dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients In the context of epidemiological screening, we tested 129 isolates of Leishmania sp. Collected in French Guiana between 2011 and 2014 for the presence of LRV1  [21]. Here, we analyzed a subset of previously identified LRV1-positive isolates obtained from patients diagnosed with ACL. A total of 20 parasite cultures, obtained from human biopsies isolated from 16 men and four women, aged 19-66 years with a median age of 35 years, were selected for high-throughput sequencing. The data (gender and age) collected for each patient are listed in Table  1. In all, we sequenced 19 LRV1-positive L. (V. ) guyanensis isolates and one LRV1-positive L. (V. ) braziliensis isolate.
Global analysis of high-throughput sequencing data Using Illumina MiSeq sequencing technology, 24 full-length or almost full-length genomic sequences were de novo assembled into individual contigs with a mean 49. 8 depth of coverage. Sequences were 4. 8-5. 3 kb long. Alignment of the full-length genome sequences obtained with those available in the database made it possible to design three sets of consensus-degenerate primers (Table  2 ). These primer pairs were used for conventional RT-PCR and Sanger sequencing to extend the sequences to the 5' and 3' ends that were missing for some strains. High-throughput sequencing combined with conventional RT-PCR applied to the 20 LRV1-positive isolates generated almost complete genomic sequences (from nucleotide 60 to 5248 of LRV1-1, accession number NC_002063) from each viral strain.
Sequence identities The almost complete genome sequences obtained for each strain as well as the nucleotide and amino acid sequences of the capsid and RNA-dependent RNA polymerase were compared to one another and to those published in the databases. All sequences were unique. On the basis of the common % 5260 bp sequences obtained for each strain, pairwise comparisons showed that nucleotide sequence identities ranged from 76  ) nucleotide identities between them and with the LRV1-1 and LRV1-4 5'UTR sequences. Of interest, it should be noted that YA70, which corresponded to the only sequence obtained from a L. (V. ) braziliensis isolate, was the most divergent sequence. If we exclude it from the data set, the nucleotide sequence conservation between LRV1 5'UTR sequences from L. (V. ) guyanensis isolates ranged from 88.
In addition, analysis of the stem-loop IV sequences located upstream, from nucleotide 266 to 281, showed that all but two sequences were 100% conserved and identical to the published one  [15]. The only two divergent sequences corresponded to strains 2001 and XJ93 G2. Strain XJ93 G2 presented two complementary substitutions at positions U269C and A278G of the stem, while strain 2001 presented one substitution at position A278G able to form a wobble base pair with the uracil at position 269 (all positions are given relative to LRV1-1 sequence, accession number NC_002063). This shows that LRV1 5'UTR sequences are strongly conserved in both nucleotide sequences and predicted RNA secondary structures.
study are shown in bold. Sequence identifiers include the strain ID and the NCBI accession number. The major clades representing the different LRV1 genotypes identified (A-F) as well as the clades of LRV1 isolates from L. (V. ) guyanensis (Lg) or L. (V. ) braziliensis (Lb) parasites are labeled. Posterior probabilities of the Bayesian analysis (>90%) are shown next to each node. The scale bar indicates nucleotide sequence divergence among sequences. https://doi. org/10. 1371/journal. pntd. 0005764. g002.
This study was funded by a  European commission "REGPOT-CT-2011-285837-STRonGer" grant  within the  FP7  and an " Investissement d'Avenir " grant managed by the (V. ) Agence Nationale de la Recherche  ( CEBA, Ref. ANR-10-LABX-25-01 ). High-throughput sequencing was performed at the  Genomics Platform , a member of the " France Genomique " consortium ( ANR10-INBS-09-08 ). ISC was funded by a  Calmette & Yersin  grant from the  Institut Pasteur International division. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The 110 × 110 km 2 footprint of each tile can be obtained in a Keyhole Mark-up Language (KML) file from Sentinel-2 internet site  [8]. The projection is computed using a global Digital Elevation Model (DEM) with approximately 90 m horizontal resolution and 10 m of elevation uncertainty. Reflectance images are provided with a spatial resolution of 10, 20 or 60 m depending on the spectral bands. Sixty meter images are obtained by spatial binning of measurements sensed at 20 m resolution as recalled in Section 2. 4. Sentinel-2 L1C products take the form of folders gathering geolocalised JPEG2000 images for each spectral band, metadata in xml format and auxiliary data. Images are gathered in folders for.
The 110 × 110 km 2 footprint of each tile can be obtained in a Keyhole Mark-up Language (KML) file from Sentinel-2 internet site  [8]. The projection is computed using a global Digital Elevation Model (DEM) with approximately 90 m horizontal resolution and 10 m of elevation uncertainty. Reflectance images are provided with a spatial resolution of 10, 20 or 60 m depending on the spectral bands. Sixty meter images are obtained by spatial binning of measurements sensed at 20 m resolution as recalled in Section 2. 4. Sentinel-2 L1C products take the form of folders gathering geolocalised JPEG2000 images for each spectral band, metadata in xml format and auxiliary data. Images are gathered in folders for each granule, together with quality masks in Geographic Mark-up Language (GML) format and tile metadata.
Level-2A Products Description Level-2A surface reflectance products are generated with Sen2Cor processor whose main purpose is to correct Sentinel-2 Level-1C products from the effects of the atmosphere. Level-2A processing is applied independently to single granules of TOA Level-1C orthorectified reflectance products. Note that this independent processing of neighbouring granules can lead to visible steps between adjacent granules. Sen2Cor processor is available as a third-party plugin of the Sentinel-2 Toolbox  [9]. L2A products provide: • The structure of the Level-2A product  [10]  is strictly based on the structure of the Level-1C product. The same tiling geometry and projection are used. The main difference is that the IMG_DATA folder contains three directories: one for each resolution at 60 m, 20 m, and 10 m.
The Scene Classification map is available at 20 m or 60 m resolution and the cloud and snow probabilities are located in the QI_DATA folder. The Scene Classification module provides a Scene Classification map divided in 11 classes presented in chapter 5. This map does not constitute a land cover classification map in a strict sense, its main purpose is to be used internally in Sen2Cor in the atmospheric correction module to distinguish between cloudy pixels, clear pixels and water pixels. Two quality indicators are also provided: a Cloud confidence map and a Snow confidence map with values ranging from 0 to 100%. The Water Vapour content is retrieved from Level-1C image using a Sentinel-2 adapted APDA (Atmospheric Pre-corrected Differential Absorption) algorithm  [11]  which uses a ratio between band B8A and band B09.
Sen2Cor can be also applied over water surface using the AOT estimated over land pixels in the image. However the processor contains no consideration of water surface effects like sun glint. The surface reflectance values are coded in JPEG2000 with the same quantification value of 10,000 as for Level-1C products, i. e. , a factor of 1/10,000 needs to be applied to Level-2A digital numbers (DN) to retrieve physical surface reflectance values. In the R10m directory of the IMG_DATA folder are located the surface reflectance images with 10 m spatial resolution for the following bands: B02, B03, B04 and B08. In the R20m directory, the 20 m resampled bands B02, B03 and B04 are provided together with the following surface reflectance bands: the "red edge" bands B05, B06, B07, the NIR band B8A and the two SWIR bands B11 and B12.
Level-1 Calibration and Validation Status Calibration and Validation (Cal/Val) correspond to the process of updating and validating on-board and on-ground configuration parameters and algorithms to ensure that the product data quality requirements are met. These activities are performed within the frame of the Mission Performance Centre (MPC). These operations are achieved through products processing and analyses at different processing levels. This section provides an overview of the Cal/Val activities that have been performed on Sentinel-2A unit.
Internal Controls As producer of the GRI, IGN is not part of the validation team. However, some internal checks are performed before the GRI is delivered to the validation team. This step is achieved just after the refining task, using a derivative of the Reference 3D® product. This derivative, called BDAmer, is a worldwide set of GCPs, directly extracted from the whole Spot 5 HRS archive, that has been spatio-triangulated before being turned into the Reference 3D® product (a global orthoimage layer). This database has a very high density, which helps to provide trustful statistical results in IGN checking step. The GCPs of the BDAmer database were automatically extracted and fully qualified. One strong advantage of this product is that every GCP is seen in stereoscopy (thanks to Spot 5 HRS instrument), sometimes with a higher multiplicity, and a long time interval.
Consequently, a quality index can be computed from these parameters, as well as other indicators such as geometrical accuracy, multiplicity, durability, etc. All available 200 × 200 pixels Spot 5 HRS cropped and centred images are associated to each GCP. As derived from Reference 3D® , the BDAmer database is accurate enough to be considered as a reference for Sentinel-2 images, although the estimated accuracy may locally vary. Since the multiplicity of each BDAmer point is higher than 2, each check point is correlated at least twice to limit the risk of false correlation. It also helps to increase the confidence in the result. These checking operations can be considered as fully independent since the GCPs used to refine the models are not extracted from this product (or other derivative). To collect homologous points between Sentinel-2 and BDAmer, the method is identical to the extraction of GCPs.
GRI processing over Australia is relatively straightforward for several reasons: the climate is helpful to find cloud-free images and the territory is very compact. The final selection covers almost the whole territory without any cloud. There are a couple of holes left, but it is not considered as blocking the use of the GRI in the Sentinel-2 context. Indeed, acquisitions are several thousands of kilometres long, so that enough GCPs can be found almost everywhere in the image. The extraction of the GCPs for this block was facilitated by the availability of the AGRI database. At first, candidate points were extracted from the orthorectified AGRI database, using SURF methods (see Figure  21 ). Then these points were resampled at the resolution of Sentinel-2, and a correlation process was applied to find the homologous point in the Sentinel-2 image (orthorectified).
About 800 points were found using this method, leading to a homogeneous repartition shown in Figure  22. Most of the points of interest found by the process are dark or light items of the landscape. The extraction of the GCPs for this block was facilitated by the availability of the AGRI database. At first, candidate points were extracted from the orthorectified AGRI database, using SURF methods (see Figure  21 ). Then these points were resampled at the resolution of Sentinel-2, and a correlation process was applied to find the homologous point in the Sentinel-2 image (orthorectified). About 800 points were found using this method, leading to a homogeneous repartition shown in Figure  22. Most of the points of interest found by the process are dark or light items of the landscape. The extraction of the GCPs for this block was facilitated by the availability of the AGRI database.
At first, candidate points were extracted from the orthorectified AGRI database, using SURF methods (see Figure  21 ). Then these points were resampled at the resolution of Sentinel-2, and a correlation process was applied to find the homologous point in the Sentinel-2 image (orthorectified). About 800 points were found using this method, leading to a homogeneous repartition shown in Figure  22. Most of the points of interest found by the process are dark or light items of the landscape. The identified parameters to refine are an order 1 on yaw, pitch and roll and an order 0 on magnification. If acquisitions are very long, the order 1 is very constrained, to avoid a "leverage effect" along track. In this case, there are seven unknowns by product to determine, with a high redundancy in the observations, since there are 800 GCPs, and about 35,000 tie points.
Methods The focal plane calibration consists in re-estimating the lines of sight of the detectors from the various retinas, i. e. , estimating distortion and possible discontinuities between the different arrays of the focal plane to improve internal image consistency. Such accurate measurements are not possible on ground before launch. In flight, an absolute method is used: it is based on the correlation between an image acquired by one of the retina to be mapped and an absolute reference image (orthorectified database, the so called BDortho, done by IGN over France). B4 has been chosen as the reference retina for Sentinel-2. Using absolute reference images guarantees planimetric and altimetric accuracy consistency with requirements because they are geometric references close to ground truth.
The conversion from irradiance reflectance to marine reflectance above sea surface (ρw) follows  [22]. To ensure a 0% cloud coverage over the region of interest (ROI), the target dataset is automatically cloud-screened using DIMITRI (Database for Imaging Multi-spectral Instruments and Tools for Radiometric Inter-comparison) package (https://dimitri. argans. co. uk). To ensure a low concentration of phytoplankton and sediment, and pure marine aerosol, six Cal/Val sites were selected following  [23]  (Figure  27  and Table  8 ). B04 for Sentinel-2) since the Rayleigh calibration is supposed to be very efficient at this wavelength and can provide an absolute calibration value for this band. The favourable imaging zones for this method are warm ocean sites in tropical latitudes where cumulonimbus clouds develop, like over the Maldives or in the Gulf of Guinea.
Ground-Based Reflectance Measurements The ground-reflectance-based approach has been used over the Radiometric Calibration Test Site (RadCaTS; Figure  27 ) to perform the absolute radiometric vicarious calibration for two decades  [25]  and recently operationally for Landsat-8  [26] [27] [28]. In this study are used the ground-based measurements provided by NASA (Landsat Cal/Val Team) via ESA as part of the ESA-NASA agreement. Five cloud-free S2A overpasses were obtained over the Railroad Valley Playa site (RRVP) but only one overpass coincides with the S2A time-series from the processing baseline 02. 01 used here. The chosen reference is the TOA normalized reflectance reconstructed by the University of Arizona team using ground and atmosphere radiometric measurements. Note that, the dataset has been specifically generated for the spectral bands of S2A using hyperspectral reference profiles of the site.
Ground-Based Reflectance Measurements Results In this analysis, the reference is the TOA normalized reflectance reconstructed by the University of Arizona team using ground and atmosphere radiometric measurements. It is worth to note that the dataset have been specifically generated for the spectral bands of S2A/MSI using hyperspectral reference profiles of the site. Table  10  displays the vicarious calibration coefficients (A b ) estimated for S2A/MSI. Almost all the VNIR bands show gain less than 3%, except B01 and B04 where the gain is about 5%. Both SWIR bands display gain coefficient higher than 5% (not shown). The uncertainty of the method estimated by the University of Arizona team is of the order of 3% for most bands, and up to 6% for B01 and B02 due to a higher impact of atmospheric effects [27,28].
Pseudo-Invariant Calibration Site Methodology Desertic PICS have been exploited since decades for multi-temporal monitoring and cross-calibration in the solar spectral range  [31]. PICS algorithm aims to simulate the TOA reflectance in the visible to near-infrared (VNIR) spectral range over pre-defined desert sites (e. g. , CEOS PICS sites  [32] ). The first step of this method consists on building a reference reflectance model for the selected site, and the model calibration using TOA measurements from a reference sensor (MERIS in  [29] ). TOA measurements are propagated to the surface using an inverse Radiative Transfer simulation. A database of BOA measurements with various acquisition geometries (Sun and viewing zenith angles and relative azimuth angle) is built. The database is used to fit a four-parameter BRDF model (for each spectral band).
Results and Analysis As mentioned above, this method considers the VNIR spectral range only  [29, 30]. Moreover, the gain coefficients are, in fact, relative calibration coefficients as MERIS is considered as a reference to compute the surface BRDF of the test site. After the ingestion of the whole datasets from S2A/MSI, the ratio R b , defined as observed/simulated (ρ obs /ρ sim ) reflectance, is then computed over the six desert sites (Table  11 ). Figure  32  shows time series of R k from S2A/MSI bands B01, B03, B07 and B8A over the six PICS sites over the period June 2015-July 2016. The gap in the time-series is mainly due to the reprocessing campaign (on-going), consequently, the computed trends are clearly impacted over the first half-year of the data acquisition. In general, all the ratios are close to 1 and within ±5%, which attests a clear consistency between the six PICS.
Results and Analysis As mentioned above, this method considers the VNIR spectral range only  [29, 30]. Moreover, the gain coefficients are, in fact, relative calibration coefficients as MERIS is considered as a reference to compute the surface BRDF of the test site. After the ingestion of the whole datasets from S2A/MSI, the ratio Rb, defined as observed/simulated (ρobs/ρsim) reflectance, is then computed over the six desert sites (Table  11 ). Figure  32  shows time series of Rk from S2A/MSI bands B01, B03, B07 and B8A over the six PICS sites over the period June 2015-July 2016. The gap in the time-series is mainly due to the reprocessing campaign (on-going), consequently, the computed trends are clearly impacted over the first half-year of the data acquisition. In general, all the ratios are close to 1 and within ±5%, which attests a clear consistency between the six PICS.
Comparisons are done between similar spectral bands between two sensors. Then the ratio of TOA reflectance from the sensor under test to the one from the reference sensor is computed. The comparison is done over three desert sites, Algeria3, Libya4 and Railroad Valley Playa (Table  11 ). After the ingestion of L1C and L1T from MSI and OLI respectively into DIMITRI, the dataset is automatically cloud-screened and the following parameters are archived: the reflectances in all spectral bands, the Sun azimuth and zenith angles (SAA, SZA) and the viewing azimuth and zenith angles (VAA, VZA). These parameters are averaged over all pixels included in the considered site. To maximize the number of the match-ups (so-called doublets), are selected all acquisitions that satisfy the condition AMC <15, <45 and <55 for Algeria3, Libya4 and Railroad Valley Playa sites respectively and maximum time lag (time window) between sensing times of 11 days.
After the ingestion of L1C and L1T from MSI and OLI respectively into DIMITRI, the dataset is automatically cloud-screened and the following parameters are archived: the reflectances in all spectral bands, the Sun azimuth and zenith angles (SAA, SZA) and the viewing azimuth and zenith angles (VAA, VZA). These parameters are averaged over all pixels included in the considered site. To maximize the number of the match-ups (so-called doublets), are selected all acquisitions that satisfy the condition AMC <15, <45 and <55 for Algeria3, Libya4 and Railroad Valley Playa sites respectively and maximum time lag (time window) between sensing times of 11 days. The spectral response of the seven reference bands used for the intercomparison is depicted in Figure  34. Except bands B02 and B04, which display a shift of about 10 nm, all the considered bands show a rather comparable relative spectral response (RSR), particularly in term of band centre.
PICS Methodology Results As mentioned above, this method considers all time-series of both sensors over a predefined period. In other words, it is not restricted to the matchups only but it considers the VNIR spectral range only. Thus the gain coefficients are, in fact, relative calibration coefficients as MERIS is considered as a reference to compute the surface BRDF of the test site. After the ingestion of the whole datasets from S2A/MSI and LANDSAT-8/OLI, the ratio R b , defined as observed/simulated (ρ obs /ρ sim ) reflectance is then computed over the six desert sites. Figure  36  shows time series of R b from both S2A/MSI bands (B03, B04 and B8A) and LANDSAT-8/OLI bands (L03, L04 and L05) over Libya-1 site. Both sensors display nearly the same temporal evolution, scatter and variability of the resulting R b.
Except a slight bias of 2 or 3% in bands B01 over Algeria3 and Mauritania2 and B02 over Libya4, the results are consistent over the VNIR spectral range, and no significant discrepancy can be observed. Except for B05 from MSI, the whole ratio values are within ±5%. These results demonstrate the complementarity between MSI and OLI, and show that MSI products can provide data continuity for OLI in agreement with  [40]. Remote Sens. 2017, 9, 584 45 of 80 The green (resp. red) dashed lines represent the 5% (resp. 10%) threshold specification. Error bars indicate the estimated uncertainty.
PICS Methodology Results As mentioned above, this method considers all time-series of both sensors over a predefined period. In other words, it is not restricted to the matchups only but it considers the VNIR spectral range only. Thus the gain coefficients are, in fact, relative calibration coefficients as MERIS is considered as a reference to compute the surface BRDF of the test site. After the ingestion of the whole datasets from S2A/MSI and LANDSAT-8/OLI, the ratio Rb, defined as observed/simulated (ρobs/ρsim) reflectance is then computed over the six desert sites. Figure  36  shows time series of Rb from both S2A/MSI bands (B03, B04 and B8A) and LANDSAT-8/OLI bands (L03, L04 and L05) over Libya-1 site. Both sensors display nearly the same temporal evolution, scatter and variability of the resulting Rb.
Except a slight bias of 2 or 3% in bands B01 over Algeria3 and Mauritania2 and B02 over Libya4, the results are consistent over the VNIR spectral range, and no significant discrepancy can be observed. Except for B05 from MSI, the whole ratio values are within ±5%. These results demonstrate the complementarity between MSI and OLI, and show that MSI products can provide data continuity for OLI in agreement with  [40].