dataesr/scientific-paragraphs-categorization huggingface dataset:
A multiplatform assessment of the Ocean-Atmosphere Spectral Irradiance Model (OASIM) radiative model focussed on the Mediterranean Sea for the period 2004-2017 is presented. The BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE) mooring and biogeochemical Argo (BGC-Argo) float optical sensor observations are combined with model outputs to analyse the spatial and temporal variabilities in the downward planar irradiance at the ocean-atmosphere interface. The correlations between the data and model are always higher than 0. 6. With the exception of downward photosynthetic active radiation and the 670 nm channel, correlation values are always higher than 0. 8 and, when removing the inter-daily variability, they are higher than 0. 9. At the scale of the BOUS-SOLE sampling (15 min temporal resolution), the root mean square difference oscillates at approximately 30 %-40 % of the averaged model output and is reduced to approximately 10 % when the variability between days is filtered out.
, 2014). In perspective, the evaluation of the uncertainty of the multispectral light at the ocean-atmosphere interface is important both for the solution of the radiative transfer model within the water column and for the development of assimilation schemes of radiometric parameters. In this framework, the Mediterranean Sea appears to be a key area for the development of a new multispectral biooptical model to be potentially integrated with the European Copernicus Marine Environment Monitoring Service (CMEMS). A dense network of BGC-Argo floats providing quality-controlled radiometric data has indeed been deployed in the Mediterranean Sea  (Organelli et al. , 2016 (Organelli et al. , , 2017)) , which can be combined with the high-frequency radiometric and bio-optical observations acquired by a dedicated fixed platform (BOUée pour l'acquiSition d'une Série Optique à Long termE -BOUSSOLE;  Antoine et al.
This activity has been carried out within the framework of the CMEMS Service Evolution project BIOPTIMOD, which is aimed at the development of a new multispectral bio-optical model that will include the integration of MedBFM with data provided by both BGC-Argo floats and multispectral satellite sensors (e. g. the Ocean and Land Colour Instrument, OLCI, on board Sentinel3-A and Sentinel3-B;  Donlon et al. , 2012). In MedBFM, the computation of the downward irradiance at the sea surface will be updated with OASIM, which has been pre-operationally interfaced with European Cen- tre for Medium-Range Weather Forecast (ECMWF) atmospheric products and validated against reference data in the Mediterranean Sea, provided by the BOUSSOLE buoy and BGC-Argo floats. This work also contributes to the extension of the validation of OASIM in the Mediterranean Sea, an area not covered by the previous skill assessment of  Gregg and Casey (2009).
In the original formulation of OASIM presented in GC2009, cov and LWP data are retrieved from the International Satellite Cloud Climatology Project (ISCCP, from June 1986 to July 2002, and the prior climatology), while r e is parameterized using MODIS data normalized with mean values obtained from the literature (please refer to GC2009 for details). Specific modelling of the spectral reflectance of sea foam, affecting the transmittance across the air-sea interface, is also included in OASIM (please refer to the Appendix of GC2009). In addition to the cloud properties necessary for the Slingo model (cloud cover and cloud liquid water path), OASIM requires the following atmospheric input data: surface pressure, sp [mb]; wind speed, ws [m s -1 ]; relative humidity, rh [%]; precipitable water (absorption by water vapour), wv [cm]; and ozone, oz  [DU].
Except for ozone, which was obtained from the multiyear dataset of Total Ozone Mapping Spectrometer (TOMS) sensors (from 1979 to May 1993, hereafter referred to as the TOMS climatology), in GC2009, the other four parameters (sp, ws, rh and wv) were acquired from the National Centers for Environmental Prediction  (NCEP 1979 -July 2002; Kalnay et al. , 1996)  reanalysis dataset. In the present implementation, most of these input data are extracted from the ECMWF ERA-Interim dataset (please refer to Sect. 2. 4). The spatial and temporal resolutions of OASIM are determined by the forcing datasets (i. e. aerosol optical data, cloud property data and atmospheric surface level data). The standard configuration presented in GC2009 is hereby maintained, with a 1 • horizontal resolution, thereby increasing the temporal frequency to 15 min to resolve the diurnal variability and compare the model output to the temporal resolution of the in situ data considered in the present study.
e. from 400 to 700 nm. Quality-controlled Chl, T and S NRT data are currently available from the Coriolis data centre (Argo Data Management Team, ADMT). The photosynthetically active radiation (PAR) must be generally derived by spectrally integrating the scalar irradiance (i. e. the radiance integrated across the complete solid angle) because phytoplankton utilizes light from all directions. In the present case, BGC-Argo sensors measure the downward irradiance integrated in the 400 to 700 nm range: to avoid confusion, the measured quantity is referred to as DPAR. QC of radiometric data (DPAR and E d ), specifically designed for in situ and remote-sensing ocean colour applications, has been implemented by  Organelli et al. (2016). The delayed-mode (DM) QC approach to identify data corruption by biofouling and instrument drift based on tests and procehttps://doi.
org/10. 5194/os-17-675-2021 Ocean Sci. , 17, 675-697, 2021 dures has been developed by  Organelli et al. (2017). In the present work, a BGC-Argo dataset was adopted covering the period between 2012 and 2017 with 3800 profiles (Fig. 2 ). Before comparing model values to observations, the irradiance profiles obtained from floats were extrapolated to the surface with an exponential fitting procedure based on the curve_fit tool of the python package scipy. Further, we required profiles to have at least one measurement in the first 1. 5 m depth from sea surface and to have at least four measurements in the first 10 m. In addition, any sub-basin (as defined in Fig. 2 ) and month containing fewer than five profiles was discarded.
OASIM input data for Mediterranean Sea applications The input data required by OASIM in the present Mediterranean Sea application are shown in Fig. 3 , together with the datasets used for the validation (BOUSSOLE and BGC-Argo). In the setup specific to the Mediterranean Sea, developed within the framework of the BIOPTIMOD project, the variables of the cloud and atmospheric properties are extracted from the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset  (Dee et al. , 2011) ; further details on their pre-processing are provided in Appendix B. The investigated period ranges from 2004 to 2017, with the input data provided as daily averages and with a spatial resolution of 1 •. The use of ERA-Interim to force OASIM is motivated by increasing the coherence between the input cloud properties and surface level atmospheric properties, which, in the GC2009 configuration, were provided by two different datasets (ISCCP and NCEP, respectively).
OASIM validation in the Mediterranean Sea Data analysis covered the time period shown in Fig. 3 , using datasets from BOUSSOLE (the downward planar irradiance E d (0 -) at  412. 5, 442. 5, 490, 510, 555, 560, 665, 670 and 681. 25  nm, as well as DPAR) and BGC-Argo floats (the downward planar irradiance E d (0 -) at 380, 412 and 490 nm and DPAR). The notation 0 -indicates quantities just below the sea surface with any reflections at the air-sea interface removed, and 0 + is applied for the radiances just above the air-sea interface (the light measured before air-sea transmission occurs). The OASIM outputs for the irradiance are expressed in watts per quare metres per waveband, while the in situ data for the same parameter are expressed in watts per square centimetre per nanometre. Therefore, before comparison, the units were standardized to watts per square metre per nanometre.
ERA-Interim and MODIS atmospheric data analysis A comparison of the ERA-Interim monthly averaged surface level atmospheric variables at grid points corresponding to the BOUSSOLE mooring buoy and the available observations (sea level pressure, wind speed and relative humidity) by the nearby Côte d'Azur buoy (Météo-France) for the period from 2004-2012 is shown in Fig. 4. An overall good agreement is observed for the surface pressure between the ECMWF data and in situ observations, while ERA-Interim underestimates the observed relative humidity  (D'Ortenzio et al. , 2008). The wind magnitude provided by the Côte d'Azur dataset is much larger than that of the ERA-Interim coarse data at 1 • , as previously reported by  Stopa and Cheung (2014)  using data from the US National Data Buoy Center. Large differences occur more frequently during the cold season (from November to April).
Validation of OASIM with the BGC-Argo float network The quality-controlled BGC-Argo float dataset adopted in this work contains radiometric measurements acquired from 10:00 to 14:00 local time. To compare OASIM with these BGC-Argo measurements, a point-by-point match-up analysis was performed, where the closest model output to the float measurement at the surface was selected. The individual match-ups were then spatiotemporally aggregated based on the climatological months and the defined 16 sub-basins, as shown in Fig. 2 , at each of the three wavelengths and for DPAR separately  (Figs. 8 to 11). At 380 nm, the mean values of the model outputs are overall higher than the observations. Apart from the seasonal cycle, a west-east gradient is also observed, with high values in the Eastern Mediterranean, most likely due to the low cloud cover (Fig.
Discussion An extensive comparison of OASIM's temporal and spatial variabilities was performed using the BOUSSOLE and BGC-Argo optical sensor data as references. The results indicated that, in general, the model reproduced the variability in the spectral downward irradiance in the Mediterranean Sea, which depends on the spatiotemporal scale. In particular, considering the OASIM applications within biogeochemical models equipped with a multispectral in-water optical module, the impact will be different according to the specific scale under investigation. In terms of fine temporal scales, it was observed that a large part of the discrepancy expressed by the RMSD occurred due to the day-to-day variability, similar to the findings reported by  Somayajula et al. (2018). When this temporal scale is filtered out, the RMSD decreases by half or more.
Nonetheless, IND at 412 nm is lower than all the other wavelengths, and this could explain, at least in part, the lower skill observed at 412 nm. Table  1. Summary of the model skill compared to the available data from the BOUSSOLE buoy (from 2004 to 2012) and BGC-Argo floats (from 2012 to 2017) for the irradiance (E d ) at the different wavelengths (WLs) and for DPAR. RMSD, bias and y-int are expressed in watts per square metre per nanometre, and in regard to DPAR, the same statistical indicators are expressed in µmol quanta m -2 s -1 , while all the other indicators (regression r and slope) are dimensionless, where N is the number of match-ups between the model and observations. For the BOUSSOLE comparison, the numbers in bold font are derived by filtering out the day-to-day variability (i.
org/10. 5194/os-17-675-2021 Ocean Sci. , 17, 675-697, 2021 between the band centred at 400 nm and the one centred at 425 nm. In addition to the seasonal indicators discussed above, the interannual trends of E d (λ, 0 -) and DPAR were investigated both for the measured data and model results. Given the strong seasonal cycle present in all the properties considered, low-pass filters (i. e. moving averages) were applied to the data before the regression. The results for both datasets were biased by the gaps in the acquisitions, which introduced spurious trends. Therefore, the focus was on the model inputs instead (ECMWF products and MODIS aerosol data) and the outputs corresponding to a 14-year gap-free time series both in spatial and temporal terms. The model outputs averaged over the Mediterranean Basin indicated a low interannual variability, or at least the procedures adopted based on the low-pass filtering and sub- sequent regression could not identify clear trends (image not shown).
Conclusions We assessed the performance of the OASIM radiative model on the Mediterranean Sea for the period 2004-2017, comparing the model outputs with multiplatform reference data. The BOUSSOLE mooring station provides a dataset with a high temporal resolution at a fixed point, while the BGC-Argo floats allow the partial resolution of the spatial vari-Ocean Sci. , 17, 675-697, 2021 https://doi. org/10. 5194/os-17-675-2021 ability (certain regions contain no floats), although with a lower spectral resolution than that of BOUSSOLE. The atmospheric multispectral input data provided by OASIM are necessary to resolve the multispectral propagation of light within the water column. Evaluating the uncertainties and the quality of the these input data is fundamental for all the future applications involving bio-optical modelling and constitutes an important starting point to develop assimilation schemes based on bio-optical modelling.
The results indicate an overall good agreement between the model outputs and in situ references, highlighting a clear seasonal variability in the model capabilities, with a generally high performance during summer. The analysis conveys that an important source of discrepancy between the model and data is the intra-monthly variability, which can be ascribed to cloud cover dynamics (the RMSD is reduced with a low cloud cover). The coarse resolution of cloud cover likely affects the model skill at both https://doi. org/10. 5194/os-17-675-2021 Ocean Sci. , 17, 675-697, 2021 seasonal and intra-monthly timescales. This is shown by the improvement in model skill at BOUSSOLE when variability between days is filtered out. Nonetheless, the analysis of the relative contribution of E dir λ, 0 -and E dif λ, 0 -indicates that the model skill is correlated to their ratio, suggesting that improving the physical description of the radiative processes should be considered.
Acknowledgements. This work was performed within the framework of the  BIOPTIMOD CMEMS Service Evolution  project. CMEMS is implemented by Mercator Ocean International within the framework of a delegation agreement with the  European Union. The authors acknowledge  Météo-France  for supplying the data and the HyMeX database teams (ESPRI/IPSL and  SE-DOO/Observatoire Midi-Pyrénées)  for their help in accessing the data. BOUSSOLE is funded by the  European Space Agency (ESA) , contract  4000119096/17/I-BG , and by the  Centre National d'Etudes Spatiales (CNES)  with support from the  French Oceanographic Fleet  (https://doi. org/10. 18142/1). We acknowledge the  ECMWF  for the  ERA-Interim  dataset (source: https://www. ecmwf. int,last access: 12 November 2020). The authors thank  Valentina Mosetti  ( National Institute of Oceanography and Applied Geophysics -OGS ) for figure editing.
Financial support. This work was supported by the  BIOPTIMOD CMEMS Service Evolution  project. CMEMS is implemented by Mercator Ocean International within the framework of a delegation agreement with the  European Union. This study was supported by the following research projects funding  BGC-Argo  floats: NAOS (funded by the  Agence Nationale de la Recherche  in the framework of the  French "Equipement d'avenir" programme , grant agreement  ANR   J11R107-F ), remOcean (funded by the  European Research Council , grant agreement  246777 ),  Argo-Italy  (funded by the  Italian Ministry of Education, University and Research)  and the  French Bio-Argo programme (Bio-Argo France ; funded by  CNES-TOSCA ,  LEFE Cyber  and GMMC). Review statement. This paper was edited by  Piers Chapman  and reviewed by  Mark Baird  and two anonymous referees.
Code availability. The OASIM Fortran code is publicly downloadable, together with the reference input data, from http://gmao. gsfc. nasa. gov/research/oceanbiology (Gregg, 2020). Data availability. Data used in the present study are publicly available from the following sources: -ECMWF (Simmon et al. , 2007; https://doi. org/10. 21957/ POCNEX23C6); -BOUSSOLE mooring  (Antoine and Vellucci, 2020 ; http:// www. obs-vlfr. fr/Boussole/html/project/boussole. php); -Mistral site  (Bouin and Emzivat, 2020 ; https://mistrals. sedoo. fr/HyMeX/); -BGC-Argo  (Argo, 2020 ; https://doi. org/10. 17882/42182# 76230).
Introduction SARS-CoV-2 is the third human coronavirus (CoV) responsible for severe respiratory syndrome that emerged in the last 20 years, the two previous ones being SARS-CoV in 2002  (Drosten et al. 2003)  and MERS-CoV in 2012  (Zaki et al. 2012). SARS-CoV-2, which causes the COVID-19 disease in humans, spread in early 2019 leding to a pandemie. By January 20, 2021, more than 97 million infections had been reported with at least 1. 4 million deaths. The etiological agent of COVID-19 was rapidly identified at the beginning of the pandemic, and by January 26, 2020, 10 viral genomes had been sequenced  (Lu et al. 2020). Sequence comparisons revealed a 99. 98% pairwise identity between those genomes, which is characteristic of a recent emergence. When the first SARS-CoV-2 isolates were sequenced, the closest coronaviruses available in databases were bat-SL-CoVZXC21 and bat-SL-CoVZC45 strains, isolated in 2015 and 2017 from bats in the Zhoushan region of eastern China, and whose genomes showed 88% identity with SARS-CoV-2  (Lu et al.
SARS-CoV-2: From Yunnan to Wuhan? The origin of SARS-CoV-2 is a matter of debate. Bioinformatic studies revealed that it has a 96. 2% identity with a CoV genome (RaTG13) reconstructed from feces and anal samples of Rhinolophus affinis bats. Interestingly, these samples were collected in 2013, but the full-genome sequence was only published in early February 2020  (Zhou et al. 2020a). Unfortunately, the precise location of the sample collection is documented neither in the original article nor in the sequence databases. However, when the current analysis was led (April 2020), we found an exact match between RaTG13 and a 370 nucleotide fragment published in 2016 (KP876546), encoding a BtCoV/4991 polymerase domain, which had been sequenced from isolates collected from a mine shaft in Yunnan Province following the death of 3 miners from an atypical pneumonia  (Ge et al.
2015). In addition, these new exotic farms come alongside all intensive farming of domestic animals (poultry, pigs, etc. ), which also creates reservoirs of viruses (influenza, etc. ) in areas with high human density  (Gibbs et al. 2009). It should be kept in mind that the reliability of the results depends on the quality of sequencing, metagenomic reconstructions, public accessibility of the data and the accuracy of the annotations in sequence databases  (Hassanin 2020). The insertion between the S1 and S2 subunits of the S protein created a furin-sensitive proteolytic cleavage site which appears to contribute to its infectivity and/or epidemic propensity in humans. This insertion must be recent since it is absent from all the close relatives of SARS-CoV-2. This observation is crucial as this site probably played a key role in the species barrier crossing and/or in the efficiency of human-to-human transmission, which is a prerequisite for the emergence of epidemics.
Reproducibility of the analyses All the analyses to produce the results and figures of this article follow the FAIR principles (Findable, Accessible, Interoperable and Reusable). The software environment, sequence data, commented code and examples can be downloaded as a github repository (https ://githu b. com/ jvanh eld/SARS-CoV-2_origi ns), and the main results can be browsed on the github web pages (https ://jvanh eld. githu b. io/SARS-CoV-2_origi ns/). The software environment is fully described in a yaml-formatted conda configuration file, enabling to re-run all the analyses on Linux or Mac OS X operating systems. The release of the code corresponding to this article is available on zenodo (https ://zenod o. org/recor d/39315 05). A few sequences could, however, not be made available in the github repository because they were downloaded from the GISAID server, which does not allow to redistribute the data and metadata.
Since these sequences were crucial to reproduce some key elements of the current debate about SARS-CoV-2 origins, we incorporated them in our analyses. These sequences can be found on the GISAID server (https ://www. gisai d. org/) with the IDs EPI_ISL_412977 (Bat virus metagenome RmYN02), EPI_ISL_410544 (Pangolin virus Gu-P2S_2019) and EPI_ISL_410721 (Pangolin virus genome Gu1_2019), respectively.
Sequences Viral sequence genomes were collected from the NCBI Nucleotide database (https ://www. ncbi. nlm. nih. gov/nucco re/). A workbook with the identifiers and descriptions of the sequences is included in the github and zenodo releases. S gene sequences were extracted based on the annotation of their coordinates in the NCBI annotations. S protein sequences were obtained from the NCBI protein database (https ://www. ncbi. nlm. nih. gov/prote in/).
PIP profiles Profiles of Percent Identical Proteins were computed with an original R script available on the github repository, which enables to draw PIP profiles for either nucleic or peptidic sequences. For genomic PIP profiles, each viral sequence of interest ("around-cov-2" or "selected" collections) was aligned onto 1 3 the reference genome (SARS-CoV-2) using the Needleman-Wunsch global pairwise alignment algorithm. PIP profiles for coding sequences were based on translation-based multiple alignments of the nucleic sequences with the R function DECIPHER::AlignTranslation(). The PIP was measured on the resulting aligned nucleic sequences, as well as on the aligned protein sequences (not shown in this article).
Structural analyses of the spike protein A model of the full SARS-CoV-2 spike protein was built by aligning the sequence of SARS-CoV-2 spike protein on the PDB 6acc. 1. A model of the full SARS-CoV spike protein (the most complete model of a mature coronavirus spike trimmer available to date) using the SWISS-MODEL online tool. Structural analyses were conducted using the pymol software and the scripts available on https ://githu b. com/ jvanh eld/SARS-CoV-2_origi ns/tree/maste r/scrip ts/pymol. The 6m0j model and the model we built were aligned, and the insertions identified by running the script https ://jvanh eld. githu b. io/SARS-CoV-2_origi ns/scrip ts/pytho n/detec tion_inser tion. py on the alignment of all sarbecovirus spike sequences (https ://jvanh eld. githu b. io/SARS-CoV-2_origi ns/resul ts/spike _prote in/muscl e_align ments /selec ted_coron aviru s_spike _prote ins_align ed_muscl e.
Acknowledgements We would like to thank the following colleagues for their careful revision of the early drafts of the French manuscript and their many suggestions for its improvement:  Cathy Bellan ,  Mathias Bonal ,  Bruno Canard ,  Bruno Coutard ,  Hélène Chiapello ,  Denis Gerlier ,  Catherine Nguyen ,  Nadia Rabah ,  Annick Stevens ,  Denis Thieffry.
To confirm these observations, we examined the replication timing in human cells depleted for REV3L. For that, HeLa cells were transfected with non-targeting siRNA (siNT) or siRNA against Rev3l. Sixty hours later, cells were pulse-labeled with BrdU for 1. 5 h and processed as for MEFs. Analysis by the START-R program revealed 116 disturbed regions in REV3L-depleted cells, corresponding to approximatively 5. 4% of the whole genome with more than 70% of delayed regions  (Fig EV2C and D). We found that depletion of REV3L in human cells affected mostly genomic domains that replicate late in S-phase  (Fig EV2E). Thus, the specific replicating domains disturbed after REV3L depletion are not exactly the same in MEFs and HeLa genomes (TTR versus Late, respectively).