) (Supplementary Fig. S1 ). This corresponds to the canonical organization of the wing-containing LAOdc superfamily  26. Based on sequence comparison, LdcF proteins appear more similar to Escherichia coli LdcI (52. 65% identity) and LdcC (48. 18%) than to Pseudomonas aeruginosa LdcA (36.
coli LdcI at the same conditions  31, 32  may indicate that, similarly to LdcI and related enzymes  33, 34  , optimal LdcF activity is pH, salt, and temperature-dependent. We were able to determine the structure of F. novicida LdcF from X-ray diffraction data collected to a resolution of 3. 4 Å (Supplementary Table  S1  and Supplementary Fig. S4 ). The structure was solved by molecular replacement (MR), using the crystal structure of the decameric E. coli LdcI (PDB ID: 3N75)  32  as a starting model (see "Methods"). All Ldcs are pentamers of dimers arranged around a central pore, thereby forming a D5-symmetric decamer  32, 35, 36    crystallographic asymmetric unit, while an LdcF decamer is generated by a two-fold crystallographic symmetry axis perpendicular to the pentamer pore (Fig. 3a ).
Comparative proteomics reveals proteins impacted by ldcF deletion. A MS-based quantitative proteomic comparison was performed on whole-cell extracts of F. novicida WT and ΔldcF strains to identify proteins for which abundance was altered by ldcF deletion. A bioinformatics analysis reliably identified and quantified 1,263 different proteins from the 1,854 protein-coding ORFs annotated in the F. novicida genome (Supplementary Table  S3 ; PXD016591). An ensuing statistical analysis revealed that expression levels of 80 proteins were significantly affected by ldcF deletion. Equal numbers of proteins were expressed at lower or higher abundance in ΔldcF compared to the WT (Table  1 ). Consistent with the lack of LdcF in the deletion mutant, the 2,3,4,5-tetrahydropyridine-2,6-carboxylate N-succinyltransferase (FTN_1727, DapD) involved in lysine biosynthesis was found to be downregulated.
Surprisingly, although all proteins encoded by the FPI (FTN_1309 to FTN_1326)  5  were detected in the MS-based quantitative proteomic assay (Supplementary Table  S3 ), none of them showed altered expression levels in the ΔldcF strain. In contrast, the amount of the major transcriptional regulator MglA (FTN_1290, MglA), which is described as a FPI gene regulator  8, 48  , was significantly reduced in the ΔldcF mutant (Table  1 ). The KEGG (Kyoto Encyclopedia of Genes and Genomes) database  49  includes 99 pathways for F. novicida (https ://www. genom e. jp/kegg-bin/show_organ ism?org=ftn). Interestingly, following KEGG annotation, only 23 of the 80 proteins affected by the ldcF deletion were assigned to functional categories. These 23 proteins can be roughly grouped into a limited number of distinct functional pathways, including bacterial metabolism, DNA proofreading and repair, and pathways related to oxidative stress.
Methods Bioinformatic analyses. Sequences of AAT-fold decarboxylases were retrieved from NCBI: LdcI (NP_418555. 1), LdcC (NP_414728. 1), AdcI (NP_418541. 1), OdcC (NP_417440. 1), and OdcI (NP_415220. 1) from Escherichia coli str. K-12 substr. MG1655 and LdcA (NP_250509. 1) from P. aeruginosa PAO1. These sequences were used as seeds to query a local database containing 4,467 complete proteomes of prokaryotes (Supplementary Table  S4 ) from the National Center for Biotechnology Information (ftp://ftp. ncbi. nlm. nih. gov) with the BLASTP 2. 2. 6 software  73  and with HMM-profile based approaches with the HMMER package v3. 1b1 (default parameters)  74. Finally, searches for unannotated sequences were performed with TBLASTN (default parameters) on the complete genome sequences corresponding to the 4,467 proteomes using default parameters.
S10 ). In addition to the N-terminally His-tagged LdcF, a C-terminally His-tagged construct was also evaluated. The lysine decarboxylase activity of purified LdcF was assessed at pH 6. 5 and 37 °C using a 2,4,6-trinitrobenzensulfonic acid assay as described  31, 39  , with 8 mM lysine and 500 nM LdcF in the initial solutions before the mixture. Purified recombinant protein (1 mg/mL) was also used for rabbit immunization and production of a polyclonal antibody (Biotem, France) which appeared to also recognize E. coli LdcI but not AdcI, illustrating the close relationships between LdcF and LdcI. Crystallization, data collection and structure determination of LdcF. Prior to setting up crystallization trials, purified LdcF (50 mM HEPES pH 7, 25 mM NaCl, 0. 1 mM PLP, 1 mM DTT) was concentrated to 3 mg/mL.
Diffraction data was collected at beamline ID-29 at the European Synchrotron Radiation Facility (ESRF), Grenoble, France, and was processed using the XDS package 81 in space group C2 2 2 1 (a = 165. 25 Å, b = 318. 21 Å, c = 183. 98 Å, α = β = γ = 90). The structure of LdcF was solved by maximum-likelihood molecular replacement using Phaser 82 in the Phenix software package  83  , starting from the crystal structure of E. coli LdcI (PDB ID: 3n75) as an initial model  32  , with side-chains trimmed using the Schwarzenbacher method  84  in Sculptor from the Phenix package. The molecular replacement solution was subsequently refined in Phenix, using reciprocal-and real-space refinement, with noncrystallographic symmetry (NCS) restraints, occupancy refinement, individual B-factor refinement with TLS (translation liberation screw), and optimized x-ray/stereochemistry and x-ray/ ADP weights.
Disk diffusion method. A protocol essentially identical to that recently described for F. tularensis LVS and SCHU S4 strains was applied  20. PVX-CHA plates were inoculated onto the entire surface with early stationary phase bacteria by using a sterile cotton swabs. Sterile cellulose disks (diameter 6 mm, D. Dutscher, France) were placed with sterile forceps and slightly pressed onto the gelose surface (up to 3 discs per plate), then impregnated with 10 µL of methyl viologen dichloride hydrate (Sigma) or menadione (Sigma). After 48 h incubation at 37 °C the diameter of the zone of complete inhibition (in mm) around each disk was measured thrice from numerized agar plate pictures and using ImageJ software.
Peptides were sampled on a 300 µm × 5 mm PepMap C18 precolumn and separated on a 75 µm × 250 mm C18 column (ReproSil-Pur 120 C18-AQ 1. 9 µm, Dr. Maisch GmbH) using a 120-min gradient. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Data were processed automatically using Mascot Distiller software (version 2. 7. 1. 0, Matrix Science). Peptides and proteins were identified using Mascot (version 2. 6) through concomitant searches against the F. novicida U112 database from MicroScope  92  , the classical contaminants database (homemade) and the corresponding reversed databases. The Proline software  93  was used to filter the results: minimum peptide length of 7 amino acids, conservation of rank 1 peptides, peptide-spectrum match identification FDR < 1% as calculated on scores by employing the reverse database strategy, minimum peptide score of 25, and minimum of 1 specific peptide per identified protein group.