dataesr/scientific-paragraphs-categorization huggingface dataset:
Over the course of the campaign, the median and the average relative uncertainty of NO 2 were 13 % and 23 %, respectively. The relative uncertainty in NO 2 was estimated as a conservative upper limit at 23 % as the average of the relative uncertainties of all data points obtained during AQABA. As the zero-air measurements in the NO 2 channel produced an increased background affected by memory effects after exposure to high NO x levels, e. g. , during measurements of stack emissions, the NO 2 raw data were initially processed without converter background subtraction. Therefore, as we expect the CLD NO 2 data to be offset due to not being initially background corrected, the converter background was estimated at 112 pptv from the center of a Gaussian fit representing the difference of 1 min averaged CLD NO 2 and concurrent cavity ring-down spectroscopy (CRDS) NO 2 measurements for data points below 10 ppbv.
Setting the threshold for calculating the difference of the two concurrent data sets to 10 ppbv is somewhat arbitrary; however, changing this limit to 5 or 20 ppbv does not significantly vary the estimated offset of the CLD NO 2 data. The offset correction of 112 pptv was taken as the ultimate absolute measurement uncertainty of the CLD NO 2 measurement. Further corrections to the final CLD data include residence time corrections as well as corrections for NO and O 3 losses and the subsequent formation of NO 2 in the sampling line  (Ryerson et al. , 2000). Both NO and NO 2 CLD data have also been corrected for nonlinearities for concentrations higher than 55 ppbv, as experienced during probing of stack emissions.
Note that we use the NO 2 CLD data rather than the NO 2 CRDS data in this study, as the temporal coverage of the CLD NO 2 data over the course of the campaign is about 60 % compared with about 35 % for the cavity ring-down measurements. Formaldehyde (HCHO) was measured with an AL 4021 HCHO analyzer (AERO-LASER GmbH, Garmisch-Partenkirchen, Germany), which is a fully automatized monitor based on the Hantzsch technique  (Kormann et al. , 2003). H 2 O measurements were obtained using a cavity ring-down spectroscopy monitor (Picarro G2401, Santa Clara, California) supervised by Laboratoire des Sciences du Climat et de l'Environnement (LSCE; Yver  Kwok et al. , 2015). Measurements of OH and HO 2 were performed with the custom-built Hydroxyl Radical measurement Unit based on fluorescence Spectroscopy (HO-RUS) instrument based on laser-induced fluorescence (LIF) spectroscopy of the OH molecule and NO titration of HO 2 to OH followed by LIF spectroscopy detection of the OH molecule  (Martinez et al.
, 2010; Regelin et al. , 2013). HO 2 data used in this study are still preliminary due to the currently uncorrected interference of organic peroxy radicals (RO 2 ). The largest uncertainty due to the interference from RO 2 is 7 % or 3 pptv, whichever is higher. The 1σ accuracy of both OH and HO 2 is 20 %. The uncertainty in the OH data is estimated here as the 1σ accuracy of the data set at 20 %, whereas the uncertainty in HO 2 is estimated as √ 20 % 2 + 7 % 2 ≈ 21 %. As HO 2 data are preliminary in this study, they will be referred to as HO * 2. The wavelengthresolved down-welling actinic flux was measured with a spectral radiometer (Metcon GmbH) with a 512 pixel CCD array. The j values for NO 2 and O 3 were not corrected for upwelling UV radiation and were estimated to have a ∼ 10 % measurement uncertainty  (Meusel et al.
S2  and S3. NO and NO 2 were measured from 3 July to 31 August 2017, O 3 was measured from 22 June to 1 September 2017, HCHO was measured from 1 July to 31 August 2017, and OH and HO 2 * were measured from 18 July to 31 August 2017. For the analysis of peroxy radicals (RO 2 ) and NOPRs around the Arabian Peninsula we removed data measured during the stopovers in Jeddah (11 to 13 July) and Kuwait (31 July to 3 August) as well as during bunkering at Fujairah City (6 August, 07:00-15:00 UTC). Due to the fact that HO x data were available from 18 July 2017 onward, we have limited the net O 3 production analysis to the period after this date.
, 2002; Mannschreck et al. , 2004). To best approximate PSS in our analysis, we have restricted the estimation of RO 2 on time frames of ±2 h around noontime for which we expect the smallest relative changes in the actinic flux. Noontime for each day was determined as the center of a Gaussian fit that was applied to the actinic flux data. We applied a Gaussian fit to the actinic flux data as this fitting method is sufficient to estimate the center of the diurnal actinic flux. To further limit the effect of periods during which PSS is not fulfilled, we use the median instead of the average, which is often disproportionately biased by strong NO x sources nearby. See Tables  S3 , S5 and S7 for detailed statistics and further information on regional averages and median values. See Fig. S1  for a detailed illustration of the calculation of the fraction of the noontime integral.
A detailed calculation of the diurnal fractional integrals is given in Fig. S1. Note that all reaction rate constants used are from the IUPAC Task Force on Atmospheric Chemistry Chemical Kinetic Data Evaluation  (Atkinson et al. , 2004). Indications on whether a chemical regime is NO x -or VOC-limited can be derived from the ratio of HCHO to NO 2. Former studies have derived HCHO/NO 2 ratios from satellite measurements to establish whether O 3 production is NO x -or VOC-limited. Lelieveld et al. (2016b). Here, we use the lowest pressure level in a terrain following coordinates (equivalent to the surface level) and simulations of NO, NO 2 , O 3 , OH, HO 2 , j (NO 2 ) and j (O 1 D). The sum of peroxy radicals was estimated as the sum of all radicals R i O 2 with less than four carbon atoms. Net O 3 production based on data retrieved from EMAC was estimated as NOPR = [NO] • k NO+HO 2 [HO 2 ] + i k NO+R i O 2 [R i O 2 ] -[O 3 ] • α • j O 1 D + k OH+O 3 [OH] + k HO 2 +O 3 [HO 2 ] (8) A list of all of the peroxy radicals R i O 2 included for the reaction with NO is given in Table  S10.
Data availability. Data used in this study are available to all scientists who agree to the AQABA protocol at https://doi. org/10. 5281/zenodo. 3693988 (Tadic and Fischer, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-6769-2020-supplement. Author contributions. IT, HF and JL designed the study. UP and IT performed the CLD NO and NO 2 measurements and processed the data; JNC and PE performed the O 3 measurements; JS performed the actinic flux measurements; JS performed the cavity ring-down spectroscopy measurements of NO 2 ; DD and BH performed the HCHO measurements; HH, MM, RR, ST performed the OH and HO 2 measurements; and JDP was responsible for the H 2 O measurements. Model simulations were carried out by AP. All authors contributed to writing this paper Competing interests.
Acknowledgements. We acknowledge collaborations with the  King Abdullah University of Science and Technology (KAUST) ,  The Cyprus Institute (CyI)  and the  Kuwait Institute for Scientific Research (KISR). We would like to thank  Marcel Dorf  and  Claus Koeppel  for the organization of the campaign. We are grateful to  Hays Ships Ltd. and the  Kommandor Iona 's crew for caring about the physical well-being of the scientific participants and for an unforgettable time on board. Last but not least, we would like to thank the whole  AQABA community  for a successful campaign. Financial support. The article processing charges for this openaccess publication were covered by the  Max Planck Society. Review statement. This paper was edited by  Andreas Hofzumahaus  and reviewed by two anonymous referees.
Background: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. Results: Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called "phylopeptidomics" principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset.
Ideally, the protein databases used should be constructed only with fully sequenced bacterial genomes rigorously filtered and curated  [17]. If a database is overpopulated, to give a more comprehensive view of the microbial diversity, then its size dramatically increases, making its search fastidious but also decreasing the number of specific peptides, as previously demonstrated  [18, 19]. Accordingly, database composition has a strong influence on protein inferences and additional genomes may lead to cross-species protein matches and misidentification of microorganisms. Furthermore, when metaproteomics is performed on complex samples containing a wide diversity of organisms, many species-specific peptides will be present in lower abundance compared with peptides shared between taxa, which will be more abundant.
Because of their higher abundance, the latter are systematically favored and selected for fragmentation by tandem mass spectrometry in "shotgun" experiments. Consequently, the relative quantification of microorganisms may be erroneous if based only on a limited set of discriminative species-specific peptides. To solve the "less discriminative peptides if the database is more comprehensive" paradox and improve the relative quantification of microorganisms by metaproteomics, we propose to consider peptides shared between taxa. While being systematically rejected or outlined as a skew factor in hitherto metaproteomics methodologies, they contain information to improve discrimination and quantification of taxa. To take advantage of their value, a phylogenetic distance can be systematically computed for almost (86%) all sequenced organisms, which allows the use of the full peptidome to analyze any organism mixture.
Results Shared peptides are informative: from taxonomical proximity to phylogenetic distance dependency We assigned the MS/MS spectra recorded on the S. flexneri proteome against the comprehensive National Center for Biotechnology Information non-redundant (NCBInr) database, the largest repertoire of genomederived protein sequences. We examined the number of peptide-to-spectrum matches (PSMs) assigned to different taxa without the application of parsimony analysis, such that each MS/MS spectrum can be assigned to several peptides, and each PSM can be assigned to various taxa (Fig. 1 ). We propose to name those MS/MS spectra assigned to taxa as taxon-tospectrum matches (TSMs). We ranked these results from the highest to the lowest number of TSMs (Fig. 1 ). Because S. flexneri contains a large fraction of sequences similar to other Shigella and Escherichia species, these two genera share a large number of TSMs.
For the pure S. flexneri sample, a clear relationship was found between the number of TSMs between organisms and their taxonomical relationships. Clearly, the distribution of TSMs (Fig. 1 ) will depend on the number of organisms present in the database, and new genome-sequenced organisms can be added to this distribution when they become available. The chart conveys the idea that the number of shared PSMs, and thus TSMs, is correlated with the phylogenetic distance of the organism present in the sample to those present in the database and can be theoretically estimated. To model the abundance of shared peptides between an organism present in a sample and any other taxa, we propose to calculate their phylogenetic distances and display the results (Fig. 1 ) along these distances. To analyze the possible relationship, we plotted taxa along the horizontal axis with values corresponding to the phylogenetic distances from taxon S.
flexneri estimated with a matrix of distances for 191 organisms based on clusters of orthologous groups of proteins (COGs) that are ubiquitous across superkingdoms  [20] , and the values corresponding to the number of TSMs for each taxon along the vertical axis (Fig. 2 ). With the vertical axis in logarithmic scale, a bilinear function (lines in red and in green) could be clearly observed for the microorganisms, meaning that the curve can be modeled by the sum of two exponential terms. Additional signals are seen at the highest distance from S. flexneri, corresponding to eukaryoticspecific signals (mammal keratins, trypsin) that can be pre-filtered against a contaminant database. This representation supports the idea that it is possible to model the abundance of shared peptides (here evaluated by their spectral counts) between an organism present in a sample and any other taxa, based solely on their phylogenetic distance, using the fit of functions-which we have termed phylopeptidomic signatures-to the experimental proteomics data.
Some eukaryotic taxa show an additional signal in relation to proteins actually in the sample (keratins, trypsin…) distances for a selection of Shigella, Escherichia, and Salmonella strains (Supplementary Figure  S1 ). Remarkably, the COG x-axis allowed a much clearer separation of pairs of taxa, in full accordance with the exact taxonomical classification of the species, suggesting an improvement in precision and accuracy for this pan-domains-of-life MSA. Validation of the phylopeptidomic signature concept with experimental MS/MS datasets Three biological replicates of the S. flexneri proteome were analyzed by shotgun tandem spectrometry along a gradient of 90 min. The resulting dataset comprised 9827, 10,518, and 9540 MS/MS spectra that were tested against the NCBInr database, resulting in 2794, 3887, and 3344 PSMs, respectively (Supplementary Table  SI ).
As expected from such an inflated database, the ratio of PSMs compared with MS/MS spectra was rather low (33. 5%), but the dataset is sufficiently informative. The number of unique peptide sequences was 1746, 2512, and 2088, respectively. TSMs were extracted and, as expected, the highest value was obtained for S. flexneri, with 2753, 3806, and 3283 TSMs for the three biological replicates. TSM values for all the taxa listed in the NCBInr database were plotted along their phylogenetic distances from S. flexneri for replicate L00221 (Fig. 3a ). The signature of S. flexneri for describing the ratio of shared peptides with any other taxon was modeled using the function:    [20] , the distances used here are calculated as detailed in the "Methods" section for 14,237 taxa. The phylogenetic distance from taxon S.
flexneri 2a str. 301 is plotted on the x-axis and is calculated as detailed in the "Results" section. The phylopeptidomics signature of a single organism REF is:   SI ). For each specific dataset involving S. flexneri, the phylopeptidomic signature can be calculated and used to predict the number of TSMs of any taxon because of the shared peptides between S. flexneri and this taxon. y i;REF ¼ N REF Â A REF Â e - x i;REF a REF þ 1-A REF ð ÞÂ y i;REF ¼ N REF1 Â ðA REF Â e - x i;REF a REF þ ð1-A REF Þ Â e -.
Acquisition of experimental datasets on laboratoryassembled mixtures of two closely related bacteria and their accumulated phylopeptidomic signature We analyzed several laboratory-assembled mixtures of S. flexneri and S. bongori (1:0, 1:0. 1, 1:0. 2, 1:0. 5, 1:1, 0. 5:1, 0. 2:1, 0. 1:1, and 0:1) in biological triplicates. Both species belong to the Enterobacteriaceae family and are animal pathogens. Because of their close phylogenetic distance, these Gram-negative bacteria are difficult to distinguish, and their relative quantitation represents a true challenge, as pathogen clades tend to be densely sequenced due to their clinical interest. The peptide sequences identified against the NCBInr database were associated with all the taxa listed in this database. The number of MS/MS spectra recorded and TSMs per taxa assigned for the 27 samples at different taxonomical levels (superkingdom, phylum, class, order, family, genus, species, and strain) are listed in Supplementary Table  SI.
The parameters of this function were calculated for each experimental dataset to minimize the quadratic sum of errors between the experimental y i number of TSMs for taxon i and the sum of adjusted y i, REF1 and y i, REF2. The parameter adjustment reflecting directly REF1 and REF2 proteomic abundances was that of N REF1 and N REF2. Figure  3b  shows the sum of both signatures automatically adjusted for the metaproteomics signal of 1:1 S. flexneri:S. bongori mixture from replicate L00273 using x i, REF1 x-axis only. Similar displays could be plotted using x i, REF2 x-axis. As shown, the phylopeptidomic global signature fitted perfectly with the experimental signals. Such representation highlights the presence of the two bacteria as discrete signals detected at their phylogenetic distance. For this mixture, the MS/MS spectra datasets were 9928, 9327, and 9426 for the three biological replicates (Supplementary Table  SI ).
The numbers of PSMs assigned to the NCBInr database were 3524, 3021, and 3174. The numbers of TSMs assigned to S. flexneri 2a str. 301 were 2464, 2157, and 2297, whereas those assigned to S. bongori NCTC12419 were slightly more numerous at 2716, 2363, and 2385. The mean percentage obtained by phylopeptidomic deconvolution of the three replicates for the S. shigella signal was 47% (± 1%), which is in good agreement with the theoretical abundance value for this mixture.
4 ). The ratio estimated on the basis of the total TSMs assigned to each of the organisms is strongly biased by the number of shared peptides between organisms, leading, for example, to an evaluation of the 1:0. 1 S. flexneri:S. bongori ratio at 62% instead of 91%, and an evaluation of the 1:0 S. flexneri:S. bongori ratio at 65% instead of 100%. Quantification relying on specific peptides is also biased by the strong dependency of specific peptides on genome sequencing density and taxonomical proximity of organisms, leading, for example, to an evaluation of the 1:0. 1 S. flexneri:S. bongori ratio at 66% instead of 91%. A calculation of the relative biomasses of Shigella and Salmonella similar to the (2 + Fido) method from Kleiner et al. [11]  was also performed, using Mascot at 5% p value instead of SEQUEST+Fido at 5% false-discovery rate and selecting only those proteins with 2 unique peptides in the same NCBInr database containing both strains.
Phylopeptidomics is applicable to complex samples A metaproteomic dataset of 39,363 MS/MS spectra was acquired on the ZymoBIOMICS Microbial Community Standard, which comprises five Gram-positive bacteria, three Gram-negative bacteria, and two fungi. The corresponding signatures for the ten microorganisms and the accurate fit to the overall proteomic signal plot are shown in Supplementary Figure  S2. These results demonstrate the universal applicability of the phylopeptidomics signature. The dataset used for the biomasses quantitation includes 6163 taxa, with TSM values on the vertical axis, and 10 different phylogenetic distances displayed along the horizontal axis. This large dataset increases the statistical significance of the phylopeptidomics quantitation method and the resistance to outliers.
Therefore, we applied phylopeptidomics to a larger MS/MS dataset of 143,804 MS/ MS spectra acquired on a more complex sample comprising 22 bacterial species, 1 archaeal species, and 5 phage viruses in known quantities  [11]. This large Fig. 4  Shigella ratios deduced from the contributions of S. flexneri 2a str. 301 and Salmonella bongori NCTC 12419 strain signatures. The Shigella ratios (black circles) are in very good agreement with sample experimental ratios (linear regression R 2 , 0. 993; slope, 0. 97; R 2 to expected, 0. 992). Ratios calculated from specific peptides (medium blue circles) that (i) can only be calculated at species level and (ii) are biased by the high sequencing density and the phylogenetic proximity of the Shigella/Escherichia genera, reducing the number of specific peptides for Shigella flexneri and underestimating Shigella flexneri percentages (R 2 to expected, 0.
These rough estimations are given only for an overview of possible biomass contributions for the ten species. The phylopeptidomic signature quantitation based on TSMs is indicated for each species as well as the corresponding calculated relative biomass contributions § TSM taxon-to-spectrum matches dataset was acquired with an extended chromatography run time adapted the complexity of the sample. We extracted from Supplementary Data 1  [11]  the protein amounts for each strain, in which the signal of four species, namely Nitrosospira multiformis, Nitrosomonas europaea, Nitrosomonas ureae, and Desulfovibrio vulgaris, was too weak to be quantified. The remaining 19 microorganism species could be quantified after calculation of their respective signatures and minimization of their combined signal (Supplementary Table  SIV ).
Discussion Both identification and estimation of biomass contribution are crucial objectives that should be achieved with the best sensitivity and precision to obtain the most comprehensive view of the structure and the temporal dynamics of a microbial community. Phylopeptidomics is a new approach that improves the estimation of biomass contributions by considering both discriminative peptides and peptides shared between organisms. It relies on a specific signature for a given organism, which describes the number and abundance of peptides shared with all other organisms depending on their phylogenetic distance. In this sense, the method we propose here is fundamentally different from the procedure presented by Kleiner et al. [11] , which is protein-centered and not peptide-centered. Protein inference in metaproteomics is complex because of the large databases and the multiple occurrences of a large number of peptide sequences.
In our approach, the higher redundancy introduced into the database, through the incorporation of additional new genome sequences, does not increase possible biases but rather benefits directly for a better fit of the signatures of shared peptides. The same applies to the shared peptide fractions, which are natively used to best fit the overall signal instead of introducing quantification bias. Indeed, quantifications obtained at the finest taxonomical resolution can then be cumulated to quantify higher taxonomical levels without suffering from multiple counting of shared peptides, since the deconvolution yields quantifications cleaned from the shared signal. The phylopeptidomic signature is based on TSMs which are MS/MS spectra assigned to taxa. This new quantitative feature in metaproteomics is the equivalent of spectral count for proteins but at the taxon level.
Future improvements could be based for example on the signal derived from the intensity of the MS signal of peptides, i. e. , extracted ion chromatogram (XIC), rather than MS/MS spectra. While sometimes providing (i) a larger quantification range than spectral counts and (ii) a more linear performance for large values, XIC-based quantification is sensitive to the signal complexity in the MS scans, which is a major issue in metaproteomics. The quality of the phylopeptidomic signature is dependent on the number of available genome sequences for a given taxonomical group, and on their intrinsic quality. Thus, we recommend the inclusion of only high-quality annotated genomes in the generalist database prior to performing phylopeptidomics. Higher genome sequencing coverage of closely related species Fig.