dataesr/scientific-paragraphs-categorization huggingface dataset:
SMOS has a Sun-synchronous orbit at a 757-km altitude with a 06:00 LST ascending Equator crossing time and an 18:00 LST descending Equator crossing time. The globe is fully imaged twice every three days. The main innovative feature of SMOS is the capability for multi-incidence-angle observations at full polarization across a 900-km swath. In this study, the SMOS Level (L) 3 TB (RE04v300) products  [58]  produced by the Centre Aval de Traitement des Données SMOS (CATDS) are used. These data are projected on the Equal-Area Scalable Earth (EASE) Grid 2  [59]  with a spatial resolution of 25 km × 25 km. The main differences between the SMOS L3 TB and the other lower levels of data are: (i) the L3 TB products are expressed at the top of the atmosphere over the terrestrial reference frame (H and V); and (ii) they are bin averaged every 5 • from 2.
Topography The digital elevation model obtained by the Shuttle Radar Topography Mission (SRTM)  [60]  with a spatial resolution of 30 arc sec(approximately 1 km) was used over the Amazon Basin. These data result from the Global 30 Arc-Second Elevation (GTOPO30) computed at the U. S. Geological Survey's EROS Data Center (USGS) and were available at https://Ita. cr. usgs. gov/GOTO30. The elevation and topographic index maps over the Amazon watersheds were computed by averaging all of the SRTM elevation values present in an SMOS pixel (Figure  2 ). These data are used to flag areas with an elevation higher than 500 and/or a topographic index indicated as moderate in the SMOS flag.
Precipitation Data Precipitation measured by the Tropical Rainfall Measuring Mission (TRMM) were used over the entire Amazon Basin from 2010-2015. TRMM is a joint mission between NASA and the Japan Aerospace Exploration (JAXA) Agency and provides rainfall estimates at 0. 25 • × 0. 25 • spatial resolution. The TRMM-3B42 product  [61]  uses microwave data to calibrate the infrared-derived estimates and creates estimates that contain microwave-derived rainfall estimates when and where microwave data are available and the calibrated infrared estimates where microwave data are not available.
Static Land Cover Maps The International Geosphere-Biosphere Programme (IGBP), the GlobeCover land cover classification and the ESA CCI maps were used. The IGBP land cover map was obtained using images from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 0. 005 • during 2001-2012  [62]. The GlobeCover land surface map is based on data from the Advanced Very High Resolution Radiometer (AVHRR) data and is at a 1-km spatial resolution. Data were acquired during 1992-1993  [63]. Recently, a new release of the ESA Climate Change Initiative (CCI) Land Cover map was made available  [64]. The new water/no water global mask at 150 m was built on previous achievements using SAR systems and further improved thanks to a combination with recent Landsat-derived products.
This dataset was based on acquisitions from the years 200-2012. Data can be downloaded at: http://www. esa-landcover-cci. org/?q=node/162. For the three products, the water classes were aggregated and re-sampled on the EASE v2. 0 grid to obtain the water fraction (%) present in each cell of the EASE v2. 0 grid. The three products are static and are presented in Figure  2.
Water Level from Satellite Altimetry In the Amazon Basin, water level (in meters) time series for virtual stations calculated from the Jason-2 altimeter satellite over the period 2008-2012 were downloaded from the Hydroweb database (http://hydroweb. theia-land. fr/). The Jason-2 satellite was launched on 20 June 2008 in the follow-on mission to the Jason-1 satellite (2002-2008, CNES/NASA). It operates at Ku-band (13. 575 GHz) and C-band (5. 3 GHz) and has a time period of ∼9. 9 days  [67]. The water level computation method and the location of the virtual stations are presented in  [68]. In the present study, water level time series over 83 virtual stations from 2010-2012 inclusive were used.
Over the six years, the values of TB(θ, p) water and TB(θ, p) f orest are very constant with time. This is not the case for the time series of TB(θ, p) mixed , which shows annual cycles due to the annual inundation of the Amazon River. For the "mixed" pixel, at each date, Equation (  2 ) is computed to obtain the SWAF value. This method is generalized over all of the pixels of the Amazon Basin (the localization of the "mixed" pixel moved, but the water and forest reference pixels are always the same). The SWAF(θ, p) data are computed each day with the SMOS ascending overpass and smoothed with a sliding window of 17 days. The passive interferometric technique has some limitations over the areas with complex topography  [70]. To avoid artifacts, SWAF data are not computed over areas with moderate topography according to the SMOS flag.
Comparison and Validation In the following section, the SWAF product is compared to other data sources and variables: static and dynamic water extent maps, water level measured by altimetry satellite, in situ river discharge at the outlet of the basin and precipitation data. The static land surface maps obtained by optical sensors and the GIEMS product were also used to analyze the spatial patterns. The SWAF data were also compared to the dynamic water fraction product available over the Amazon Basin, the SWAMPS product. Note that all of these datasets are completely independent from the SMOS water surface extent maps.
Comparison to Static and Climatological Water Extent Maps The SWAF temporal average over the entire Amazon Basin had been compared to three static land cover maps (IGBP  [62] , GlobeCover  [63]  and ESA CCI  [64] ) and the mean of the GIEMS water fraction maps. These maps are detailed in Section 3. 5 and presented in Figure  2. Main similar patterns can be observed from the three different static land cover maps. The number of pixels partially flooded over the Amazon Basin is higher for the GlobeCover map than for both the IGBP and ESA CCI maps. Conversely, the ESA CCI map produces less pixels totally flooded over the Amazon Basin than the others maps. IGBP, GlobeCover and ESA CCI maps present inundation surfaces of 220,000 km 2 , 360,000 km 2 and 210,000 km 2 , respectively. Note that the static maps are based on data from different time periods (see Section 3.
Impact of the Angles and Polarization on the Water Surface Retrievals The SMOS mission provides data in multi-angular and full polarization modes. A specific analysis is presented to determine the best acquisition configuration to retrieve the water fraction from SMOS. The use of combined polarization and angles was used in this study as the single angle and channel gave satisfactory results. As shown in the previous section, the spatio-temporal evolution of the SWAF estimated using different angles and polarizations was very close in the Amazon Basin. Differences are mainly present in areas far from the main Amazon river stream characterized by high vegetation density. Table  5  summaries the best SMOS configurations (low or high incidence angles and H or V polarizations) in order to obtain the higher agreement between the SWAF product and each variable used for validation (land cover classification, water level, dynamic water fraction, precipitation and discharge).
Impact of Vegetation Cover Estimating water surface extent under dense vegetation with passive microwave is challenging. The work in  [26, 27]  provided estimates of the water surface extent over the Amazon Basin by using the passive microwave SMMR sensor at 37 GHz (Q-band). They found good agreement in the seasonal changes in inundation area and good correlations values with the water level in Manaus. However, they noticed the effects of the vegetation on their results, in particular for small patches of open water intermixed with vegetation canopies or from an attenuating effect of homogeneous canopies overlying water surfaces. To overcome the vegetation attenuation, the GIEMS product mixed passive and active microwave products at coarse spatial resolution with the optical dataset at finer resolution, which enhances the capability to detect the small water fraction.
Limitations and Prospects of the SWAF Dataset The most important limitation of the SWAF product is its coarse resolution inherent to the passive microwave sensor. This limitation was already noticed for the GIEMS  [42]  products using microwave sensors. This limitation implies that the water surface lower than 4% for all of the angles and polarization could not be mapped by the SWAF products. The SMOS sensor is based on dual polarizations and multi-angular measurements. Mountainous areas modify local incidence angles and multi-scattering, which impacts the TB values  [70]  and, consequently, the water surface estimation. The effects led to an overestimation of the water fraction. To overcome this effect, areas with moderate to strong topographic slopes were not considered in this study. The snow is also an important component of the hydrological cycle.
The SWAF product is computed from the L-band, and it can be computed easily and quickly without any ancillary data. Over this basin, the water surface extent showed a strong seasonal and interannual variability with two marked droughts in 2010 and 2015. The SWAF data were compared to three sets of static land cover maps provided from visible sensors (IGBP, GlobeCover and ESA CCI) and the average inundation extent from GIEMS over 1993-2007. It was found that the SWAF products are close to the IGBP and ESA CCI maps. On average and during the 2010-2015 period, 270,000 km 2 were inundated over the Amazon Basin. A slight overestimation of the flooded areas could be noticed. Over the Amazon Basin, the SWAF products were highly correlated with water levels measured by Jason-2 (r > 0. 8) for the significant stations.
To avoid this effect, the areas with high topography slopes were flagged in the SWAF products. Based on the SMOS product, the SWAF products declined with several incidence angles at two polarizations (H and V). It was clear that high incidence angles (>47 • ± 5 • ) were not suitable to sense the water surface from the L-band microwave signal. The H-polarization tended to increase the lower value of the water fraction extent with respect to the V-polarization. The SWAF products computed with different angles and polarizations led to similar results with very slight differences over the Amazon Basin. For future use, the authors advise the use of SWAF computed with low incidence angles (32 • ± 5 • and/or 37 • ± 5 • ) at V for the Amazon Basin. The methodology permitting retrieval of the water fraction applied in this study does not require much computation time and can be easily be applied to another L-band microwave dataset, such as the new Soil Moisture Active and Passive (SMAP) data or an older dataset (SSM/I.
). The method had been validated over the Amazon Basin by taking advantage of the numerous data and research performed over this area. In the near future, this recent water surface fraction product can be easily extended with the future SMOS data and the Soil Moisture Active and Passive (SMAP) data to obtain a long record of inundation products under dense vegetation. These data will be useful to better understand the water, carbon and methane cycles over the tropical areas. By adding a third component (saturated soil) on the first-order radiative transfer, this method is likely to be applied in other regions in the world.
80% and a total of 1,697 coding sequences. The genomic analyses using the nucleotide sequences of the 16S rRNA gene, hsp60 and gyrB genes, as well as the GyrA protein sequence, and the results of Average Nucleotide Identity and in silico DNA-DNA hybridization suggest evidence for a novel Helicobacter species close to Helicobacter equorum and belonging to the group of enterohepatic Helicobacter species. As soon as the particular peptide mass fingerprint of this pathogen is added to the spectral databases, MALDI-TOF MS technology will improve its identification from clinical specimens, especially in case of "sterile infection". We propose to associate the present strain with the Latin name of the place of isolation; Caesarodunum (Tours, France) and suggest "Helicobacter caesarodunensis" for further description of this new bacterium.
, 2011; Angeletti, 2016). Databases associated with MALDI-TOF MS systems are regularly updated with novel spectra in order to increase the accuracy of identification. Nevertheless, this technology fails when the bacterial protein spectrum is absent or too different from those in the database. Fortunately, whole genome sequencing has become popular and easy to access for many clinical laboratories, and many free-access bioinformatics tools can be quickly run to analyze genomic data. In the present study, we report the identification and characterization of a strain that MALDI-TOF MS failed to identify, belonging to the Helicobacter genus, and recovered from a patient suffering a bloodstream infection. Based on the results of routine identification tests, electron microscopy and whole genome sequence analysis, this strain differed from all previously described Helicobacter species and was named S15.
MALDI-TOF MS Assessment Colonies were subjected to a Bruker Daltonics biotyper device using the Microflex platform (Bruker Daltonics, Bremen, Germany) as previously described  (van Veen et al. , 2010). Single colonies were transferred onto a target plate and overlaid with matrix solution. FlexControl software (version 3. 4) was used for measurements; this version includes eight Helicobacter species (Helicobacter canadensis, Helicobacter canis, Helicobacter cholecystus, H. cinaedi, Helicobacter fenneliae, Helicobacter mustelae, Helicobacter pullorum, and H. pylori with 1-9 spectra for each species). The spectra were analyzed using BioTyper software (version 3. 1. 66; Bruker) and MBT-Compass and MBT-RUO databases, and extracted profiles were compared to that of H. pylori.
Genome Sequencing and Analysis Genomic DNA obtained from colonies was purified using the DNeasy kit (Qiagen, Courtaboeuf, France). DNA was subjected to whole genome sequencing on the Illumina HiSeq 2500 (Illumina, San Diego, CA, USA) using 200 base-pair reads with paired-ends according to the Truseq protocol (Illumina), following the manufacturer's recommendations. The quality of sequence reads was assessed with the Fastqc program (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and reads were quality filtered using the fastq-mcf program (https://expressionanalysis. github. io/ea-utils/). Genome assembly was performed using the Edena v3 assembler  (Hernandez et al. , 2014). Assembled genomes were annotated using the Prokka v1. 10 program  (Seemann, 2014). The "Get_homologues.
pl" script was used for core proteome comparisons  (Contreras-Moreira and Vinuesa, 2013). The CVTree3 Web Server was used to perform the phylogenetic analysis  (Xu and Hao, 2009). Prokka annotation and blastP analysis were performed to identify specific genes involved in the phenotype, evolution, and virulence of the strain. The predicted gene function is based on protein similarity of at least 40% and an E-value of 10e-6. Phylogenetic tree topologies of nucleotide (16S rRNA gene, gyrB and hsp60 genes) and predicted amino acid alignments (for GyrA) were constructed with the neighborjoining method using the MEGA (Molecular Evolutionary Genetics Analysis) software version 6. 06  (Trespalacios et al. , 2015). The sequences previously created by  Ménard et al. were used for that purpose  (Ménard et al.
, 2016)  (The accession numbers are provided in Supplemental Table  S2 ); the sequences from strain S15 were obtained after genome sequencing. Four phylogenetic trees were constructed based on the 16S rRNA gene, hsp60 and gyrB genes and the GyrA protein sequence according to phylogenetic analyses previously described by  Ménard et al. (2016). The evolutionary distances were computed using the Kimura 2-parameter method for the 16S rRNA, hsp60 and gyrB genes, and the Poisson correction method for GyrA.
Determination of Average Nucleotide Identity (ANI) and in Silico DNA-DNA Hybridization (DDH) ANI-and DDH-values were assessed in silico using online tools. An assembled genome of strain S15 was uploaded in GGDC (http://ggdc. dsmz. de/ggdc. php#) with the recommended local alignment tool BLAST+ and compared with the closest genomes available in public databases identified in the different phylogenetic trees, to obtain DDH-values. The accession numbers of the strains included in the phylogenetic analyses are provided in Supplemental Table  S1.
Antibiotics were stopped after 3 weeks because of an agranulocytosis, but by that time, the patient was clinically cured of this infection. MALDI-TOF MS performed in the context of routine analysis failed to identify the isolate and showed poor scores (unacceptable score for identification of 1. 1 with H. pullorum with the Bruker Daltonics Biotyper). Profiles obtained with six different colonies from the isolation plate were superimposed and showed a unique profile (Supplemental Figure  S1A ). The two highest scores obtained were Staphylococcus capitis and Lactobacillus paracasei; these profiles were then extracted from the database and compared with that of strain S15. However, as shown in Supplemental Figures  S1B, C , these two profiles were totally different from strain S15, suggesting distant organisms not phylogenetically related.
DISCUSSION AND CONCLUSION We identified in the blood cultures from a febrile patient a Gram-negative rod-shaped mobile bacterium requiring a prolonged time/incubation (almost 5 days) for detectable growth. MALDI-TOF MS used in the routine identification process failed to reliably identify this bacterium (named S15 strain), because of the bacterial protein spectrum absent in the current database. Its belonging to the Helicobacter genus was suggested by the phenotypic characteristics of the strain. The whole genome sequencing data strongly provided evidence for a strain close to, but distinct from, several Helicobacter species. A clear distinction can be made between S15 and the major well-characterized Helicobacter species based on the phenotypic characteristics (e. g. , ability to grow at 42 • C, alkaline phosphate hydrolysis, number and disposition of flagella; Supplementary Table  S3 ).
WGS analysis provided evidence that the S15 strain is close to H. equorum, a bacterium that does not commonly infect humans  (Moyaert et al. , 2009)  and is rarely found in human samples. It should be noted that the number of nucleotide sequences in public databases has evolved rapidly facilitating genome comparison. Public databases already contain an impressive number of 16S rRNA gene sequences, a lower number of gyrA/B sequences and only a limited diversity of Helicobacter genomes. Based on all available sequences, comparison at the whole genome level showed a sharp distance between our S15 strain and H. equorum but clearly suggest that our strain belongs to a likely novel Helicobacter species. We propose to associate the present strain with the Latin name of the place of isolation; Caesarodunum (Tours, France) and suggest "Helicobacter caesarodunensis" for further description of this new bacterium.
Septicemia associated with a Helicobacter that is not characterized at the species level is not scarce  (Schwarze-Zander et al. , 2010). As soon as the databases associated with MALDI-TOF MS systems are updated with the peptide mass spectra of strain S15, the identification of S15-like Helicobacters will be improved in routine clinical laboratory settings. Further isolations of this bacterium in patient clinics will enable a better understanding of bacterial pathogenicity, origin, and potential routes of transmission.
GENOME SEQUENCE ACCESSION The whole genome sequence of the S15 isolate has been deposited at DDBJ/ENA/GenBank under the accession number NESU00000000. The version described in this paper is version NESU01000000. The 16S rRNA gene has been also deposited under the accession number KX057485. The phylogenetic analyses were generated with the neighbor-joining method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches. The trees are not rooted and drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Kimura 2-parameter method for 16S rRNA gene, the Poisson correction method for GyrA and are represented in the units of the number of base substitutions per site.
The analysis included 34 sequences. All ambiguous positions were removed for each sequence pair. Evolutionary analyses were conducted using MEGA6. All sequences are labeled according to species, strain name, collection number in brackets. Mycobacterium tuberculosis was used as the outgroup sequence. All accession numbers are provided in Supplementary Table  S2. van Veen, S. Q. ,  Claas, E. C. , and Kuijper, E. J. (2010). Highthroughput identification of bacteria and yeast by matrix-assisted laser desorption ionization-time of flight mass spectrometry in conventional medical microbiology laboratories. J. Clin. Microbiol. 48, 900-907. doi: 10. 1128 /JCM. 02071-09 Varon, C. , Mocan, I. , Mihi, B. , Péré-Védrenne, C. , Aboubacar, A. , Morate, C. , et al. (2014). Helicobacter pullorum cytolethal distending toxin targets vinculin and cortactin and triggers formation of lamellipodia in intestinal epithelial cells.
Information extracted from these trees suggests that the S15 strain most likely corresponds to a new enterohepatic sheathed Helicobacter species close to H. equorum. The closest relatives of S15, identified using the phylogenetic trees, were used to calculate in silico values of DDH and ANI. Pairwise ANI-values of the closest strains identified by the algorithm in the JSpeciesWS database are depicted in Supplementary Tables S3A,B, respectively. The highest DDHvalues were 39. 40% obtained for the comparison with the genome of H. canadensis MIT98-5491 and 26. 5% for H. equorum QF1. Comparison with the other species from the 16s RNA and GyrA phylogenetic trees (H. bilis and H. pullorum) yields a distance of ≥0. 975 and a probability that DDH is >70%.
Information extracted from these trees suggests that the S15 strain most likely corresponds to a new enterohepatic sheathed Helicobacter species close to H. equorum. The closest relatives of S15, identified using the phylogenetic trees, were used to calculate in silico values of DDH and ANI. Pairwise ANI-values of the closest strains identified by the algorithm in the JSpeciesWS database are depicted in Supplementary Tables S3A,B, respectively. The highest DDHvalues were 39. 40% obtained for the comparison with the genome of H. canadensis MIT98-5491 and 26. 5% for H. equorum QF1. Comparison with the other species from the 16s RNA and GyrA phylogenetic trees (H. bilis and H. pullorum) yields a distance of ≥0. 975 and a probability that DDH is >70% between these species and S15 of 0%. The highest ANI-value was 93% between S15 strain and H.
Quantification of transcripts were performed by calculating fold changes using 2 -ΔΔCt method, corrected by qPCR efficiency for each gene, and normalised to the control condition at 0 h, as well as the control gene expression of 16S rRNA  67. Statistical significance was assessed using multiple t-tests coupled with the Holm-Sidak method, as implemented in Graphpad Prism 6. Whole genome sequencing and annotation. Once identification and purity of mycobacterial isolates were confirmed by both 16 S and rpoB sequencing, total DNA were processed for whole genome sequencing, using GS junior 454 pyrosequencer (Roche). Library preparation, emulsion PCR and sequencing were performed following manufacturer's protocols. Raw sequences were then assembled using GS denovo Assembler software (Roche). The assembly was uploaded for annotation using RAST online annotation tool  68, 69.