dataesr/scientific-paragraphs-categorization huggingface dataset:
Sentinel-2 snow cover maps Wayand  et al. (2018)  suggested that snow persistence indices from Sentinel-2 images present a strong potential for the evaluation of distributed snow models in mountainous areas. Hence, maps of the snow-covered area from the Copernicus Sentinel-2 satellite mission  (Drusch et al. , 2012)  at 20 m resolution and at 5 d revisit time were considered as complementary data to evaluate CHM simulations. Sentinel-2 images from 1 September 2017 to 31 August 2018 were processed using the snow retrieval algorithm that is currently used to produce the Theia Snow collection  (Gascoin et al. , 2019). First, orthorectified top-of-atmosphere (level 1C) products were processed to bottom-of-atmosphere reflectances (level 2A) using the MAJA software version 3. 1  (Hagolle et al. , 2017).
MAJA output cloud mask and flatsurface reflectances were used as input to the LIS software version 1. 5. The LIS algorithm is based on the normalized difference snow index  (Dozier, 1989)  and uses a digital elevation model to better constrain the snow detection  (Gascoin et al. , 2019). Hansen et al. (2013)  global forest product was used to mask out pixels with a tree cover density larger than 50 % since the snow retrieval algorithm is not adapted to the detection of the snow cover in dense forest areas where the ground is obstructed by the canopy. To further avoid misclassifications due to forest obstruction or turbid water surfaces, the DEM was used to mask out pixels below 2000 m. The final snow product provided the following classification for each pixel: (i) no snow, (ii) snow, (iii) cloud including cloud shadows and (iv) no data.
Sentinel-2 snow cover maps at 20 m resolution were resampled to the same 50 m raster as the CHM output using a median filter. Maps of observed snow persistence (SP) indices at 50 m resolution were then derived following  Macander et al. (2015)  and  Wayand et al. (2018). SP represents for each pixel the ratio between the number of snow-covered days and the total number of clear-sky observations (snow or no snow). SP was computed using images from 1 April 2018 to 31 August 2018 and SP ranges from 0 (always snow-free) to 1 (always snow-covered). Over the study period, the mean number of clear-sky observations per pixel reached 18. 6 d. The same calculation was carried out with CHM outputs to derive maps of simulated snow persistence indices. The same dates as the Sentinel-2 maps were used, and for each date, the Sentinel-2 cloud and no-data masks were applied to make sure that the same pixels and dates were considered when https://doi.
This is the case in the simulations presented in this paper (Sect. 2. 2. 5). Two types of metrics were used when using ALS snow depth data for model evaluation: RMSE and Wasserstein distance. RMSE corresponds to a traditional "point-to-point" verification metric. Such a metric may favour homogenous snow cover simulations. Indeed, a snow cover simulation including avalanching may present a degree of realism, but errors in the exact location of the avalanche deposits may increase RMSE compared to a simulation without avalanching due to the double-penalty problem (e. g. ,  Nurmi, 2003). This issue is often encountered when evaluating the ability of high-resolution atmospheric models to simulate localized events such as convective precipitation (e. g. ,  Clark et al. , 2016). The Wasserstein distance  (Rüschendorf, 1985)  was used in this study as a complementary metric to evaluate the agreement between observed and simulated distributions for specific areas (elevation bands or specific slope orientations).
These results suggest that the absence of any subgrid topography effects on snow transport in CHM can partially explain the overestimation of snow redistribution from windward slopes to leeward slopes and subsequent avalanching. In addition, CHM uses the formulation for the threshold friction velocity for snow transport of  Li and Pomeroy (1997)  that only depends on snow presence and air temperature. Though based on a large multi-year observational dataset, such parameterization is empirical and does not also account for the effect of snow fragmentation during blowing-snow events  (Comola et al. , 2017) , which may lead to an underestimation of the threshold friction velocity and an overestimation of blowing-snow occurrence in alpine terrain  (Vionnet et al. , 2013). CHM may benefit from the inclusion of a more physically based snow transport routine in the future.
Conclusions This study presents a new multi-scale modelling strategy for mountain snowpacks over large regions. It combines (i) atmospheric forcing from the Canadian GEM NWP system at a convective-permitting scale  (Milbrandt et al. , 2016) , (ii) a meteorological downscaling module including a winddownscaling strategy relying on the diagnostic wind model WindNinja  (Forthofer et al. , 2014)  and (iii) the multi-scale snowdrift-permitting model CHM  (Marsh et al. , 2020a, b). This system was used to simulate the snowpack evolution for an entire snow season over a domain of 958 km 2 in the Kananaskis Valley of the Canadian Rockies. Wind simulations were evaluated using data from automatic stations in the domain. The distributed evaluation data for the snowpack simulations consisted of maps of snow depth derived from airborne lidar and snow persistence indexes derived from optical satellite imagery.
Acknowledgements. Martyn Clark  (USask) and  Barbara Casati  ( ECCC ) are thanked for their helpful scientific discussions. Special thanks are due to  Logan Fang  (USask) and  Greg Galloway (USask)  for providing quality-controlled snow and meteorological data. Simon Gascoin  acknowledges the  Centre National d'Etudes Spatiales (CNES)  for granting him access to its high-performance computer. We thank  Rebecca Mott  and  Tobias Sauter  for their careful reading and useful comments, which improved the manuscript. Financial support. This research has been supported by the  Canada First Research Excellence Fund (Global Water Futures) , the  Natural Sciences and Engineering Research Council of Canada (Discovery Grants) ,  Alberta Innovates  ( Water Innovation Program ),  Canada Research Chairs  ( CRC in Water Resources and Climate Change, CRC in Glacier Change ), and the  Canadian Foundation for Innovation and Tula Foundation.
Code availability. The open-source CHM model code (Marsh  et al. , 2020b(Marsh  et al. ,  , https://doi. org/10. 5194/gmd-13-225-2020) )  is available at https://github. com/Chrismarsh/CHM (last access: 29 January 2021). The mesher algorithm  (Marsh et al. , 2018 (Marsh et al. ,  ,  https://doi. org/10. 1016(Marsh et al. ,  /j. cageo. 2018. 06. 009. 06. 009) is available at https: //github. com/Chrismarsh/mesher (last access: 29 January 2021). The high-resolution wind library has been generated using the WindNinja diagnostic wind model (Forthofer et al. ,  2014, https://doi. org/10. 1071/WF12089; https://weather. firelab. org/ windninja/, last access: 29 January 2021) and the Windmapper tool (https://github. com/Chrismarsh/Windmapper, last access: 29 January 2021, Marsh and Vionnet, 2021).
The Sentinel-2 snow cover maps were generated from level-1C images using the free software Data availability. CRHO meteorological and snow observations are available through the web portal http://giws. usask. ca/meta/ (last access: 29 January 2021, GIWS, 2021). HRDPS forecasts are distributed on the Canadian Surface Prediction Archive (CaS-PAr, https://caspar-data. ca/, last access: 29 January 2021, Mai et al. , 2020, https://doi. org/10. 1175/BAMS-D-19-0143. 1). Sentinel-2 level 1C data were obtained from the Plateforme d'Exploitation des Produits Sentinel (https://peps. cnes. fr, last access: 29 January 2021, CNES, 2021). Final snow cover maps over the Kananaskis Valley are available on Zenodo (Gascoin, 2020, https://doi. org/10. 5281/zenodo. 3834623). The HRDPS forcing file and the ALS data used in this study are available on request to the authors.
Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is commonly used by clinical microbiology laboratories to identify pathogens, despite some limitations of the technique. The Enterobacter cloacae complex (ECC) taxonomy has recently been expanded, leading to uncertain identification of some species within the ECC when commercial MALDI-TOF MS is used. This technique is especially unsuited in the case of E. hormaechei, the main species responsible for infections and one of the most prone, within the ECC, to acquire antibiotic resistance. Hence, rapid and reliable identification at the species level could improve patient management. Here, we evaluated the performance of the Bruker Microflex MALDI-TOF MS instrument to identify ECC isolates using two databases and algorithms in comparison to the hsp60 gene sequencing reference method: the Bruker database included in the MALDI Biotyper software and an extensive online database coupled to an original Mass Spectrometric Identification (MSI) algorithm.
Among a panel of 94 ECC isolates tested in triplicate, the online database coupled to MSI software allowed the highest rate of identification at the species level (92%) compared to the MALDI Biotyper database (25%), especially for the species E. hormaechei (97% versus 20%). We show that by creating a database of MALDI-TOF reference spectral profiles with a high number of representatives associated with the performant MSI software, we were able to substantially improve the identification of the E. cloacae complex members, with only 8% of isolates misidentified at the species level. This online database is available through a free online MSI application (https://msi. happy-dev. fr/). IMPORTANCE Creation of a database of MALDI-TOF reference spectral profiles with a high number of representatives associated with the performant MSI software enables substantial improvement in identification of E.
The objective of this study was to improve ECC species identification using matrixassisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), with hsp60 gene sequencing as the reference method. Using a panel of isolates from four different hospitals, we compared the performance of two databases and algorithms: the Bruker database coupled to the MALDI Biotyper software versus an extensive online database coupled to an original Mass Spectrometric Identification (MSI) algorithm (https://msi. happy-dev. fr/). This online database is available through a free online MSI application.
RESULTS Comparative species identification with the MALDI Biotyper and MSI systems. Sequencing of hsp60 segments allowed correct species or subspecies identification for 99% (133/134) of the clinical isolates. The only isolate having a dissimilarity score of. 2% between its hsp60 sequence and that of type strain sequences (Table  1 ) was identified as E. cloacae subsp. cloacae by WGS. The replicate of one E. kobei isolate among those of the tested panel was removed because of very poor spectrum quality. The MSI software with the online database provided the highest identification rate (Table  2 ). Overall, 92% (259/281) versus 25% (71/ 281) of spectra were correctly identified at the species level with a kappa coefficient of 0. 80 versus 0. 19, using the MSI and MALDI Biotyper systems, respectively.
The identification accuracy of MSI was superior to that of MALDI Biotyper software for 6/7 species (Table  2 ). The MSI software allowed accurate identification of more than 93% of the isolates of three species (E. asburiae, E. kobei, and E. hormaechei), while less than 44% were identified with the MALDI Biotyper software. The E. hormaechei species was correctly identified in 97% of cases with the MSI system versus 20% with the MALDI Biotyper system. E. roggenkampii was never identified with the MALDI Biotyper versus 66% of cases with the MSI system. The discrepancies observed between the identifications obtained by hsp60 sequencing or using the MSI system mainly concerned spectra of E. ludwigii (n = 5) identified as E. bugandensis or spectra of E. roggenkampii (n = 5) identified as E. ludwigii, E.
kobei, or E. asburiae (see Table  S1  in the supplemental material). Among the 94 isolates analyzed with the MSI system, 87% (82/94) were correctly identified from the three technical replicates, and 96% (90/94) of the isolates were correctly identified on at least one spot of the three replicates. Finally, 4% (4/94) of the isolates were never properly identified on the three spots analyzed. Statistical log score differences seen with the MALDI Biotyper software between correct and false identification were not significant (mean 6 standard deviation of 2. 26 6 0. 08 versus 2. 25 6 0. 09, respectively; P. 0. 05). Conversely, MSI confidence scores from correct identifications were significantly higher (P , 0. 0001) than those from false identifications (mean 6 standard deviation of 57. 33 6 8.
In this case, the species identification rate considered unreliable was only 14. 6%. The species used to control the specificity of the MSI software (i. e. , Escherichia coli, Klebsiella pneumoniae, and Streptococcus pneumoniae) had similarity scores below 20. Identification of discriminant peaks. We found no single peak allowing discrimination between species. To unambiguously identify a particular species, it is therefore necessary to consider several specific and constant peaks (see Table  S2  in the supplemental material). For example, it is necessary to detect three peaks (m/z = 3. 845, 8. 517, and 8. 998) to discriminate between E. hormaechei and all other species. Of note, visualization of the average spectra of the different species with a simple interactive interface is available online (https://agodmer.
DISCUSSION MALDI-TOF MS is increasingly used by clinical microbiology laboratories for the identification of pathogenic species, despite some limitations of this technique. In this study, we showed that by creating a database of MALDI-TOF reference spectral profiles with a significant number of representatives of different species coupled with an original algorithm and software (MSI), we were able to greatly improve the identification of ECC members, with only 8% of the isolates misidentified at the species level. It is of medical importance to be able to identify ECC isolates at the species level since this complex is known to comprise important nosocomial pathogens, some of which are particularly prone to acquire antibiotic resistance. In a multicenter study from 2018 conducted in 10 French hospitals on 193 clinical ECC isolates from various samples (urine, respiratory specimens, blood cultures, and wounds), E.
hormaechei represented approximately 80% of the recovered species, and it had the highest prevalence of resistance to third-generation cephalosporins  (15). Additionally, two predominant subspecies of E. hormaechei were found to be especially prone to produce carbapanemases: i. e. , E. hormaechei subsp. xiangfangensis, mostly of sequence type ST114, and E. hormaechei subsp. steigerwaltii, especially of sequence types ST90 and ST93  (16). Conversely, carbapenemase production was only rarely reported for E. cloacae and E. roggenkampii and never for the remaining ECC species (E. kobei, E. asburiae, and E. bugandensis). The performance of the MALDI-TOF MS identification depends very strongly on the quality of the database used. ECC species are for the most part not or inadequately identified with the MALDI Biotyper (22 and see below), with, in this study, a 25% correct identification rate (Table  2 ).
In particular, of the three most prevalent ECC species, E. asburiae, E. kobei, and E. hormaechei, only 22% (49/221) were identified by the Bruker database using the MALDI Biotyper software, whereas with the MSI software, more than 97% of them were identified accurately. The main clinical species of interest, E. hormaechei, was mostly misidentified, with only 20% correct identification by the MALDI Biotyper, while with the MSI system, 97% of species were identified correctly. The main reasons for the poor performance of the commercial system are related to the spectral profile contents of the database and are listed as follows. (i) The species E. homaechei is represented by a single main spectrum profile (MSP) from the Enterobacter hormaechei subsp. hormaechei type strain, a subspecies very rarely isolated in clinical samples  (11).
(ii) E. roggenkampii, a major clinical species is not represented. (iii) Three important clinical subspecies of E. hormaechei (i. e. , hoffmannii, steigerwaltii, and oharae) are not represented in the database, making their identifications impossible. (iv) The MALDI Biotyper database falsely classifies E. hormaechei subsp. xiangfangensis as a species and not as a subspecies, implying that closely related subspecies (hoffmannii and steigerwaltii, as well as clade S) are frequently identified as "E. xiangfangensis," which may confuse nonspecialists. (v) Finally, the taxon E. cloacae is represented by 14 MSPs, 11 of which were obtained from strains identified only at the genus level (e. g. , DSM3264, Enterobacter sp. ) or belonging to E. hormaechei subsp. hoffmannii (e. g. , DSM3060) (Table  S1 ).
Thus, 173 spectra were wrongly identified as E. cloacae. Among these incorrect identifications, nearly 90% concerned the species E. hormaechei, suggesting that the spectra in the MALDI Biotyper database could derive from this species. The performance of MALDI-TOF MS identification also depends on the quality of the spectra included in the database, which depends on the extraction methods used and on the spectral acquisition method. Recently, another study aimed to develop a MALDI-TOF MS method to improve identification of the ECC members  (23). In that study, the identification performance at the species level was slightly better than ours (i. e. , 100% versus 92%), which could be explained by the modus operandi of Wang et al. (  23 ): (i) they extracted all strains using a time-consuming protocol that produces good-quality spectra but is not applicable in the daily routine of medical bacteriological laboratories; (ii) moreover, the acquisition of the spectra was performed using conditions (500 laser shot acquisitions) different from those recommended by the Bruker protocol and outside the CE-IVD or FDA specifications.
This implies having two conditions of acquisition of the spectra, two settings of the device, and a lack of practicability that can lead to risks of errors for users. To our knowledge, this is the only study that has reported peaks discriminating between ECC species using the MALDI Biotyper explorer module  (23). We also searched for discriminant peaks using the same method. In addition, we confirmed our results by extracting and processing the spectra using MALDIquant (24), a scripting module running in the R environment. Wang et al. (  23 ) described eight specific peaks (at a threshold frequency of. 97% and absent in other taxa) discriminating between E. hormaechei and other Enterobacter species, as well as 11 peaks common to all Enterobacter species except E. hormaechei. Unfortunately, none of these peaks was found to be discriminating in our study (Table  S2 ).
Our number of reference strains per species was lower and probably needs to be augmented with proteomically informative strainsespecially some from other geographical locations, which the concept of the online MSI software should allow us to achieve. At this stage, and for two reasons, we are not yet able to satisfactorily discriminate between the different E. hormaechei subspecies. First, our collection of reference strains belonging to the species E. hormaechei is incomplete. Clade S is represented by a single isolate and E. hormaechei subsp. hormaechei and subsp. oharae are not represented at all. Second, according to spectral analysis, it is delicate to identify all subspecies of E. hormaechei at first glance due to the close resemblance to spectral patterns of the subspecies. While hsp60 sequencing is a powerful means of discriminating between most of the known species and subspecies of the complex (133/134 clinical strains in this study), one isolate had an hsp60 sequence dissimilar (.
2%) to those of the type strains. It is therefore necessary to further explore the phylogenetic diversity in the ECC, in particular by using WGS. Due to their absence or scarcity in human clinical samples, our study was not able to include genera or species close to Enterobacter outside ECC as specificity controls (i. e. , Leclercia or Lelliottia); therefore, our database should be used in case of ECC identification by the Bruker system (i. e. , score of. 2. 00) since the latter system wrongly identified most of them. Some studies aim to explore the expressed virulence factors or clinical tropisms that could be species or subspecies specific. For example, some ECC strains demonstrate strong hemolytic and leukotoxic activity  (20, 21) , and some produce a type I or II Shiga-like-toxin  (21, 25) , whereas some strains of E.
hormaechei contain a pathogenicity island coding for the mobilization of extracellular iron by a siderophore such as yersiniabactin  (25). It would thus be interesting to carry out a rapid and robust identification of ECC subspecies, in particular to elucidate the involvement of certain taxa in different pathologies as well as the association of taxa and virulence factors. Access to the MSI identification system is open to the entire community of microbiologists. Collaboration with other laboratories should allow us to rapidly enrich our online database to further improve the identification of ECC species and also of E. hormaechei subspecies.
Conclusion. We have built a new reference spectral database allowing the identification at the species level, by MALDI-TOF MS, of most ECC taxa encountered in a clinical laboratory. Overall, the rate of correct identification with the MSI versus the Biotyper system was improved from 25% to 92%, with a good confidence score. Our study shows that the identification of a clinically important species (E. hormaechei) is correctly identified with the MSI software at 97% versus 20% with the MALDI Biotyper software. This is the first use of the MSI system for the identification of bacteria. Access is open to the entire scientific community, which will allow us to easily improve and enrich the database according to future advances in phylogenomics or following the observation of atypical spectral profiles.
MATERIALS AND METHODS Bacterial isolates. A collection of 134 clinical isolates belonging to seven ECC species were prospectively collected in one laboratory from four different Paris hospitals (Saint-Antoine, Tenon, Trousseau, and Cochin). These isolates were from human samples and prospectively taken from the daily workflow of the laboratory. Additionally, two reference strains, E. cloacae subsp. dissolvens (ATCC 23373) and E. asburiae (ATCC 35993), were included. All isolates were stored at 220°C (Microbank; Pro-Lab Diagnostics). Three isolates belonging to the species Klebsiella pneumoniae, Streptococcus pneumoniae, and Escherichia coli, which were not represented in the ECC database in MSI, were used as specificity controls to test the database. Online database. Among the 136 isolates (clinical and ATCC strains), a total of 42 were selected (40 clinical isolates and 2 reference strains) to set up the MSI database for MALDI-TOF MS identification.