dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2001). The Landsat archive was reprocessed into a tiered data Collection structure in 2017. In this study, Landsat-8 Collection 1 Level 1 Precision and Terrain (L1TP) corrected data were used. The Collection 1 data provide a number of new metadata, including angle coefficients that may be used to derive per-pixel solar and viewing geometry, and also provide per-pixel cloud, cloud shadow, cirrus cloud and snow masks derived from the CFmask algorithm  (Foga et al. , 2017).
This is because Sentinel-2A has a shorter revisit cycle and a wider field of view and also because different orbits of Sentinel-2A data laterally overlap at these latitudes every 3 and 7 days whereas different orbits of Landsat-8 data laterally overlap every 7 and 9 days. To ensure that a representative range of reflectance spectra were considered, all the available Landsat-8 OLI Collection 1 L1TP and Sentinel-2A L1C data over the study area for two summer (December and January) and two winter (June and July) months, 2016 were considered. A total of 209 and 255 Landsat-8 L1TP images and 1545 and 1687 Sentinel-2A L1C tile acquisitions were available in the summer and winter months respectively. There were less summer data because of greater cloud cover that reduced the number of Landsat images processed to L1TP and because in a summer month, i.
A total of 485 spectra were used: 224 vegetation, 40 soil, 147 man-made materials, and 74 dry vegetation spectra. The spectra are extracted from three spectral libraries: the Version 6 USGS spectral library  (Clark et al. , 2007) , the Version 2 ASTER spectral library  (Baldridge et al. , 2009) , and the Jasper Ridge spectral library  (ENVI, 2013). The simulated normalized difference vegetation index (NDVI) was derived as the simulated NIR reflectance minus the simulated red reflectance divided by their sum. There are two Sentinel-2A MSI NIR bands and throughout the paper the Sentinel-2A NDVI was always derived using the narrower MSI NIR band 8A (855-875 nm) as it is more comparable to the Landsat-8 OLI NIR band 5 (851-879 nm) (Table  1 , Fig. 1 ). 3. 2. Sentinel-2A and Landsat-8 data pre-processing 3.
2. 1. Top of atmosphere reflectance and geometry derivation The Landsat-8 Collection 1 L1TP digital numbers for each band were converted to top of atmosphere (TOA) reflectance using the scaling factors stored in the metadata and dividing by the cosine of the solar zenith angle. Landsat per-pixel solar and viewing angles were calculated using the Landsat Angles Creation Tool provided by USGS (https://landsat. usgs. gov/solar-illumination-and-sensor-viewing-anglecoefficient-file) and the angle coefficient file available with each Collection 1 L1TP file. The Sentinel-2A L1C tile data store the TOA reflectance, and also the solar and view geometry every 5 km. The Sentinel-2A solar and view zenith angles were bilinear resampled to provide angles for each Sentinel-2A pixel location.
Surface reflectance computation The Sentinel-2A and Landsat-8 sensor TOA reflectance data were corrected to surface reflectance using the same algorithm to reduce biases that may occur if different algorithms were used. The LaSRC (Land Surface Reflectance Code) algorithm V3. 5. 5  (Vermote et al. , 2016) , that has been adapted for Sentinel-2A application, was used in this study. The LaSRC has higher accuracy than the heritage LEDAPS algorithm  (Masek et al. , 2006)  when applied to Landsat-8 OLI data  (Vermote et al. , 2016). It is based on the established 6S radiative transfer code  (Vermote et al. , 1997)  with, in particular, an improved aerosol determination based on a red to blue reflectance ratio and a linear function of a spectral index with slope and intercept terms derived from a spatially explicit climatology of MODIS and MISR data.
, 2012). In this study, any 20 m pixels labeled by SEN2COR as saturated, medium or high confidence cloud, cloud shadow, snow, or cirrus were discarded. The Landsat-8 Collection 1 L1TP product includes a per-pixel bitpacked quality band  (Foga et al. , 2017). Pixels labeled as medium or high confidence cloud, cloud shadow, cirrus, or snow were all discarded. Landsat-8 reflective wavelength data are much less saturated compared to previous Landsat's  (Roy et al. , 2016b)  but if any of the six Landsat-8 bands (Table  1 ) were labeled as saturated then the pixel observation was discarded. The saturation status was derived by comparison of the stored digital numbers and metadata saturation values. Medium resolution satellite data may have pronounced adjacency effects whereby atmospheric scattering from adjacent pixel locations may contribute non-negligible scattered radiation into the sensed pixel  (Ouaidrari and Vermote, 1999; Liang et al.
, 2016c) , and also projected into the Landsat-8 Collection 1 data. Each orbit of satellite data was reprojected and stored independently so no temporal compositing was undertaken. The affine transformations defined by the least-squares registration characterization were injected into the sinusoidal to UTM coordinate transformation so that the satellite data were not resampled twice. The Sentinel-2A 20 m and the Landsat-8 30 m band data were resampled by bilinear resampling. The Sentinel-2A 10 m bands were resampled using a boxcar resampler, whereby the four corners of each sinusoidal projection 30 m pixel were projected into the Sentinel-2A UTM projection and all the 10 m Sentinel-2A pixels intersecting the projected pixel were identified and their average reflectance used as the resampled value.
(  1 ). In addition, the root mean square difference between the Sentinel-2A and Landsat-8 simulated data was derived as:   (Vermote et al. , 2016)  and the data were adjusted to NBAR using the c-factor method  (Roy et al. , 2016a (Roy et al. , , 2017)). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) = ∑ - RMSD v v n ( ) i n i OLI i MSI 2 (2) where RMSD is the root mean square difference between corresponding Landsat-8 OLI (v i OLI ) and Sentinel-2A MSI (v i MSI ) simulated reflectance or NDVI values derived as Eq. (  1 ) for spectra i, and there are n = 485 spectra.
Sentinel-2A MSI and Landsat-8 OLI spectral reflectance and NDVI sensor comparison To generate data for the analysis, pixels were sampled systematically every five 30 m pixels in the row and column directions across each Sentinel-2A and Landsat-8 5295 × 5295 30 m pixel tile for each orbit of reprojected data. Only pairs of Sentinel-2A and Landsat-8 pixel observations that were both valid, i. e. , not cloudy, shadow, or cirrus contaminated (Section 3. 2. 4) were compared. Only pairs of Sentinel-2A and Landsat-8 pixel observations acquired within one-day of each other were considered to minimize the impact of surface changes between the MSI and OLI sensor observations. In addition, following the approach used to quantify spectral relationships between Landsat-7 Enhanced Thematic Plus and Landsat-8 OLI data sensed one day apart  (Roy et al.
, 2014b)  and so these observations were removed from the surface reflectance and surface NBAR comparisons. - + > ρ ρ ρ ρ | | 0. 5 | | 1. 0 blue TOA MSI blue TOA OLI blue TOA MSI blue TOA OLI , , , , (3) Comparisons of the TOA reflectance, surface reflectance, surface NBAR, and derived NDVI were undertaken. For the NDVI comparisons, only NDVI values falling in the range zero to unity were retained to reflect typical land surface NDVI values. The correspondence between the comparable Sentinel-2A MSI and Landsat-8 OLI sensor band data were examined in the same way as for the simulated data, i. e. , by RMA regression. In addition, to provide other overall summary difference measures that capture the directionality of any differences, the mean difference and the mean relative difference between the Landsat-8 and Sentinel-2A data were derived as: ∑ = - Δ v v n i n i OLI i MSI (4) = ∑ * - + ( ) Δ n 100 i n v v v v ( ) 0.
5( ) i OLI i MSI i OLI i MSI (5) where Δ and * Δ are the mean difference and the mean relative dif- ference respectively between corresponding Landsat-8 OLI (v i OLI ) and Sentinel-2A MSI (v i MSI ) values (TOA reflectance, surface spectral reflectance, surface NBAR, or derived NDVI) for n pairs of sensor observations. Statistical functions to transform between the Sentinel-2A MSI and Landsat-8 OLI data (TOA reflectance, surface spectral reflectance, surface NBAR, and derived NDVI) were developed using ordinary least squares (OLS) regression. The goodness of fit of the OLS regressions were defined by the coefficient of determination (r 2 ) and the significance of the OLS regressions was defined by examination of the regression overall F-statistic p-value.
0145). This is expected and is because these two bands have very different bandwidths -the OLI band 5 NIR spectral response function intersects with only 21. 2% of the MSI NIR band 8 response function (Fig. 1 ). The RMA slope for this pair of bands is 1. 0177, i. e. , in general the simulated MSI NIR broad band reflectance is smaller than the simulated Landsat-8 OLI NIR reflectance. When the MSI NIR narrow band 8A (855-875 nm) is compared with the Landsat-8 OLI NIR band 5 the difference is less pronounced (RMSD = 0. 0003) and the RMA slope is very close to unity (0. 9998). The simulated NDVI data have an RMA slope close to unity (1. 0015). However, the NDVI RMSD is not negligible (0. 0151). The NDVI is designed to emphasize the difference between red and NIR reflectance to differentiate between vegetation and soil.
Data sampling Over the study area there were 464 Landsat-8 Collection 1 L1TP images and 3232 Sentinel-2A L1C tile acquisitions available in the summer (December and January) and the winter (June and July) 2016. From these, 206 Landsat-8 OLI images and 707 Sentinel-2A L1C tile acquisitions were acquired within one day of each other. From these, pairs of MSI and OLI 30 m pixel observations were extracted across the tiles (Fig. 1 ) every five 30 m pixels. This sampling was necessary to reduce the data volume, and even so, a total of 116,164,813 pairs of MSI and OLI 30 m pixel observations were extracted. After filtering to remove saturated, cloud, cloud shadow, cirrus and snow contaminated pixels 65,380,881 pairs remained. The blue TOA reflectance filter (Eq. (  3 )) removed a very small number of pairs (29) and consequently 65,380,852 pixels were used for the TOA analyses.
3 ) but also atmospheric contamination, calibration, and bi-directional reflectance differences. These are examined and discussed in the following sections. The solid lines in Fig. 4  show the RMA regressions derived without intercept and so are directly comparable to the simulated RMA regression results illustrated in Fig. 3. As for the simulated data, the RMA TOA reflectance slopes are in general less than unity, i. e. , the MSI TOA reflectance is greater than the OLI TOA reflectance, but with slopes that are further from unity likely due to the confounding factors noted above. The exceptions are the blue and broad band NIR TOA reflectance data. The blue band (MSI band 2 and OLI band 2) data have an RMA slope of 1. 0221 (compared to 0. 9823 for the simulated data) and is the shortest wavelength band considered and so is more susceptible to atmospheric effects present in the TOA data than the longer wavelength bands.
Discussion and conclusion The Sentinel-2 MSI and Landsat-8 OLI sensors have similar characteristics and their data together have the potential to support global coverage, medium spatial resolution, near daily time series science and applications. Combined use of different sensor data can be undertaken in a number of different ways, including sensor data fusion into a single new data set, or by transforming the data from one sensor so that they are more similar to the other sensor's data. This paper focused on the latter approach. There are a number of pre-processing issues that need to be addressed before the well calibrated Landsat-8 and Sentinel-2 data  (Markham et al. , 2014; Mishra et al. , 2016; Gascon et al. , 2017)  can be used together or treated as effectively being sensed from the same sensor.
The Sentinel-2 MSI and Landsat-8 OLI data have quite different sensor spectral response functions and some bands that are not common, including the MSI red-edge bands  (Drusch et al. , 2012)  that were not considered in this study as they are not acquired by the OLI  (Irons et al. , 2012). The Sentinel-2 MSI has 10 m and 20 m bands and there are six approximately spectrally equivalent Landsat-8 OLI 30 m bands. To handle this spatial resolution difference, the Sentinel-2 10 m and 20 m data could be resampled to Landsat-8 30 m resolution, or the Landsat-8 data could be downscaled to Sentinel-2 resolution  (Li et al. , 2017b). In this study, the MSI and OLI sensor data were registered precisely  (Yan et al. , 2018)  and reprojected to 30 m in the same equal area projection. This was necessary because the MSI and OLI sensor data are defined in different tiling schemes with different map projections (UTM zones) and, at the time of writing, are not well registered for direct comparison without additional registration  (Yan et al.
The RMA regression of the MSI broad band NIR (785-900 nm) and the OLI NIR (851-879 nm) indicated that the MSI reflectance was generally smaller than the OLI reflectance and this was attributed to the very different spectral band passes. The simulated NDVI data RMA regressions indicated similar sensor data. The 65 million pairs of Sentinel-2A MSI and Landsat-8 OLI TOA reflectance values considered in this study likely had differences due not only to sensor spectral response function differences, but also to atmospheric contamination, calibration, and sensing geometry (i. e. , bidirectional reflectance) differences. The TOA RMA regression slopes were generally similar to those for the simulated data but with slopes further from unity because of these factors. Compared to the simulated data, the blue band RMA slope changed from below unity to above unity, and the broad band NIR and the NDVI RMA slopes increased further above unity.
Further work is required to examine whether they will also be applicable to the recently launched Sentinel-2B MSI data. Please note that the v3. 5. 5 LaSRC atmospheric correction software used Version 2. 0 (released before December 19th 2017) Sentinel-2A spectral response functions (Version 3. 0 is illustrated in Fig. 1 ) and the L1C Sentinel-2A TOA reflectance data were also processed by ESA using Version 2. 0. Unfortunately, this version of the spectral response functions was found recently to have anomalies that affect primarily the MSI blue and broad NIR bands (MSI bands 1, 2 and 8) but with negligible impact on the L1C Sentinel-2A TOA reflectance  (Clerc and MPC Team, 2018). Thus, although the Sentinel-2A TOA and surface reflectance considered in this study were retrieved with consistent spectral response functions, biases with respect to the Landsat-OLI equivalent wavelength bands may be present.
Quality was evaluated using the RNA Integrity Number (RIN) value  [23]. First strand cDNA was then synthesized from 1 ng of total RNA, primed with oligo(dT)20 and random primers (1:1, v/v) using the Superscript III reverse transcriptase (Invitrogen, Life Technologies) according to the manufacturer's instructions. Single-stranded cDNAs thus obtained were quantified using the Agilent BioAnalyzer 2100, and then stored at -20 °C. To determine the expression of a set of selected genes, primers were designed using the Primer Express ™ Software v3 (Applied Biosystems, ThermoFisher Scientific) using gene sequences (Ccl5, Il1α and MIF), from a caprine database (Capra hircus, ID 10731, NCBI), or using primers previously designed  [24]  (Table  2 ). All qPCR amplification systems were first validated as described  [24].
qPCR was performed using the Power SYBR ® Green Master Mix (Life Technologies, Ther-moFisher Scientific) and a QuantStudio ™ 12K Flex Real-Time PCR System (Applied Biosystems, ThermoFisher Scientific). Each qPCR well contained 10 µL Master mix, 5 µL cDNA and 1. 2 µL of each primer at 5 µM (300 nM final) and nuclease-free water for a final volume of 20 µL. Reactions were run in triplicate. The results generated by the QuantStudio ™ 12K Flex Software v1. 2. 2 (Applied Biosystems, ThermoFisher Scientific) were then exported as ". eds" files and analyzed with the Standard Curve version 3. 4 (ThermoFisher Cloud apps). Relative expression levels were calculated using the software qBase  [25] , using the reference genes RPS24 and PPIA.
Acknowledgements Many thanks to  Pierre Sarradin  and  Edouard Guitton , directors of  Experimental Infectiology Platform (Nouzilly, France ), and to the zootechnical staff of the PFIE, and particularly to  Maud Renouard  for expert and dedicated help with the goat experiment. Many thanks also to the  dedicated zootechnical staff of the experimental farm of Bourges  ( UE 0332 ,  La Sapinière , 18390 Osmoy, France) and to its director  Frédéric Bouvier.
Specifically, we ask what the extent of putative species boundaries within grass-associated Abacarus complex is and whether there is a host-associated genetic differentiation within the complex. We reveal high cryptic diversity within Abacarus spp. and discuss their host associations. We also proceed the story by describing the new species associated with smooth brome, on the basis of standard morphological characters and DNA barcode data (mtDNA COI and 28S rDNA D2) obtained from the type material of the new species.
Trace files were checked and edited using MEGA6  (Tamura et al. 2013). Newly generated 59 mitochondrial Abacarus COI sequences were combined with 23 previously published in NCBI GenBank database (till August 2017) COI sequences of grass-associated Abacarus  (Skoracka and Dabert 2010; Szydło et al. 2015; Xue et al. 2017)  and outgroup Trisetacus silvestris Castagnoli data (GenBank Acc. No. KC776553. 1; Lewandowski et al. 2014 ) for analysis (Table  1 ). Sequences were aligned using the ClustalW algorithm  (Higgins et al. 1996)  implemented in MEGA6, and the final datasets contained 82 Abacarus COI sequences (603 bp) of mites from 18 identified grass species, one unknown grass species and five countries (Table  1 ). Alignment reliability and product authenticity of COI sequences was validated by translating aligned DNA sequences into amino acids and assessing the alignments for premature stop codons.
For COI dataset, neighbor-joining (NJ) tree with 1000 bootstrap replicates was constructed using the Kimura 2-parameter model  (Kimura 1980). Additionally, maximum likelihood (ML) analysis was done using GTR + G + I model as selected by Modeltest v. 2. 1. 3 (Guindon and Gascuel 2003; Darriba et al. 2012 ) according to the Akaike and Bayesian information criteria (AIC, BIC). The D2 sequence of the new species was aligned with corresponding sequences from the study by  Skoracka and Dabert (2010)  that fell into to the clade investigated as a putative species of Abacarus complex from smooth brome, and with Abacarus longilobus Skoracka, a sister species of the new species. These sequences were downloaded from Genbank (FJ392672, FJ392673, FJ392674, KC776553, respectively). Pairwise mean genetic distances between clades were calculated for COI and D2 datasets with the Kimura 2-parameter model (K2P)  (Kimura 1980).
Mean K2P genetic distances within clades were calculated for COI dataset, and standard error estimates were obtained by a bootstrap procedure (1000 replicates). All above analyses were done in MEGA6. To confirm the presence of Abacarus species we applied the combination of the Poisson Tree Processes model for species delimitation (PTP) and a Bayesian implementation of PTP (bPTP). This approach allows to infer putative species boundaries on a given phylogenetic input tree  (Zhang et al. 2013 ). The analysis was done with bPTP server (bPTP server: a Bayesian implementation of the PTP model for species delimitation, http:// speci es. h-its. org/ptp/). The input tree for bPTP model was obtained by Bayesian inference approach with codon partitioning. The Hasegawa-Kishino-Yano  (Hasegawa et al. 1985)  substitution model with proportion of invariable sites (I)  (Shoemaker and Fitch 1989)  and gamma distributed rate of variation among sites (G)  (Yang 1994 ) (HKY + I + G) was applied to the first and second codon positions in sequences, and General Time Reversible model  (Tavaré 1986 ) with I and G (GTR + I + G) was applied to the third position of each codon.
The analysis was run with MrBayes v. 3. 2 software  (Ronquist et al. 2012)  as two parallel runs with four chains (three heated and one cold chain), with G and I estimated during the analysis for each model. The analysis was run for 560,000 generations, until the average standard deviation of split frequencies value for runs was less than 0. 01. When the convergence was reached, the analysis was stopped, 25% of first trees were discarded from results, and consensus tree with posterior probabilities was generated. The Monte Carlo sampler for the PTP model was applied to the tree with following parameters: 200,000 generations; thinning: 100; burnin: 0. 1 and seed: 123.
Morphological analyses Mites were studied with a phase-contrast microscope Olympus BX41. All specimens were taxonomically identified on the basis on apparent morphological characteristics as belonging to Abacarus genus complex  (Sukhareva 1985; Nalepa 1896; Skoracka et al. 2002). Specimens of a newly described species were measured. Morphological nomenclature follows  Lindquist (1996) , data measurements follow  Amrine and Manson (1996) , and systematic classification follows  Amrine et al. (2003). Measurements are given in micrometers. Measurements refer to the lengths of each structure unless otherwise stated. Positions of leg setae were measured from the proximal margin of the seta-bearing segment. Locations of ventral setae c2, d, e, and f on ventral annuli were measured from the posterior margin of coxae II.
Genetic differentiation within the Abacarus complex The final COI dataset consisted of 83 aligned sequences which represented 82 ingroup populations of grass-associated Abacarus mites (Table  1 ), and one outgroup. The 78 sequences of Abacarus formed 10 highly supported clades (bootstrap value: 99%) and four remaining sequences represented distinct genetic lineages (Fig. 1 ). Relationships between lineages were not fully resolved. The genetic distances between lineages ranged from 6. 6 to 26. 5% (Table  2 ). The PTP/bPTP analyses' results were consistent with NJ structuring and identified 14 lineages as putative species (Fig. 2 ). The NJ analysis of partial COI gene confirmed the monophyly of four already described species within the complex: A. lolii, A. longilobus, A. hystrix and A. acutatus.
1 ). Abacarus acutatus was associated predominantly with plants from Calamagrostis genus, and one population was found on Echinochloa crus-galli (L. ) P. Beauv. Abacarus hystrix group (both Abacarus hystrix 1 and Abacarus hystrix 2) was the most polyphagous taxon, found on quackgrass, wall barley, wheat and triticale. It was the only lineage associated with cereals. Abacarus sp. 1 was represented by the sequence from a mite population collected from an unknown host plant in China, which had been identified by the authors as A. hystrix (GenBank Acc. No. KM111087). However, it is highly divergent from other Abacarus sequences, including A. hystrix sensu lato (> 21%). Abacarus sp. 2, found on Bromus hordeaceus (soft brome), also represented a divergent lineage (> 20%). The new Abacarus species (plumiger) collected from Bromus inermis (smooth brome) was a sister clade (99% bootstrap support) to A.