dataesr/scientific-paragraphs-categorization huggingface dataset:
Anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH; Abcam, Cambridge, UK) was used as a loading control. Chemiluminescence of secondary Ab-HRP conjugates was elicited using Luminata Crescendo Western HRP Substrate (Merck Millipore, Burlington, Massachusetts), imaged with Gel Doc XR+ (Bio-Rad), and quantified using Image Lab software (Bio-Rad). Liver homogenates (5 mg total protein/lane) were loaded onto 10% Mini-Protean TGX gels (Bio-Rad) and separated with Tris/glycine/ SDS electrophoresis buffer (Bio-Rad). Trans-Blot Turbo Transfer Starter System (Bio-Rad) was used to transfer the proteins onto Immun-Blot low-fluorescence PVDF membranes (Bio-Rad). The membranes were then blocked with 1% TBS-T and 3% BSA for 1 h. HMBS was probed with 1:2,000 monoclonal mouse anti-HMBS (H-11; Santa Cruz Biotechnology), together with 1:1,000 rabbit anti-actin (Sigma-Aldrich), in 1% TBS-T, 3% BSA overnight, 4 C.
Derivatization of C6 A similarity search of C6 was performed over the MolPort libraries (https://www. molport. com/shop/index) of commercially available compounds. 66 The libraries were downloaded from the ZINC database, and Tanimoto coefficients  38  to the query compounds were calculated for the 2D molecular similarity using OpenBabel and the ChemInf packages. 39 All compounds showing a Tanimoto coefficient of more than 70% to the query compounds were collected and clustered based on their pairwise similarity.
Protein-Ligand Docking Identification of pockets on the surface of HMBS and possible binding sites were probed with fpocket 41 using the structure with PDB: 3ECR  55  or 5M6R  42  as input. Docking of C6 and C6-3 to the identified pockets was performed with Glide (Schrödinger, New York, NY)  67  using default settings for ligand and protein preparation, grid generation, and docking.
ACKNOWLEDGMENTS We are very grateful to  Ming Ying  for expert help with immunoblot and immunoquantifications. The authors thank the K. G. Jebsen Foundation ,  Western Norwegian Regional Health Authorities  (grant  912246 ),  Norwegian Research Council  for grants  FRIMEDBIO 261826/F20 ,  BIOTEK2021 285295 ,  Norwegian NMR Platform ,  NNP 226244/F50 , and infrastructure project  NOR-OPENSCREEN (245922/F50 ).
The RDP naive Bayesian classifier (54) and the SILVA SSU database release 132  (55)  were used to assign taxonomy to each merged sequence, referred to here as operational taxonomic units (OTUs), at 100% similarity. The Shannon diversity index, the number of OTUs per sample, and the pairwise Bray-Curtis dissimilarities were calculated using the R packages vegan v. 2. 5-2 (56) and phyloseq v. 1. 24. 0  (57). Twelve extraction control samples were also included to assess potential contamination during DNA extraction and sequencing. Any OTUs that were identified as predominant in the negative controls were removed prior to analysis. Estimation of Lactobacillus abundance from NS using qPCR. Because of the semiselective nature of MRS plates, accurate counts of Lactobacillus spp. could not be achieved.
) containing the PCR product from a Lactobacillus strain (L. paracasei 57A). A melt curve analysis was performed following amplification for all qPCRs to ensure that only target genes were amplified. Statistical analysis. The mean M. haemolytica counts for each animal were obtained by averaging CFU from the two NS collected per animal (right and left nasal cavities). Bacterial counts, rectal temperatures, and respiratory rates were compared between treatment groups (BT ϩ Mh and Mh). The M. haemolytica counts were natural log transformed prior to statistical analysis. All data were analyzed as a randomized complete block design with repeated measurements using a linear mixed-effects model. This model accounted for the repeated measurements and also adjusted for day of sampling and M. haemolytica counts before challenge (i.
The relative abundance of specific taxa from the NS and TTA samples and the qPCR data, as well as serum cytokine concentrations, were analyzed using the GLIMMIX procedure in SAS (SAS 9. 4; SAS Institute Inc. , Cary, NC). The individual, block, treatment, and time were included in the CLASS statement. The models were "generalized" due to the specification of residual distributions that were not Gaussian normal. Models were "mixed" due to the inclusion of fixed effects (treatment-nested-in-time and time) and random effects (block and individual). Variance heterogeneity was modeled using a "RANDOM_RESIDUAL_/GROUP ϭ Treatment*Time" statement. Residual distributions and covariance structures were selected for each genus based on the model fit statistics, i. e. , Bayesian information criterion (BIC).
Therefore, path analysis was based on the matrix Spearman rank-based correlation coefficients for each subset that were produced using SAS PROC CORR with the SPEARMAN option (SAS 9. 4). In the initial model, time and the relative abundance of Lactobacillus spp. (the BT inoculation) were exogenous (predictor or independent) variables. In path diagrams, single-headed arrows that represent causal paths point from but never point to exogenous variables. The sources of variability in the exogenous variables are not included in the model. The initial model hypothesized that the experimental variables (time and Lactobacillus) predicted variability in the relative abundance of the studied genera (which were therefore hypothesized to be endogenous or criterion variables). As such, the initial model was entered into SAS PROC CALIS:.
ACKNOWLEDGMENTS This work was financially supported by  Alberta Agriculture and Forestry and Agriculture and Agri-Food Canada. S. A. was the recipient of a  Canadian Natural Science and Engineering Research Council (NSERC)   Doctoral Scholarship. We thank  Pamela Caffyn  and  Leandra Schneider  at the  Lethbridge Research and Development Centre ,  Agriculture Agri-Food Canada , for their excellent technical support and  Katherine Gzyl  for help with the processing of 16S rRNA gene sequences. We also express our appreciation to the animal care staff at the  Veterinary Sciences Research Station  and to the pathologists at  Diagnostic Services Unit, College of Veterinary Medicine, University of Calgary.
15% bile salts (Fluka, St Louis, MO, USA) and was incubated at 37 C for 24 h  [9]. Suspected colonies (up to five) were subcultured onto MacConkey plates. Species identification was performed using an API 20E identification system (bioMérieux, Marcy-l'Étoile, France) and was confirmed using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) (microflex TM ; Bruker Daltonik GmbH, Bremen, Germany) with the MALDI Biotyper software package database version 4. 2. 90 (Bruker Daltonik GmbH). Identification was made on whole cells and a score of !2. 000 was required for an accurate species-level identification as recommended by the manufacturer.
Genome sequencing One K. pneumoniae strain (CF21) was further characterised by WGS using Ion Proton TM technology (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. Quality check of raw reads was done using FastQC v. 0. 11. 5 and MultiQC v. 0. 9, and no trimming was performed. Reads are available under Sequence Read Archive (SRA) accession no. PRJNA604085. Mean coverage of 40Â was determined after alignment against the K. pneumoniae subsp. pneumoniae HS11286 reference genome (GenBank CP003200. 1). Reads were then assembled using Mimicking Intelligent Read Assembly (MIRA) software. Contigs were analysed using Geneious software (Biomatters Ltd. , Auckland, New Zealand). Average nucleotide identity (ANI) was estimated using ANI calculator from Kostas lab (http://enve-omics.
ce. gatech. edu/ani) using K. pneumoniae HS11286 reference strain and contigs: 99. 85% identity was obtained. Antimicrobial resistance genes were identified using ResFinder 3. 2 (https://cge. cbs. dtu. dk/services/ResFinder/). Genes with a 60% minimum length and a percentage of identity >98% were considered. Plasmid replicon types were determined using the PlasmidFinder 2. 1 database (https://cge. cbs. dtu. dk/services/Plas-midFinder/). Sequence types (STs) were determined using the Klebsiella multilocus sequence typing (MLST) database (https:// cge. cbs. dtu. dk/services/MLST/). Mutations in the quinolone resistance-determining regions (QRDRs) of gyrA and parC were analysed.
Mutations were detected in GyrA (Ser83Phe; Asp87Ala) and ParC (Ser80Ile) topoisomerase proteins that are responsible for resistance to quinolones. MLST analysis assigned this isolate to the international clone ST11. Plasmid analysis showed that K. pneumoniae CF21 possesses five different plasmids including IncA/C2, IncFIA(HI1), IncFIB(K), IncFII(K) and ColRNAI, with high identity to the previously described pNDM-KN (GenBank JN157804), pTR2 (GenBank KJ187752), pKPN-IT (GenBank JN233704), pKPN3 (GenBank CP000648] and pIGMS32 (GenBank DQ298019), respectively (Table  2 ).
Acknowledgments The authors would like to thank  Mr Lahelal Abane  ( Béjaïa forest conservation, Algeria ) for his kind help. The authors also thank  Dr Valentin Tilloy  ( UF9481   Bioinformatique ,  CHU Limoges, Limoges, France ) for his kind help in the bioinformatics analysis and the  'Direction Générale de la Recherche Scientifique et du développement technologique (DGRSDT)' of the Algerian Ministry of Higher Education and Scientific Research.
Culture Conditions Seminavis robusta strain 85A was obtained from the diatom culture collection (BCCM/DCG) of the Belgian Coordinated Collection of Micro-organisms 1 , accession number (DCG 0105). Diatom cultures were grown at 20 • C in semi-continuous batch culture in 1. 8 L glass Fernbach flasks (Schott) under a day/night rhythm of 16/8 h with a light intensity of 20 µmol photons m - 2  s -1. Cells were cultured in Provasoli's enriched f/2 seawater medium  (Guillard, 1975)  using Tropic Marin artificial sea salt (34. 5 g L -1 ) enriched with NaHCO 3 (80 mg L -1 final concentration). Cultures were acclimated to these culturing conditions for at least 2 weeks.
Identification of LHCX Genes in the S. robusta Draft Genome LHCX sequences from T. pseudonana, P. tricornutum, F. cylindrus and Pseudo-nitzschia multiseries (with kind permission of E. V. Armbrust) were obtained from the JGI database 2. These LHCX sequences were used to build an amino-acid HMM (Hidden Markov Model) profile using HMMer (version 3. 1b1;  Mistry et al. , 2013) , which was used to search all genes (annotation version 1. 0) predicted for the As most LHCX3 sequences are rather similar in the primer regions, the primer LHCX3 picks up multiple related transcripts. The LHCX6 primer was designed on the SrLHCX6 RNA trailer and denoted with an "*". in-house draft genome of the D6 strain of S. robusta (DCG 0489). Only hits having an E-value ≤ e -50 were retained. This way, 17 S. robusta LHCX (SrLHCX) genes were found.
GenBank accession numbers are provided (Table  1 ). Similarly, LHCX/LHCSR were retrieved from Thalassiosira oceanica, Cyclotella cryptica, Fistulifera solaris, Synedra acus, Pseudonitzschia multistriata, the moss Physcomitrella patens and the brown alga Ectocarpus siliculosus. A multiple alignment was made using MAFFT L-INSi (version 7. 187;  Katoh and Standley, 2013)  and visualized using ESPript  (Robert and Gouet, 2014 ; Supplementary Figure  S1 ; coding sequences are provided in Supplementary Figure  S2 ). The signal peptide of each LHCX (between 17 AA and 22 AA) was found using ASAFind  (Gruber et al. , 2015)  after running SignalP 3. 0  (Bendtsen et al. , 2004). It was removed to calculate the approximate molecular weight (Mw) using the Compute pI/Mw tool (ExPASy, Swiss Bioinformatics Institute).
RT-qPCR RT-qPCR was performed with a Light Cycler R 480II (ROCHE). Primer sets were designed using Primer3 (Supplementary Table  S1 ). Primer specificity was tested in silico with FastPCR (PrimerDigital). Single nucleotide polymorphisms (SNPs) between the whole genome sequenced strain (D6) and the strain used in the experiments (85A) were identified using in-house RNAseq data  (Bilcke et al. , 2020)  using Integrative Genomics Viewer (IGV, Broad Institute) and did not affect primer specificity. CDKA1, V4 and V1  (Moeys et al. , 2016)  were used for normalization as these were most stably expressed (Qbase + software). Qbase + normalized data is shown in Supplementary Figure  S3. Log 2 expression ratios were compared with REST2013 software. The RT-qPCR program contained the following steps: pre-incubation: 95.
, 2009; Bailleul et al. , 2010; Lepetit et al. , 2013 Lepetit et al. , , 2017;; Taddei et al. , 2018). As all SrLHCX transcripts (except SrLHCX6 for which data are lacking) were induced upon a dark/light transition, we cannot rule out that multiple SrLHCX proteins fulfill a similar role as PtLHCX1. Linking transcriptional data and immuno-blotting, in this case, is not straightforward as the used antibody (anti-LHCSR3,  Bonente et al. , 2011)  was not specifically designed to recognize diatom LHCX isoforms. In addition, the large number of LHCX genes of similar sizes (Table  1 ) and differences in actual and predicted LHCX/LHCSR protein size, because of posttranslational modifications  (Bonente et al. , 2011) , complicate the comparison of both datasets as was also observed for the discrepancies in transcriptional and translational regulation of LHCXs in P.
Gene ID, GenBank accession number, Name, calculated molecular weight (Mw) and primer specificity. Name colours match the clade colours used in Figure1. Primer specificity was tested using FastPCR in silico PCR with default settings. Green shading represents an amplified PCR product, whereas orange represents the possibility of an amplified gene product, however, with one of both primers having a melting temperature 40 • C < t m < 50 • C when binding on the corresponding LHCX transcript.
ACKNOWLEDGMENTS We would like to thank  E. V. Armbrust  ( University of Washington, United States ) for granting us access to the Pseudonitzschia multiseries genome. A preliminary version of this manuscript was part of the doctoral thesis "Photoprotection in intertidal benthic diatoms" of LB (Blommaert, 2017), with promotors KS and WV and published by  Ghent University.
FUNDING The authors would like to thank the  Research Foundation Flanders  ( FWO  project  G. 0222. 09N ),  Ghent University  ( BOF-GOA 01G01911  and  BOF-GOA 01G01715 ) and the  Egide/Campus France-PHC Tournesol  (n128992UA) exchange program for their financial support. JL thanks the  CNRS  for their financial support. MH acknowledges a Postdoctoral Fellowship of the  Research Foundation Flanders. AB and PW were supported by grant  267474  from the  Norwegian Research Council.
The EURYBIA field segment comprises off-shore and on-shore infrastructures to manage the observation system and perform routine sensors calibrations. The ground segment includes the telemetry center for data communication and the processing center to compute data products and uncertainty budgets. The study shows that the overall uncertainty of EURYBIA SVC gains computed for the Sentinel-3 OLCI mission under EUMETSAT protocols is of about 0. 05% in the blue-green wavelengths after a decade of measurements, similar to that of the reference site in Hawaii and in compliance with requirements for climate studies.
Introduction The European Copernicus Program for Earth monitoring (http://www. copernicus. eu/) aims to deliver remote sensing capabilities, field measurements, and data processing services that provide users with reliable and up-to-date information related to environmental, climate, and security issues. As part of the Copernicus space component, the European Commission is developing, operating, and planning a series of Sentinel missions for the next 15 years, together with ESA and EUMETSAT. Among these missions, the Sentinel-3 Ocean and Land Colour Instrument (S3 OLCI) is a key component for monitoring the biogeochemical status of the ocean. OLCI is a primary source of ocean color (OC) data for the Copernicus Marine Environment Monitoring Service (CMEMS). For instance, OLCI data are operationally used to advance our understanding of living marine environments and assimilated in the CMEMS models to forecast the evolution of marine ecosystems, together with the other OC third party missions.
The Sentinel-2 mission, although mainly developed for land applications, already demonstrates a great potential for coastal applications like sediment and chlorophyll retrieval (e. g. , reference  [1] ), and it is expected to become an operational complement of the CMEMS next phases. OC is also an important dataset for Copernicus Climate Change Service (C3S) to monitor the impact of climate change on marine ecosystems and the ocean carbon cycle  [2]. OC science is making an increasing contribution to Ocean Observing systems thanks to continuous global monitoring of biogeochemical variables, to the point that OC missions have become fundamental to the success of the Copernicus Marine and Climate services. This has also raised expectations on the quality of data products. The current threshold requirement for calibration uncertainty of Copernicus satellite OC sensors is 0.
None of these systems is, however, fully compliant with the operational requirements of the Copernicus Programme. The creation of an OC-SVC infrastructure dedicated to the Copernicus Programme is a key point to improve the performance of OC missions that meet the long-term and high-quality standards underpinning marine bio-geochemistry data products delivered by Climate and Marine Services  [5]. EUMETSAT has identified a series of steps to achieve this goal. The first step accomplished in 2017 defined the requirements for the Copernicus OC-SVC Infrastructure  [3]. These requirements represent the baseline for the present second step: preliminary design of the Copernicus OC-SVC Infrastructure. The overall objective of this paper is to contribute to this European effort by designing the OC-SVC infrastructure able to provide highly accurate, state-of-the-art Fiducial Reference Measurements (FRM) in the very tight development schedule (36 months from the start development) requested by Copernicus Programme.
Section 2 describes the core EURYBIA optical system, the characterization and calibration procedures, as well as acquisition protocol. The optical buoy and the mooring buoy for the deployment of the optical system and ancillary equipment are presented in Section 3. Solutions featured by the Ground Segment for data telemetry, processing, quality control, dissemination, and the scheduling of maintenance operations are discussed in Section 4. Regarding the location of the site, previous studies  [4, [11] [12] [13]  investigated the suitability of a set of candidate regions using, as reference metrics, the number of valid matchups resulting from a set of single threshold criteria applied to a restricted set of variables. With respect to the previous studies, the OC-SCV requirements  [3]  encompass a larger number of variables and corresponding constraints taken into account by this paper.
This environment assessment includes oceanographic and atmospheric properties and adjacency effect of the proposed selected sites and is summarized in Section 5. Section 6 summarizes the suitability of the site by developing a model to estimate the uncertainty on the vicarious calibration gains and by applying it to OLCI observations  [14]  and to the results of the characterization study. Final conclusions are addressed in Section 7. Based on OC-SVC requirements, this paper is structured as follows. Section 2 describes the core EURYBIA optical system, the characterization and calibration procedures, as well as acquisition protocol. The optical buoy and the mooring buoy for the deployment of the optical system and ancillary equipment are presented in Section 3. Solutions featured by the Ground Segment for data telemetry, processing, quality control, dissemination, and the scheduling of maintenance operations are discussed in Section 4.
Bathymetry Two regions with bottom depth larger than 300 m at 35. 5248 • N-12. 7667 • E and 35. 7430 • N-12. 3579 • E, LMP1 and LMP2 respectively, have been identified as possible sites for OC-SVC Infrastructure (Figure  5 ). These two sites are characterized by a negligible impact from fishing activities and ship traffic and are located at a reasonable distance from Lampedusa (less than 20 km), thus enabling technical operations and allowing for timely interventions, while avoiding any possible adjacency effect (see Section 5. 4) and possible influence from emissions occurring on the island. Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 41 studies make advantage of these data for inter-comparison, validation, and combined analyses of satellite observations.
e. , assuming the representativeness of the parameters employed for the MC simulations). The characterization of the ocean temperature and salinity in the Lampedusa area has been based on in-situ and satellite observations. Data from a conductivity temperature depth (CTD) at 18 m depth, and from a temperature and pressure sensor at a 1 m depth at the Lampedusa Oceanographic Observatory have been used for the reference period 2018-2019. The annual evolution of the temperature and salinity monthly mean values at 18 m depth is shown in Figure  8. By comparison, the range of temperature and salinity at the MOBY site, derived from Feinholz et al. [16] , are also shown in Figure  8. The annual temperature cycle is larger at Lampedusa than at MOBY; conversely, the annual salinity changes are significantly smaller at Lampedusa.
5% in the whole area around Lampedusa. 3 ). The characterization of the ocean temperature and salinity in the Lampedusa area has been based on in-situ and satellite observations. Data from a conductivity temperature depth (CTD) at 18 m depth, and from a temperature and pressure sensor at a 1 m depth at the Lampedusa Oceanographic Observatory have been used for the reference period 2018-2019. The annual evolution of the temperature and salinity monthly mean values at 18 m depth is shown in Figure  8. By comparison, the range of temperature and salinity at the MOBY site, derived from Feinholz et al. [16] , are also shown in Figure  8. The annual temperature cycle is larger at Lampedusa than at MOBY; conversely, the annual salinity changes are significantly smaller at Lampedusa. These variations constitute essential information for the determination of the water refractive index.
Currents Data for currents at different depths at the proposed sites have been extracted from the CMEMS Med Sea Physical Reanalysis dataset  [52]. Daily data for the period 1987-2013 have been used. The information on currents at the Lampedusa area has been compared with current data for the MOBY site retrieved from the CMEMS GLORYS12V1 global reanalysis (http://resources. marine. copernicus. eu/documents/PUM/CMEMS-GLO-PUM-001-030. pdf). The reference period that has been considered is 1993-2013. Figure  9  shows profiles of the median, 99th percentile, and maximum values of currents at LMP1, LMP2, and MOBY. For comparison, also values at the Boussole area (Ligurian Sea), also derived from the CMEMS Med Sea Physical Reanalysis dataset, are displayed. Currents are generally weaker at the Lampedusa area than at MOBY and in the Ligurian sea, although higher current may occur in specific events.
Currents Data for currents at different depths at the proposed sites have been extracted from the CMEMS Med Sea Physical Reanalysis dataset  [52]. Daily data for the period 1987-2013 have been used. The information on currents at the Lampedusa area has been compared with current data for the MOBY site retrieved from the CMEMS GLORYS12V1 global reanalysis (http://resources. marine. copernicus. eu/ documents/PUM/CMEMS-GLO-PUM-001-030. pdf). The reference period that has been considered is 1993-2013. Figure  9  shows profiles of the median, 99th percentile, and maximum values of currents at LMP1, LMP2, and MOBY. For comparison, also values at the Boussole area (Ligurian Sea), also derived from the CMEMS Med Sea Physical Reanalysis dataset, are displayed.