dataesr/scientific-paragraphs-categorization huggingface dataset:
PRJEB14816. Quality trimming of fastq files (average base quality of 30, aiming for 100-fold coverage) and de novo assembly using Velvet 1. 1. 04(55)wereperformedwithSeqSphere+(version 3. 1; Ridom GmbH) as described recently(56). Only genomes that harbored ≥95% cgMLST targets passed the quality control; otherwise, sequencing was repeated until the criterion was met. Identification of USA300-characteristic genes and their allelic variants was performed using a BLAST search with ≥95% nucleotide identity and ≥99% length coverage against all nucleotide sequences listed in Dataset S2. SCCmec types were determined by the BLAST-based detection of specific SCCmec typing primer sequences(57,58)and different combinations of cassette chromosome recombinases (ccr) gene complexes and the mec gene complex, according to the definitions of the International Working Group on the Staphylococcal Cassette Chromosome elements (IWG-SCC; www.
ACKNOWLEDGMENTS. We thank  Isabell Höfig ,  Ursula Keckevoet ,  Stefan Bletz , and  Elvira Chapka  for excellent technical assistance. Support of the  Münster Graduate School of Evolution (MGSE)  (L. S. ) is gratefully acknowledged. This study was supported by the  Deutsche Forschungsgemeinschaft  ( EI 247/8-1 ,  Me3205/4-1 ). Funders had no role in study design or interpretation of results.
sccmec. org)(Dataset S3). PVL-Phage Identification. All de novo assembled genomes positive for both PVL genes, i. e. , lukF-PV and lukS-PV (n = 120), were screened for the presence of any of the seven phages known to carry the PVL genes (59, 60): (i) SNP position (locus tag)* Gene product SNP Substitution 328599 (SAUSA300_RS01475) DUF5079 domain-containing protein G → A Glu 213 Lys 992391 (SAUSA300_RS04860) Hypothetical protein A → G Val 172 Ala 2232409 (SAUSA300_RS11400) Threonylcarbamoyl-AMP synthase C → T Ala 236 Thr 2648462 (SAUSA300_RS13595) MFS transporter G → A Pro 356 Leu 2779237 (SAUSA300_RS14285) Arginine-ornithine antiporter T → G Ile 30 Leu *SNP localizations, locus tags, and gene products were given with reference to the genome of strain USA300_FPR3757 (GenBank accession no. NC_007793).
The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρ w ) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs.
5 and 119 g•m -3. For SPM above 119 g•m -3 , ρ in the NIR is most sensitive to concentration changes. This implies that models based on the visible bands are not effective in discriminating SPM in highly turbid waters as demonstrated by previous studies  [18, 31]. The relationships between ρ (convoluted to OLI bands) and SPM concentration are presented in Figure  3. Analogous relationships were obtained for both MODIS and VIIRS convoluted bands, and are not shown here. Figure  3a  shows a linear relationship between ρ in the green band and SPM concentration lower than 10 g•m -3. Above 10 g•m -3 , a saturation of ρ is observed in this band. A green band relationship was not established for the Bourgneuf dataset, as low concentration measurements were not available, so the green band relationship obtained for the Gironde was also applied for Bourgneuf Bay.
Several empirical models using single bands and NIR-red band ratios were considered in this study. The semi-analytical model developed by  [16]  was also re-calibrated with in situ datasets. Table  A2  in the appendix shows all the algorithms tested. The performance of each model was assessed using the coefficient of determination (r 2 ) and the normalized root mean square error (NRMSE, in %) calculated as follows: % = ∑ , - , , - , × 100 (1) where xp and xobs are respectively the model-derived and field-measured SPM concentration, in g•m -3. In the case of the Gironde Estuary, the dataset (n = 67) was divided into two sets, one for calibration (n = 34) and one validation (n = 33). The NRMSE was computed for the SPM provided by the models with respect to the validation dataset. The best fits and minimum errors were obtained with the empirically-derived polynomial (second order) regression for the NIR band and linear regressions for the red and green bands (Table  2 ).
In the case of Bourgneuf Bay, the best fits were obtained for the NIR band and the semi-analytical equation developed by  [16]. Due to the low number of measurements, the Bourgneuf dataset was not separated into calibration and validation sets. Hence, the percent NRMSE (Equation (  1 )) in this case represents the deviation of the random component within the data. Table  2  summarizes the equations with the best fits for each area and for The relationships between ρ (convoluted to OLI bands) and SPM concentration are presented in Figure  3. Analogous relationships were obtained for both MODIS and VIIRS convoluted bands, and are not shown here. Figure  3a  shows a linear relationship between ρ in the green band and SPM concentration lower than 10 g•m -3. Above 10 g•m -3 , a saturation of ρ is observed in this band.
A green band relationship was not established for the Bourgneuf dataset, as low concentration measurements were not available, so the green band relationship obtained for the Gironde was also applied for Bourgneuf Bay. Water reflectance in the red band is highly sensitive to variations of SPM concentration lower than 50 g•m -3 and presents a good linear correlation (r 2 = 0. 89). Above 50 g•m -3 , a saturation of ρ is observed. The NIR band is less sensitive to SPM concentration below 50 g•m -3 , however it presents a very good fit above this limit by means of a polynomial regression (r 2 = 0. 97 see Table  2 ). Figure  3b  presents the sensitivity differences between the three bands (green, red, NIR) to SPM concentration for the ~0-50 g•m -3 range. There is a sharper increase in reflectance for the green band compared to the red for concentration below ~10 g•m -3 , and a sharper increase in red band reflectance compared to the NIR bands for concentration below 50 g•m -3.
This figure also shows the saturation of the green band above ~10 g•m -3 , so the r 2 was computed for the values below that concentration. Several empirical models using single bands and NIR-red band ratios were considered in this study. The semi-analytical model developed by  [16]  was also re-calibrated with in situ datasets. Table  A2  in the appendix shows all the algorithms tested. The performance of each model was assessed using the coefficient of determination (r 2 ) and the normalized root mean square error (NRMSE, in %) calculated as follows: NRMSE (%) = ∑ N i=1 (Xp,i -X obs, i ) 2 N X obs, max -X obs, min × 100 (1) where x p and x obs are respectively the model-derived and field-measured SPM concentration, in g•m -3. In the case of the Gironde Estuary, the dataset (n = 67) was divided into two sets, one for calibration (n = 34) and one validation (n = 33).
The NRMSE was computed for the SPM provided by the models with respect to the validation dataset. The best fits and minimum errors were obtained with the empirically-derived polynomial (second order) regression for the NIR band and linear regressions for the red and green bands (Table  2 ). In the case of Bourgneuf Bay, the best fits were obtained for the NIR band and the semi-analytical equation developed by  [16]. Due to the low number of measurements, the Bourgneuf dataset was not separated into calibration and validation sets. Hence, the percent NRMSE (Equation (1)) in this case represents the deviation of the random component within the data. Table  2  summarizes the equations with the best fits for each area and for different SPM ranges. These ranges were established by testing the extent to which the equations predicted accurately the actual value estimated in situ by gravimetry.
Corresponding results for NPP/VIIRS and AQUA/MODIS were calculated but not shown here. These can be requested from the authors; the dataset (n = 67) of the Gironde Estuary was divided into two sets, one for calibration (n = 34) and one for validation (n = 33); the fit and error were computed with respect to the validation set. The Bourgneuf dataset was not separated into calibration and validation sets, so the results represent the deviation of the random component within the data.
From the reflectance vs. SPM relationships (Figure  3 ), summary of the best SPM models for L8/OLI green, red and NIR reflectance bands, for the Gironde and Bourgneuf-Loire areas. The goodness of fit (r 2 ) and normalized relative root mean square error (NRMSE) are indicated. The most appropriate SPM model (or a combination of two models) is then selected using a radiometric switching criterion (Table  3 ). Corresponding results for NPP/VIIRS and AQUA/MODIS were calculated but not shown here. These can be requested from the authors; the dataset (n = 67) of the Gironde Estuary was divided into two sets, one for calibration (n = 34) and one for validation (n = 33); the fit and error were computed with respect to the validation set. The Bourgneuf dataset was not separated into calibration and validation sets, so the results represent the deviation of the random component within the data.
Satellite Data and Atmospheric Correction Satellite images from three sensors were used in this study: Landsat-8/OLI, MODIS, and VIIRS. Detailed information on the characteristics of the three sensors can be found in the literature  [19, [48] [49] [50]  (see also  Table 4). OLI imagery was initially used for the development of the multi-conditional algorithm due to its high spatial resolution and high quality of the radiometric data in visible, NIR and SWIR spectral bands. Orthorectified and terrain corrected Level 1T OLI data was downloaded from the Landsat-8 portal USGS portal (http://earthexplorer. usgs. gov/) then processed using the ACOLITE software (http://odnature. naturalsciences. be/remsem/acolite-forum/) [19, 51]  to derive water-leaving reflectance (hereinafter referred as ρ w = π × R rs ).
ACOLITE establishes a per-tile aerosol type (or epsilon) as the ratio between the Rayleigh corrected reflectance in the aerosol correction bands, for pixels where the marine reflectance can be assumed to be zero (where ρ w 655 < 0. 005, as defined by  [19]. The epsilon is then used to extrapolate the observed aerosol reflectance to the visible bands. ACOLITE also provides a choice for aerosol correction using a full tile fixed epsilon, a per pixel variable epsilon or a user defined epsilon. In this study, the first option was selected for the atmospheric correction, as it has been shown to provide good results in highly turbid coastal waters  [52]. This software proposes two atmospheric correction (AC) options: the NIR algorithm  [51]  based on the MUMM approach  [23]  and using the red (655 nm) and NIR (865 nm) bands, and the SWIR algorithm  [19]  using the SWIR bands 6 (1609 nm) and 7 (2201 nm).
The maximum differences were observed in the green band (NRMSE = ~7%). The MACCS algorithm is based on land pixels and estimates the aerosol optical thickness combining a multi-spectral assumption, linking the surface reflectance in the red and blue wavebands, and the assumption that multi-temporal observations of a given area should yield similar surface reflectance when separated by a few days. However, this method is not able to estimate the aerosol model and uses a constant model for a given site, which is a disadvantage for regions where the aerosol model is subject to large spatial variations, such as coastal regions. Instead, the ACOLITE AC method estimates the aerosol type using SWIR bands in clear water pixels for each image. In this study, the aerosol type was assumed to be constant over a single OLI tile (170 × 185 km 2 ), but there is an option provided by the ACOLITE software to allow the type to vary spatially.
However, this method is not able to estimate the aerosol model and uses a constant model for a given site, which is a disadvantage for regions where the aerosol model is subject to large spatial variations, such as coastal regions. Instead, the ACOLITE AC method estimates the aerosol type using SWIR bands in clear water pixels for each image. In this study, the aerosol type was assumed to be constant over a single OLI tile (170 × 185 km 2 ), but there is an option provided by the ACOLITE software to allow the type to vary spatially. Research  [19]  demonstrated that products from OLI compare well with those of MODIS Aqua and Terra, using the SWIR bands corrected atmospherically with ACOLITE. For this reason, and since the ACOLITE AC uses an ocean color approach, this method is considered more appropriate for the coastal waters of the study regions, even though the MACCS method is proved to provide satisfactory results over the most turbid waters.
Validation of Atmospheric Correction Figure  7a  shows the band-to-band ρ value scatter-plots derived from four OLI satellite images of the Gironde Estuary (a and b) and for the Bourgneuf-Loire area (c and d). The same band-to-band ρ value scatter-plots derived from in situ measurements are superimposed. In the case of the Gironde area, the in situ values accurately match the OLI-derived reflectance values, and the logarithmic regressions for both datasets follow the same trend. In the case of the Bourgneuf-Loire area, the trend divergence between in situ and satellite datasets is more significant. This is mainly due to the dispersion observed on the satellite dataset caused by the presence of clouds (Figure  7 ) on the image acquired on 12 April 2013. The ACOLITE software provides a good mask for clouds, but in some cases cloud shadows or cloud edges are insufficiently masked, introducing significant scatter.
Nevertheless, there is a fair overlap between in situ and satellite data for this area as well, taking into account the dataset number difference (in situ stations n = 29 vs. satellite pixels n = ~20,000) and the effect of cloud pixels from 12 April 2013. This image was selected because it was acquired on the last day of the field survey carried out in this area, in April 2013. Remote Sens. 2017, 9, 61 16 of 31 model in this area, while it has been demonstrated that the maritime model is dominant in the Gironde area  [58]. The aerosols of maritime origin are almost non-absorbent, so an overestimation of aerosol absorbance is expected when applying a continental model, especially in offshore waters where there is no land aerosol influence. The reason for the green band differences observed is unclear; it could be due to an overestimation caused by the MACCS AC, or an underestimation by the ACOLITE AC caused by a green band overcorrection.
Nevertheless, there is a fair overlap between in situ and satellite data for this area as well, taking into account the dataset number difference (in situ stations n = 29 vs. satellite pixels n = ~20,000) and the effect of cloud pixels from 12 April 2013. This image was selected because it was acquired on the last day of the field survey carried out in this area, in April 2013. Similar ρ band-to-band comparisons were conducted between VIIRS-derived images, and in situ-measured water reflectance at bands centered at 551, 671 and 862 nm (Figure  8 ). A fair match is obtained between field and satellite datasets for both the Gironde and the Bourgneuf-Loire areas. As can be observed, the in situ (black line) and satellite (red line) regression curves overlap. This demonstrates that the atmospheric correction applied to the VIIRS images, using the SWIR bands and the Seadas (version 7.
3), is appropriate for this type of environment. Remote Sens. 2017, 9, 61 17 of 31 Similar ρ band-to-band comparisons were conducted between VIIRS-derived images, and in situ-measured water reflectance at bands centered at 551, 671 and 862 nm (Figure  8 ). A fair match is obtained between field and satellite datasets for both the Gironde and the Bourgneuf-Loire areas. As can be observed, the in situ (black line) and satellite (red line) regression curves overlap. This demonstrates that the atmospheric correction applied to the VIIRS images, using the SWIR bands and the Seadas (version 7. 3), is appropriate for this type of environment. Figure  9  presents a comparison between in situ-measured and satellite-derived reflectance spectra corresponding to VIIRS and MODIS Aqua images acquired on 12 June 2012, 15 July 2014, 16 July 2014.
This is caused by the use of the MODIS band B5 (1240 nm), which causes an overcorrection in the shorter wavelength band. The comparison between in situ and satellite data products resulted in a good match between the green-red and red-NIR bands, with some discrepancies. In general, the trends observed for the satellite data were lower in the higher reflectance values. This is due to the difference in the amount of data (in situ stations n = 29 vs. satellite pixels n = ~20,000) and to cloud shadow and land effects on coastal pixels. Regarding the OLI red-green bands' (655 vs. 561 nm) comparison, a break was observed between the 0-0. 15 and the 0. 15-0. 2 intervals. If the fit would have been made for values of ρ 655 <0. 08, the red and black curves in Figures  7  and 8  would have had a better correspondence for the lower reflectance values, so better results would have been obtained if the comparison was achieved by intervals (e.
g. , 0-0. 08, 0. 8-0. 2). However, showing the entire data range provides a better understanding of the type of data obtained in situ and from satellite remote sensing measurements, and since the in situ data matches the satellite data, the atmospheric correction was In general, satellite products appear to underestimate the 'true', i. e. , field-measured, water reflectance. Valid match-ups with MODIS and VIIRS were not obtained on the same day. The comparison between in situ and satellite data products resulted in a good match between the green-red and red-NIR bands, with some discrepancies. In general, the trends observed for the satellite data were lower in the higher reflectance values. This is due to the difference in the amount of data (in situ stations n = 29 vs. satellite pixels n = ~20,000) and to cloud shadow and land effects on coastal pixels.
satellite) would not affect the switching bound values selected. considered to provide realistic reflectance results. The switching bounds were determined using field data, so a better match between the two types of data (in situ vs. satellite) would not affect the switching bound values selected. Differences observed between in situ and satellite data, in Figures  9  and 10 , could be due to several reasons: (1) an overcorrection of the atmospheric contribution in the SWIR method; (2) the spatial difference between the satellite pixel and field measurements (250/750 m 2 pixel versus ~1 m 2 ); (3) the near-shore location of the station in the estuary. Option 3 appears to be the most plausible, as the pixel selected for the comparison did not correspond exactly to the location of the Pauillac field station: the next pixel away from the shore was selected to avoid the land effect.
However, there is an increasing interest in the exploitation of VIIRS products for coastal studies as this satellite sensor is considered to provide continuity to MODIS. In this study, the VIIRS performance was assessed in highly turbid coastal waters; it showed good results in coastal waters, but the bands' spatial resolution was too coarse to be used inside the estuary. Nevertheless, the results presented here are promising, as this sensor is still being calibrated, its performance is being tested  [59]  and methods are currently being developed to use the high-resolution bands in coastal waters  [60]. The water reflectance values derived from satellite data were slightly lower than the values measured in situ. This is mainly due to the difference in sampling size, as OLI images have a resolution of 30 m, while the water volume sampled in situ was much lower (~10 L).
Research  [57]  showed good agreement between OLI and in situ spectra in low-to high-turbidity waters. This study confirms their findings in study areas with different SPM characteristics with respect to the North Sea  [21]. In summary, the SWIR atmospheric correction appears to be the most appropriate for the selected study areas, including low-to high-turbidity waters (Figure  5a ). ACOLITE software corrections provided satisfactory water reflectance values for OLI images, in good agreement with MACCS reflectance products (Figure  6a ), as already highlighted in previous studies (e. g. ,  [42] ). In relation to VIIRS, there is a good correspondence between in situ and satellite-derived water reflectance for both study areas (Figures  8  and 9 , Table  7 ), although additional in situ-satellite match-ups would be necessary to draw further conclusions.
Different SPM models including band ratios, such as those developed by  [18, 42]  for these areas, were applied to the images, but the match-up results did not improve (see Table  8 ). Figure  14  compares the SPM concentrations derived from OLI, VIIRS and MODIS (Aqua) satellite data recorded on the same day over the Gironde Estuary. Water reflectances at 655 (OLI), 671 (VIIRS), 645 nm (MODIS aqua) show a similar trend along the transect. VIIRS products provided higher values than OLI in general, up to the most upstream section of the Estuary, where there was a sharp decrease. It corresponded to a failure of the atmospheric correction, due to the low VIIRS spatial resolution for this particular area. This failure was also observed along the MODIS transect, where the most upstream pixels are missing, resulting in a sudden SPM concentration decrease.
Similar methods have already been developed  [35, 36] , but the procedure presented in the present study  (1)  automatically selects the model switching bounds based on in situ measurements; (2) fully applies the method to real satellite data provided by three different sensors; and (3) validates the results based on match-ups with field data. Table  8. Slope, adjusted r 2 , percent NRMSE (%) and offset resulting from the match-ups between in situ measurements from the MAGEST (Gironde) and SYVEL (Loire) network stations and the OLI-derived SPM concentration obtained using the different empirical and semi-empirical models shown in Tables  2  and A2.
The selected models for each SPM range and each band were chosen based on goodness-of-fit tests (r 2 ) and NRMSE (%) results from the validation exercises. In the case of the Gironde estuary, the best-performing models were a single-band, second-order polynomial relationship for the NIR band (r 2 = 0. 97; NRMSE = 9. 11%) and a linear relationship for the red (r 2 = 0. 81; NRMSE = 7. 23%) and green bands (r 2 = 0. 81; NRMSE = 16. 41%). In the case of the Bourgneuf-Loire area, the best-performing model was the semi-empirical relationship published by  [16] , re-calibrated with the in situ dataset, for both the NIR (r 2 = 0. 93, NRMSE = 7. 81%) and red bands (r 2 = 0. 82, NRMSE = 18. 22%). The bounds for switching between models were based on water reflectance values derived from the saturation points of the most sensitive bands.