dataesr/scientific-paragraphs-categorization huggingface dataset:
Misidentification between Taylorella equigenitalis, the causative agent of contagious equine metritis (CEM), and Taylorella asinigenitalis is observed by the gold standard culture method. The performance of MALDI-TOF MS for Taylorella species identification was evaluated using 85 T. equigenitalis and 28 T. asinigenitalis strains selected on the basis of multilocus sequence typing (MLST) data. Seven of the T. equigenitalis and nine of the T. asinigenitalis strains were used to generate in-house reference spectra to expand the existing commercial Bruker database. Two bacterial incubation times and three different sample preparation procedures were compared. Overall, we demonstrated the usefulness of MALDI-TOF MS as a differential diagnostic tool for CEM; however, commercial spectra databases should be expanded with T.
Despite (i) the high initial cost of the MALDI-TOF equipment and (ii) the accessibility to an exhaustive spectral database for the bacterial species identification, MALDI-TOF MS identification is rapid, accurate and less expensive in routine use than molecular and immunological detection methods  (Patel, 2015; Singhal et al. , 2015). The results of identification using MALDI TOF MS depend on the microorganism, sample preparation procedure, the matrix used for the reaction, and mostly the spectra present in the database used to obtain the identification. Within this context, we aimed to evaluate the performance of MALDI-TOF MS for the identification of Taylorella species. Two different bacterial incubation times and three different sample preparation procedures were compared. Sixteen in-house Taylorella reference spectra were generated during this study to expand the existing commercial Bruker database to 10 reference spectra per Taylorella species.
MALDI-TOF MS All isolates and reference strains were subcultured on ready-to-use chocolate agar with polyvitamin supplement (BioMerieux, France) for 48 h and 72 h at 37±2°C in 7% (v/v) CO 2 in air, and identified using a Microflex LT mass spectrometer (Bruker Daltonics, Germany) with MALDI BioTyper and FlexControl V3. 0 software. Three different sample preparation procedures were tested on three colonies per strain and per sample preparation procedure: (i) direct transfer (spotting) of the colony onto a target plate, (ii) formic acid overlay method that consists in depositing 1 µl of formic acid on direct colony spotting, and total protein extraction (only for T. asinigenitalis species) using the acetonitrile/formic acid protocol, according to the Bruker Daltonics manufacturer's recommendations.
0. 0 database, composed of 8 223 mass spectrometry profiles (MSPs), including three T. equigenitalis MSPs from strains DSM 10668 T , GDD 39 and GDD 40, and one T. asinigenitalis MSP from the CIP 107673 T strain. The implementation of in-house reference MSPs was done using seven T. equigenitalis strains and nine T. asinigenitalis strains, to expand the Bruker database. MSPs were generated using 26-32 spectra from total protein extracts spotted onto a target plate in eight replicates, and each spot was analysed four times. All raw spectra were done with a laser frequency of 60 Hz, an acceleration voltage of 20 kV and extraction delay time of 120 ns, and analysed according to the established MSP protocol of the Maldi Biotyper ® V1. 1 to remove suboptimal spectra. Preprocessing of the MSP was done using the Biotyper MSP creation standard method (Bruker Daltonics, Germany) with a default parameter set for the baseline correction (multipolygon with search window 5 and number of runs 2) and the spectral smoothing (Savitzky-Golay with frame size 25).
Retrospective analysis of MALDI-TOF MS results from a veterinary diagnostic laboratory The veterinary diagnostic laboratory LABOCEA22 (Ploufragan, France) implemented MALDI-TOF MS in clinical microbiology diagnosis almost 10 years ago  (2009) (2010). Between 2011 and 2017, microbiological investigations were performed on 4 920 genital samples, including 2 330 from equine species that were all CEM-negatives by the culture method according to AFNOR Standard NF U47-108 (AFNOR, 2012). Isolated bacterial colonies from these microbiological investigations were identified by MALDI-TOF MS using a Microflex LT mass spectrometer with MALDI BioTyper and FlexControl software (Bruker Daltonics, Germany). Colonies were directly spotted and overlaid with 1 µl HCCA matrix solution (Bruker Daltonics, Germany) according to the manufacturer's recommendations.
Results In all, 113 Taylorella species isolates and reference strains were selected based on MLST data to evaluate the identification and differentiation of T. equigenitalis (n=85) and T. asinigenitalis (n=28) by MALDI-TOF MS. The MLST results of these 113 strains yielded a total of 46 sequence types (ST) distributed in 31 STs for T. equigenitalis (0. 36 STs/strain) and 15 STs for T. asinigenitalis (0. 54 STs/strain). Moreover, all strains were previously identified by the gold standard culture method. Each T. equigenitalis strain was T. equigenitalis-specific IIF positive, T. equigenitalis-specific PCR positive and T. asinigenitalis-specific PCR negative, and conversely, each T. asinigenitalis strain was T. equigenitalis-specific IIF negative, T. equigenitalis-specific PCR negative and T.
asinigenitalis-specific PCR positive. Among these strains, seven T. equigenitalis and nine T. asinigenitalis were selected based on MLST data to generate 16 in-house Taylorella reference spectra to expand the existing commercial Bruker database to 10 reference spectra per Taylorella species. Colony diameter reached 1. 0-1. 5 mm after an incubation time of 48 h for T. equigenitalis and 72 h for T. asinigenitalis on chocolate agar with polyvitamin supplement at 37±2°C in 7% (v/v) CO 2 in air. In order to carry out a MALDI-TOF MS analysis as soon as the Taylorella species colonies were visualised, the 48 h and 72 h incubation times were compared. Three isolated 48 h and 72 h colonies per strain and per sample preparation procedure were processed by direct spotting and formic acid extraction, and three isolated 48 h and 72 h colonies per T.
asinigenitalis strain were also processed by total protein extraction. 1. The MALDI-TOF MS system accurately differentiated both species, T. equigenitalis and T. asinigenitalis. With the existing Bruker database, the results showed typeability of 100% for T. equigenitalis and 93-100% for T. asinigenitalis, and achieved accuracy of 100%. Overall, 85 (100%) T. equigenitalis strains were identified in all conditions tested with a species-level identification of 98. 8-100%. Additionally, 26 (93%) to 28 (100%) T. asinigenitalis strains were identified with a species-level identification of only 26-43% after formic acid extraction, 59-61% with direct spotting, and 75-77% after total protein extraction. With the Bruker database expanded with seven T. equigenitalis and nine T. asinigenitalis reference spectra (Bruker database, expanded), all results showed 100% typeability and accuracy, and all strains per conditions tested were identified with a species-level identification of 100%.
193  c MALDI-TOF MS species is not the same as the reference (scores from 1. 17 to 1. 51). 194  MALDI-TOF MS score distributions are depicted using violin plots (Fig. 1 ). As expected, the score values were increased for T. equigenitalis (Fig. 1A  and 1B ) and especially T. asinigenitalis (Fig. 1C  and 1D ) with the expanded Bruker database (Fig. 1B  and 1D ) compared to the Bruker database (Fig. 1A  and 1C ); the score values for the 85 T. equigenitalis strains ranged from 1. 52 to 2. 54 (mean ± SD: 2. 28 ± 0. 10) with the Bruker database, and 1. 84 to 2. 67 (2. 43 ± 0. 09) with the expanded Bruker database, while the values for the 28 T. asinigenitalis strains ranged from 1. 33 to 2. 47 (1. 92 ± 0. 16) with the Bruker database, and 1. 64 to 2. 78 (2. 42 ± 0. 17) with the expanded Bruker database.
For T. equigenitalis, best results were obtained when colonies were processed by direct spotting compared to formic acid extraction. Significant differences across both bacterial incubation times were observed only when colonies were processed by formic acid extraction, with better results for 72 h colonies compared to 48 h colonies. For T. asinigenitalis, better results with the Bruker database were obtained when colonies were processed by direct spotting and by total protein extraction, compared to formic acid extraction. In contrast, no significant differences were observed across the three sample preparation procedures with the expanded Bruker database.
Discussion In this study, the usefulness and accuracy of MALDI-TOF MS for the identification and differentiation of T. equigenitalis, the causative agent of CEM, and T. asinigenitalis were evaluated. With the existing commercial Bruker database, including three T. equigenitalis reference spectra and a single T. asinigenitalis reference spectrum, our findings revealed excellent MALDI-TOF MS performance at the species-level for T. equigenitalis and at the genus-level for T. asinigenitalis. The poorer results obtained with the T. asinigenitalis strains can be attributed to the presence of a single T. asinigenitalis reference spectrum included in the Bruker database that is not sufficiently representative of the species in circulation, especially in a context where the genetic diversity of T. asinigenitalis appears to be higher than that of T.
equigenitalis  (Duquesne et al. , 2013). Nevertheless, it is important to note that no T. asinigenitalis were falsely interpreted even though T. equigenitalis is its closest phylogenetic relative, confirming the potential of MALDI TOF MS to perform correct discrimination between both Taylorella species. Retrospective analysis of MALDI-TOF MS results from a veterinary diagnostic laboratory, LABOCEA22, confirmed that no T. equigenitalis and T. asinigenitalis were falsely interpreted from all microbiological investigations on CEM-negative equine samples between 2011 and 2017. Finally, the performance of MALDI-TOF MS for the identification of T. asinigenitalis became excellent at the species-level when the Bruker database was expanded with in-house T. asinigenitalis reference spectra generated during this study.
Importantly, the addition of the T. equigenitalis reference spectra simply made it possible to improve the score values that were already at species-level identification. Improvement of identification with an expanded database has been shown in various studies on different microorganisms  (Christensen et al. , 2012 , Sogawa et al. , 2012 , Pérez-Sancho et al. , 2018). The presence of exhaustive reference MSPs in a commercial database is a critical point for this technology for microorganism identification. To meet this requirement, numerous species are needed, along with expansive and time-consuming work to obtain a validated database. Two to three sample preparation procedures were compared during this study. Overall, the easier sample preparation procedure by direct colony spotting seems sufficient to obtain excellent MALDI-TOF MS performance at the species-level.
In all cases, T. equigenitalis score distributions were significantly increased by direct colony spotting. The differences between the sample preparation procedures were less marked for T. asinigenitalis. In fact, no impact of the sample preparation procedure was observed between (i) direct spotting and total protein extraction with the existing Bruker database, and (ii) direct spotting, formic acid extraction and total protein extraction with the Bruker database expanded with in-house Taylorella reference spectra. Nevertheless, T. asinigenitalis score distributions with total protein extraction were the most extensive at values <2. 00, while the number of strains identified at the species level was the highest. Two bacterial incubation times were compared during this study. Overall, the shortest bacterial incubation time (48 h compared to 72 h) was sufficient to obtain excellent MALDI-TOF MS performance at the species level.
total of 113 Taylorella species strains were analysed in this study: 110 isolates and three reference strains from the Collection Institut Pasteur (CIP, France): T. equigenitalis CIP 79. 7 T (= ATCC 35865 = NCTC11184), T. equigenitalis CIP 79. 44 (= NCTC 11225), and T. asinigenitalis CIP 107673 T (= UCD-1 = ATCC 700933). Selection of isolates was based on MLST data previously obtained according to the Duquesne et al. (2013) MLST scheme developed for both Taylorella species, and available in the Taylorella MLST database (https://pubmlst. org/taylorella/). Isolates were 83 T. equigenitalis strains from 71 horses and two donkeys (at least 35 males and 22 females) located in France (77%), Belgium, Poland, Switzerland, the United Arab Emirates and unknown geographical locations, and 27 T.
Acknowledgements We are very grateful to  Marie-France Breuil  from the  ANSES Dozulé Laboratory for Equine Diseases , the French veterinary laboratories approved for CEM diagnosis, the  Belgian  ( David Fretin  from  CODA-CERVA, Belgium ),  Polish (Wojciech Iwaniak  from the  National Veterinary Research Institute, Poland)  and  Swiss  ( Gudrun Overesch  from the  Institute of Veterinary Bacteriology, University of Bern, Switzerland) National Reference  Laboratories for CEM and Ulrich Wernery from the  Central Veterinary Research Laboratory, United Arab Emirates , which contributed to the isolation and identification of T. equigenitalis and T. asinigenitalis isolates. We also wish to thank  Craig Stevens , professional translator/editor and native English speaker, for his editorial assistance.
A total of 113 Taylorella species strains were analysed in this study: 110 isolates and three reference strains from the Collection Institut Pasteur (CIP, France): T. equigenitalis CIP 79. 7 T (= ATCC 35865 = NCTC11184), T. equigenitalis CIP 79. 44 (= NCTC 11225), and T. asinigenitalis CIP 107673 T (= UCD-1 = ATCC 700933). Selection of isolates was based on MLST data previously obtained according to the Duquesne et al. (2013) MLST scheme developed for both Taylorella species, and available in the Taylorella MLST database (https://pubmlst. org/taylorella/). Isolates were 83 T. equigenitalis strains from 71 horses and two donkeys (at least 35 males and 22 females) located in France (77%), Belgium, Poland, Switzerland, the United Arab Emirates and unknown geographical locations, and 27 T.
We also assimilate observation surface weather station data (ds461. 0) and upper-air meteorological fields (ds351. 0) from the Research Data Archive at the National Center for Atmospheric Research (https://rda. ucar. edu/datasets/ds351. 0/; https://rda. ucar. edu/datasets/ds461. 0/) using a nudging technique (the surface analysis nudging and observation nudging options 5 of WRF, which are described in details in  Lian et al. , 2018). Details regarding the model configurations used in this study are summarized in Table  2. The urban canopy parameterization is a critical element in reproducing the lower boundary conditions and thermal structures, which are of vital importance for accurate modeling of the transport and dispersion of CO2 within the urban areas. We therefore pay special attention, in this study, to examine the impact of two available urban canopy models on WRF transport results, namely 10 the single-layer Urban Canopy Model (UCM)  (Chen et al.
In-situ measurement We analyze the horizontal differences between pairs of in-situ stations as a function of wind speed and direction, expecting a larger concentration at the downwind station with respect to the upwind station, in this region of high emission. For wind fields, we use the ECMWF high-resolution operational forecasts (HRES) linearly interpolated at the hourly resolution, and extracted at a height 15 of around 25 m AGL (https://www. ecmwf. int/en/forecasts/datasets/set-i) as a proxy for all stations located within the IdF region even though the wind fields might be slightly different between the Paris city and its surroundings due to the impact of urban structure (e. g. the difference in wind speed is less than 0. 5m/s, as shown in  Lian et al. , 2018). Furthermore, the hourly afternoon CO2 data are classified into the wind classes with a bin-width of 1 m/s for wind speed and 11.
To resolve this question, we look at the spatial difference between the in-situ sites within the city (JUS-CDS) during summer. Unfortunately, the JUS instrument was not working during the summer of 2016. Therefore, we use the JUS and CDS data over the 5 summers from December 2015 to December 2018 (Figure  7c ). In general, the modeled CO2 concentration differences between pairs of in-situ stations are larger than the modeled inter-chord range of the GreenLITE™ system. During the summer, the observed absolute differences between JUS and CDS are only of a few ppm (the median is on the order of 2 ppm during July and August). These observations indicate that the spatial differences of CO2 between these two sites within the Paris city are much smaller during the summer than during the winter, and tend to support the modeling results, which would undermine the assumptions H1 10 and H2.
Acknowledgements This work is supported by the  Ph. D. program funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02  together with  Harris Corporation. We would like to thank  Harris Corporation  and the  management of Atmospheric and Environmental Research, Inc. for their support of these ongoing analyses. We acknowledge the support of  Francois Ravetta  (LATMOS/IPSL) and  Sorbonne University , for the installation of the GreenLITE™ system on the Jussieu Campus. Thanks also to  Paris Habitat , Elogie and 30  Montparnasse ICADE  for providing the locations to install the transceivers and reflectors. Finally, thanks to  Marc Jamous  at  Cité des Sciences et de l'Industrie (CDS) , to  Cristelle Cailteau-Fischbach  (LATMOS/IPSL), to OVSQ, and to LSCE/RAMCES technical staff for the maintenance of the in-situ monitoring network, coordinated by  Delphine Combaz.
Correlation analysis of operational taxonomic units and metabolite ions To identify candidate metabolites that could influence the bacterial communities associated with the roots, we determined correlations between the metabolic profiles (six independent replicates) and microbiome data (eight independent replicates) (Fig. S2 ). Since metabolome and microbiome analyses are destructive, it was not possible to compare the same samples directly. Accordingly, datasets were paired at random 100 times and the average Spearman correlation was calculated between the variance stabilised values obtained from the DESeq2 generalised linear models. To reduce the number of calculations, only bacterial taxa and metabolites showing statistically significant differences between the WT and any mutant line were considered.
Fungal Operational Taxonomic Units (OTUs) were not included in the analysis as few fungi were affected by the bx mutations. To further increase confidence levels, only correlations of magnitude >0. 5 (±) were selected. Putative identities were assigned to metabolites based on exact mass measurements, using the METLIN and PubChem databases, as reported previously  [30, 31]. The scripts used for analysis are available from the authors upon request.
Global effects of roots and Bx genotype on soil bacterial and fungal communities To examine impacts of maize roots, root type and Bx genotype on microbial communities, DNA was extracted from rhizosphere soil-covered crown and primary roots and plant-free soil. Quantification of total bacterial 16S rRNA genes and fungal ITS regions by qPCR revealed no statistically significant differences between WT and mutant plant samples (Fig. S5 ), indicating that Bx genotype and root tissue did not affect total microbial biomass. To investigate whether root type and Bx genotype changed microbiome composition, bacterial 16S rRNA genes and fungal ITS2 regions were amplified and subjected to Illumina sequencing. Following quality control and chimera removal, the bacterial dataset consisted of 8,740,090 16S rRNA gene sequences, with a mean sequence length after primer removal of 377 bp and 52,176-234,324 sequences per sample (Table  S3 ).
The fungal dataset consisted of 9,722,174 ITS sequences with trimmed sequence lengths of 130 bp after primer removal and 45,382-280,805 sequences per sample (Table  S3 ). Sequences were classified into 41,449 bacterial OTUs and 21,981 fungal OTUs. Rarefaction analysis indicated sufficient sequencing depth to capture the majority of OTUs for both bacteria and fungi in all samples (Fig. S6 ). Dominant bacterial phyla included the proteobacteria (43. 4%) and actinobacteria (29. 0%). At the class level, bacterial OTUs included betaproteobacteria (18. 3%), actinobacteria (15. 5%) and alphaproteobacteria (13. 1%, Fig. S7a ), all commonly associated with soil and/or plant roots  [7, 34]. No single bacterial OTU had a relative abundance above 2% in any sample. In contrast, fungal communities were dominated by one OTU (putatively identified as Purpureocillium lilacinum, syn: Paecilomyces lilacinus), which had a relative abundance between 49.
'Up' and 'Down' refer to abundance in bx mutants relative to wildtype the bx6 mutation (Figs. 3  and S12 ). In crown roots, 89 and 33 bacterial OTUs were altered in the bx1 and bx2 mutant, respectively, of which 22 were shared between both mutant genotypes. In primary roots, only 12 and 24 bacterial OTUs were influenced by the bx1 and bx2 mutations, respectively, of which 9 were shared between both mutants (Fig. 3a ). In crown roots, the Bx1-dependent and Bx2-dependent OTUs represented 10. 3% and 4. 1% of the sequence reads in the dataset, respectively; in primary roots Bx1-dependent and Bx2-dependent OTUs represented 14. 8% and 4. 5% of the sequence reads in the dataset, respectively. Thus, the impacts of the Bx1 and Bx2 genes in terms of numbers of bacterial taxa is greater for crown roots than for primary roots, but the relative abundances are comparable between both root types.
An unexpected outcome of the root metabolome analysis was that mutations in Bx biosynthesis genes caused major changes in root metabolites (Fig. 2 ). The bx1 and bx2 mutations, which almost completely blocked root BX biosynthesis (Fig. 1b, c ), induced the strongest shifts in the root metabolome (Fig. 2  and S4 ). This indicates that BXs act as endogenous regulators of root metabolism, in addition to their previously characterised activities as biocidal defence compounds and semiochemicals  [23]. Accordingly, the bx1 and bx2 mutations had the most significant impacts on rhizobiome composition of maize. In the most extreme case, 3% of bacterial OTUs, representing 10. 3% of bacterial sequences in the dataset were statistically affected by the bx1 mutation in crown roots. Since 31% of the bacterial OTUs, representing 25.
2% of all bacterial sequences in the dataset, were directly influenced by the presence of crown roots, these results suggest that BXs are particularly important for rhizobiome assembly. As can be expected from the proximity of the Bx1 and Bx2 genes in the BX biosynthesis pathway (Fig. 1a ), both mutations had comparable impacts on the root metabolome and associated bacterial communities (Figs. 2  and 3 , respectively). By contrast, the bx6 mutation had a relatively minor impact on the root metabolome (Fig. 2 ) and a weaker impact on bacterial OTUs than the bx1 and bx2 mutations (Fig. 3 ), which is likely due to the leaky nature of the bx6 mutation  [28]. The recent study by Hu et al. [25]  identified the DIM-BOA catabolite MBOA as a root-derived compound of maize that alters microbial soil communities.
Acknowledgements We thank  Georg Jander  and  Kevin Ahern  for provision of seeds and PCR protocols. In addition, we would like to thank all additional members of the  BENZEX consortium  for suggestions and feedback. This work was funded by an  ERA-CAPS   BBSRC grant  ( BB/L027925/1 , ' BENZEX ') to  Jurriaan Ton ,  Stephen Rolfe  and  Duncan Cameron. The research was also supported by a consolidator grant from the  European Research Council  (ERC; no. 309944  ' Prime-A-Plant ') and a  Research Leadership Award  from the.