dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability. The RTTOV model can be downloaded free of charge from the NWP SAF website (https://nwp-saf. eumetsat. int, last access: 16 November 2020) once users have registered on the site to agree to the licence conditions. Updates to the code and coefficients for new instruments are also posted on the site. RTTOV v13. 0 was released in November 2020 and coefficients based on the new v13 predictors as well as the older predictor versions are available for download from the website. The website also hosts plots of statistics of LBL vs RTTOV comparisons over the training profiles similar to those discussed in this paper for the majority of RTTOV coefficient files for all predictor versions (https://nwp-saf. eumetsat. int/site/software/rttov/download/coefficients/comparison-with-lbl-simulations/).
Simulations 250 were run for all 14 secants used in training the coefficients for the v9 and v13 predictors with variable O 3 and CO 2. The profile dataset includes variable water vapour and O 3 , and all other gases use fixed background profiles. While there is an increase in bias with the v13 predictors in the water vapour and CO 2 channels (6. 3, 7. 3 and 13. 4 µm), the new predictors result in (a) Mean 0. 00 Reflectance difference x10 3 0. 10 0. 05 0. 75 1. 00 1. 25 v9pred v13pred (b) Standard deviation 0. 50 0. 25 0. 00 0. 47 0. 64 0. 86 Channel wavelength m 1. 37 1. 61 2. 24 comparable or smaller standard deviations in all channels which is arguably more important since bias correction can mitigate larger biases. https://doi. org/10. 5194/gmd-2020-424 Preprint. Discussion started: 15 February 2021 c Author(s) 2021.
Experimental purpose The experimental purpose is correlated with microscopic analysis (R = 0. 57, P < 0. 001), CPCE software (R = 0. 49, P < 0. 001) and biodiversity analysis (R = 0. 44, P < 0. 001). This objective is also correlated with a structural ecology approach (R = 0. 27, P = 0. 024), submersion depths from 0 to 10 m in the intertidal zone (R = 0. 51, P < 0. 001 and R = 0. 41, P < 0. 001), artificial seabed (R = 0. 35, P = 0. 004) and short or medium immersion periods (R = 0. 49, P < 0. 001 and R = 0. 32, P = 0. 008). Experimental purpose is correlated with the following AR construction materials: plastic (R = 0. 41, P < 0. 001), ceramic (R = 0. 28, P < 0. 022), shells (R = 0. 27, P = 0. 031) and rocks (R = 0. 44, P < 0. 001); and with shapes made of bags (R = 0. 24, P = 0. 048), plates (R = 0.
Conclusions The binary nature (presence or absence) of the data frame might explain the massive loss of detailed information in the scientific literature. While the 67 studied variables are the most representative of the selected papers, there are many other aspects not included in this statistical analysis such as the surrounding environment or non-indigenous species. Our study nevertheless represents an objective view of the quantitative and qualitative aspects of artificial reefs. It reveals the wide variety of artificial reefs in the world. Independently of any purpose, the effectiveness of AR depends to a great extent on the properties of these structures. Concrete with high roughness is by far the most widely used material and seems to be one of the most efficient. The rugosity of the substrate would increase the surface available for settlement and can consequently increase the biomass, the coverage percentage and the primary production of the structure.
Acknowledgements The results presented were obtained in the collaborative project  Interreg Va MARINEFF , selected under the  European Crossborder Programme INTERREG FMA , and co-funded by the  ERDF. The authors wish to thank the co-financiers and all project partners for their support. This work was funding by the  University of Caen-Normandie (France) , the  Interreg Va MARINEFF  project and the  European Union. Dr M. S. N. Carpenter  post-edited the English style and grammar.
Flow Cytometry Analyses According to previously described protocols  (Khalil et al. , 2016; Andreani et al. , 2017) , we centrifuged the viral supernatant at 700 × g for 10 min, added a 1:100 SYBR green dye dilution (SYBR green I nucleic acid gel stain; Molecular Probes, Life Technologies), and then heated the mixture at 80°C for 3 min. Stained viral populations were acquired on a BD LSR Fortessa (BD Biosciences) cytometer and compared with previously recorded gates for known viral isolates on FlowJo software.
The software EPI2ME lead to 13,122 analyzed viral sequence reads totaling 30. 3 Mb, with an average length of 2,305 kb and a maximum read length of 32,328 bp. Paired-end Illumina reads and Minion reads were de novo assembled using the hybridSPAdes algorithm  (Antipov et al. , 2016). We obtained two scaffolds representing a total size of 1,595,395 bp with an average read coverage of 121-fold for the first scaffold and 102-fold for the second.
Genome Analysis Gene predictions were computed using Prodigal  (Hyatt et al. , 2010). We deleted 49 predicted proteins of fewer than 50 amino acids, or which had abnormal tri-dimensional folding (from 50 to 99 amino acids), as detected by Phyre2  (Kelley et al. , 2015). Protein blast was performed against the non-redundant (nr) protein database. Annotation was performed using a combination of Interpro https://www. ebi. ac. uk/interpro/search/ sequence-search version 69. 0, a CD-search tool online  (Marchler-Bauer and Bryant, 2004 ) and delta-blastp  (Boratyn et al. , 2012). tRNA prediction was computed online  (Lowe and Chan, 2016)  with the eukaryotic model parameter.
Phylogenetic Analysis -Protein Trees Phylogenetic analyses for ATPases were conducted as follows. Blastp was used to find 39 close homologous proteins, and amino acid sequences were then aligned using the MUSCLE program  (Edgar, 2004). The Mega 6. 0 software was used with standard parameters using the maximum likelihood method with 1,000 bootstrap replicates and the Jones-Taylor-Thornton model for amino acid substitution. Phylogenetic trees were then visualized using iTOL v6 online  (Letunic and Bork, 2016).
Genomic Features of Fadolivirus The Fadolivirus genome assembled into two scaffolds with a total length of 1,595,395 bp (with 1,573,504 bp for scaffold 1 and 21,891 bp for scaffold 2; Table  1 ), encoding 1,452 genes of which 1,428 were on the larger scaffold, here referred to as the major scaffold. A megablast of the major scaffold against the nucleotide collection in the NCBI database revealed matches to Indivirus, Klosneuvirus, Catovirus, and Tupanviruses. A whole genome alignment showed that numerous blocks were conserved between Fadolivirus, Klosneuvirus KNV1 and Indivirus ILV1 (Figure  3 ). However, when aligned to the genome of Bodo saltans virus, the proportion of shared sequences decreased notably (data not shown). A blastp comparison of Fadolivirus proteins to the NCBI nonredundant database (nr) revealed the presence of 256 ORFans without any matches in nr (≈17,6% of all predicted proteins), 552 hits with Klosneuvirus KNV1 (≈38% of all predicted proteins), 223 with Indivirus ILV1 (≈15,4% of all predicted proteins), 24 with Catovirus CTV1, 21 with Hokovirus HKV1 and 24 with Tupanvirus strain deep ocean, 23 with Tupanvirus soda lake, and only 59 with other viruses.
Fadolivirus Genes Involved in Energy Metabolism The Fadolivirus genome annotation revealed multiple sequences annotated as putative ATPase subunits and proteins associated with electron transfer with a blue copper enzyme and a putative cytochrome 5b (see previous section). This cytochrome sequence is a probable nitrate reductase (NADH) protein  (Chamizo-Ampudia et al. , 2017)  in the Fadolivirus genome. We identified six proteins associated with proton pumps containing ATPase domains. However, these genes were not co-located in the genome of Fadolivirus. A more detailed phyre2 analysis of these 6 proteins revealed that two of them were indeed predicted as v-type proton ATPase subunit D (QKF94710. 1) with 100% confidence and 28% identity with the Yeast V-ATPase state 3 (PDB database 3J9V), and with the other states 1 and 2.
FUNDING This work was supported by the  French Government  under the " Investissements d'avenir" (Investments for the Future ) programme managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research)  (reference: Méditerranée Infection  10-IAHU-03 ). Parts of this study were performed by the  US Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility  and made use of resources of the  National Energy Research Scientific Computing Center , both supported by the  Office of Science of the US Department of Energy  under Contract no. DE-AC02-05CH11231.
As these quantities can be diagnosed from an appropriate dataset, we can use this formulation to diagnose the variance growth. In the proceeding section we diagnose (from more realistic models that are assumed to capture a substantial portion of atmospheric and oceanic turbulence) l and S for the cases of external (atmospheric; l E , S E ) and internal (oceanic mesoscale eddy driven; l I , S I ) turbulent fluxes, assessing the appropriateness of the Ornstein-Uhlenbeck representation. We then proceed to attribute the variance of different metrics in response to these sources, which, following (24) and assuming independence between the internal and external components is given by Var[hFju(t 0 , t 1 i]5 ð t 1 t 0 ð t 1 t 0 hFjC(t 1 , t)e 2l I t S I e 2l y I s C y (s, t 1 )jFi dt ds 1 ð t 1 t 0 ð t 1 t 0 hFjC(t 1 , t)e 2l E t S E e 2l y E s C y (s, t 1 )jFi dt ds.
Model configurations, methods, and experimental design a. Linear ocean model configuration As outlined in section 2, we use a linear ocean model to provide the propagator matrix C, which is used both to derive our  OSPs [following (17) ] and to evolve our prescribed, diagnosed stochastic processes [following (  24 )]. The model is v3. 4 of the NEMO GCM  (Madec et al. 2012 ) whose routines are linearized in the tangent-linear and adjoint model (TAM) package NEMOTAM  (Vidard et al. 2015). The model is run in the nominal 28 ORCA2 configuration with 31 vertical levels in partial-step z coordinates, subject to repeated CORE normal year forcing  (Large and Yeager 2004). More details can be found in  Stephenson et al. (2020). We note that the same ocean model is common to our linear propagator C, the coupled climate model (section 3b) used to diagnose our stochastic external flux representation (l E , S E ) and the high-resolution ocean model (section 3c) used to diagnose our stochastic internal flux representation (l I , S I ).
Acknowledgments. This research was supported by the  U. K. Natural Environmental Research Council  (SMURPHS,  NE/N005767/1  and  MESO-CLIP ,  NE/K005928/1  and  SPITFIRE ,  NE/L002531/1 ) and by the  DECLIC  and  Meso-Var-Clim  projects funded through the  French CNRS/INSU/LEFE program. The authors kindly thank  Victor Estella Perez  and  Juliette Mignot  for performing the coupled model simulation and  Thierry Penduff  and  Jean-Marc Molines  for supplying source code and forcing files for the eddying model simulation, as well as  Simon Müller  for his assistance with the adjoint model configuration.
Data availability statement. Data used to produce this work are available as follows. Output used from the IPSL-CM5A model is located at https://doi. org/10. 5281/zenodo. 4300471. Source code, configuration files and climatological forcing files for NEMO v3. 5 in the ORCA025 configuration are available at https://doi. org/10. 5281/zenodo. 4473198. The source code for NEMO/NEMOTAM v3. 4 in the ORCA2 configuration is available from the NEMO team at https://forge. ipsl. jussieu. fr/ nemo/svn/NEMO/releases/release-3. 4, with configuration and normal year forcing files for ORCA2-LIM located at https:// doi. org/10. 5281/zenodo. 1471702. Modifications to model source code specific to our experiments, scripts to run the experiments themselves, and diagnostic scripts used here can be found at https://doi.
Melting curves were systematically monitored (5 s at 95 • C, 1 min at 65 • C, temperature gradient 0. 11 • C/s from 65 to 97 • C) at the end of the last amplification cycle to confirm the specificity of the amplification reaction. Each PCR assay included replicate samples (duplicate of reverse transcription) and negative controls (reverse transcriptase and RNA free samples). Relative quantification of target gene expression was performed using the E-method from the Light Cycler 480 software (v. SW 1. 5; Roche Diagnostics). PCR efficiencies were measured by the slope of a standard curve using serial dilution of cDNA, and they ranged between 1. 85 and 2. 0. Transcripts were normalized to ef1α and exprimed as fold change vs. D9 density for all genes.
Primer Design The primer sequences used to amplify all genes and accession numbers of the primers are presented in Scheme 1. For gene targets that had not previously been validated, primers were tested on a pool of cDNA and amplified products were systematically sequenced. hormone receptor (crhr1, crhr2), melanocortin 2 receptor (mc2r), lysozyme 2 (lyzII), interferon gamma (ifn-γ1), heat shock proteins (hsp30, hsp70), interferon regulatory factor (irf-1a irf-1b, irf3, irf4, irf-5a, irf-5b and irf9) were designed based on the conserved regions of the sequences in Gene Bank (Rainbow trout, Accession Number respectively). Multiple qPCR primer combinations were designed for each gene using Primer3 software  [23]  (Scheme 1). To optimize annealing temperature for the respective gene, PCR was performed with specific primer pairs and analysis of PCR products was done by electrophoresis on agarose gel (1.
Transcript Forward Primer Reverse Primer Accession Number ef1a TCCTCTTGGTCGTTTCGCTG ACCCGAGGGACATCCTGTG AF498320 il-1β1 AGTGTTGGAGTTGGAGTCGG CATCAGGACCCAGCACTTGT NM_001124347 il-1β2 ACCACTTAGAATGCATGCCCA CCGACTCCAACTCCAACACT AJ245925 il-1β3 AGGACGCCAGAAAGCTCATC TTCTCAGATTCACCCCGCAC AJ557021 il-4 GACAATCTTGGCCTCCGTGA CCACCTGGTCTTGGCTCTTC NM_001246341 il-6 CCCTACTCCCCTCTGTCACA AACACGCTTCCTCTCACTGG NM_001124657. 1 il-8 ATTGAGACGGAGAGCAGACG AATCTCCTGACCGCTCTTGC NM_001124362. 1 il-10 CGCCTTCTCCACCATCAGAG CTGTCCATAGCGTGACACCC NM_001245099 il-11 ACCACCCTGCTCTCCCTAAA GGAGAGAGACGCTGAGCATC AJ535687. 1 tnf-α1 AATCTTTCCGCTGACACCGT CGAAGAAGAGCCCAGTGTGT NM_001124357 tnf-α2 GCACTATGATGAGAGAGAGGCA GGCTCAGTTACCATAAAATTGCA NM_001124374 tnf-α3 GAGTGACGGTGGACTTGGAG CTGAGACACACAGCGCCAT HE798544 crf GCAACAGTCTCTTCCCCTCC CTTGGGGTGCATGACTTTCG NM_001124286.
1 Scheme 1. Nucleotide sequences of the PCR primers used to evaluate mRNA abundance of transcripts by RT-PCR. The qPCR primers for interleukin: il-1β (1,2,3), il-4, il-6, il-8, il-10 and il-11, for tumor necrosis factor: tnf-αa (1,2,3), corticotropin releasing factor (crf ), corticotropin-releasing hormone receptor (crhr1, crhr2), melanocortin 2 receptor (mc2r), lysozyme 2 (lyzII), interferon gamma (ifn-γ1), heat shock proteins (hsp30, hsp70), interferon regulatory factor (irf-1a irf-1b, irf3, irf4, irf-5a, irf-5b and irf9) were designed based on the conserved regions of the sequences in Gene Bank (Rainbow trout, Accession Number respectively). Multiple qPCR primer combinations were designed for each gene using Primer3 software  [23]  (Scheme 1). To optimize annealing temperature for the respective gene, PCR was performed with specific primer pairs and analysis of PCR products was done by electrophoresis on agarose gel (1.
Primer design Catalase, gst, hsp90, hsp70, and ferritin genes were targeted as biomarkers for the ROS response. A CYP gene from clan 3 (cyp3026b) was targeted as a biomarker for PAH metabolism. Primers for catalase (cat), cyp3026b and gst were designed de novo from conserved regions in closely related crustacean species and scaffolds from the Acartia tonsa genome project  (Jørgensen et al. , 2019 ) (Accession number PRJEB20069). Clustal Omega () was used for multiple sequence alignment. For cyp3026b, Acartia tonsa genome sequence LS054218. 1 was aligned with the gene sequences from Tigriopus japonicus (KF639998. 1) and Eurytemora affinis (XM 023493699. 1). For catalase, the genome sequence LS054460. 1 was aligned with sequences from Calanus finmarchicus (EL965956. 1) and Eurytemora affinis (XM023472406.
Data showing non-normal distribution were analyzed using the non-parametric Kruskal-Wallis test, followed by the Dunn's post-hoc test for multiple comparisons. In all cases the Bonferroni correction for multiple comparisons was applied. All these analyses were performed with the aid of the statistical package IBM-SPSS v26. The relationship between the count data of the number of reproductive females and WAF doses was analyzed by a modified Fisher exact test (Freeman Hamilton test), due to the small sample size and the contingency table larger than 2 × 2 (VassarStats,  Lowry, 1998 Lowry, -2019)). The association between both variables was determined by a linear regression coefficient (IBM SPSS v26). Egg hatching success during individual fecundity tests was calculated as follows: (number of hatched egg per female/total number of eggs produced per female) x100.
Gene Primer sequences 5 ′ to 3 ′ Tm ( relative gene transcription levels were determined using the Pfaffl method  (Pfaffl et al. , 2001). The transcript levels were log2 transformed and statistical differences between control and treatments were determined using one-way ANOVA. The Dunnet post-hoc test was further used to determine the statistical difference compared to control (IBM SPSS v26). Z-test was used to determine significant differences in the IBR/n index values among experimental groups.
Effect of WAFs on total lipid Ovaries were demonstrated as a vital accumulation tissue for lipids in female Acartia tonsa copepods (see Supplementary Material and Fig. S1 ). We quantified the lipid content using the Nile red lipophilic dye to give further insights into how exposure to the three types of WAFs affected the amount of accumulated lipids in females. Overall, the collected data was highly variable in all treatments and control (Fig. 4 ). Nevertheless, Nile red fluorescence values of the copepods exposed to the high MGO WAF dose were significantly lower than thaose observed in the control copepods (Fig. 4c ), indicating a loss in total lipid levels.
Acknowledgements This work was funded by the  Basque Government  (grant to consolidated research groups  IT1302-19 ) and the  European Union  ( GRACE project, EU Horizon 2020  grant No  679266 ). The  University of the Basque Country  supported TH with a cotutelle (co-supervision) doctoral grant with the  Universté de Pau et des Pays de l'Adour. We want to thank the  Norwegian University of Science and Technology  ( NTNU; Trondheim, Norway ) for providing us with the oils used in the experiments.
08 P c t- where (x t , y t , t) are the coordinates of the eye defined in Equations (  1 ) and (2), SST(x t , yt, t) and T tropo (x t , y t , t) are respectively the sea-surface and tropopause temperatures, R d = 287. 058 J • kg -1 • K -1 is the specific gas constant for dry air, MSLP(x t , y t , t) is the mean local sea level pressure, RH(x t , y t , t) is the near surface relative humidity at 2 meters extracted from the monthly dataset of ERA-5 climate reanalysis or CMIP climate models. f (y t ) = 2ω sin(y t ) is a Coriolis parameter depending on the latitude, r env is the distance between the eye and the area under regular conditions (fixed at 500 km), q env and q * c respectively are the specific humidity at environmental conditions and at saturation, i. e. for RH = 100%, in the eye. ∆S m is the difference of moist entropy between the environment and the storm center and L υ is the latent heat of vaporization.
In particular, the reporting can vary in terms of sustained wind speed. According to the dataset documentation the North Atlantic -U. S. Miami (NOAA NHC) bureau (hurdat/atcf)gives the 1-minute winds speed while Tokyo i. e. RSMC Tokyo (JMA) provides dirrectly the 10-minute sustained wind speed (Similarly, newdelhi corresponding to RSMC New Delhi (IMD) gives the 3-minute wind speed; reunion -RSMC La Reunion (MFLR), the Australian TCWCs (TCWC Perth, Darwin, Brisbane) (BOM), the RSMC Nadi (FMS); TCWC Wellington (NZMS) provide the 10 minute sustained wind speed and (CMA) 2-minute sustained wind). The lack of reporting standards between agencies is a source of uncertainty in the input data.
A new model for grapevines (Vitis vinifera) is the first perennial fruit crop model using the Agricultural Production System sIMulator (APSIM) Next Generation framework. Modules for phenology, light interception, carbohydrate allocation, yield formation and berry composition were adapted or added into APSIM Next Generation to represent the nature of fruit-bearing vines. The simulated grapevine phenological cycle starts with the dormancy phase triggered by a critical photoperiod in autumn, and then goes through the subsequent phenophases sequentially and finally returns to dormancy for a new cycle. The canopy microclimate module within APSIM Next Generation was extended to allow for row crop light interception. The carbohydrate arbitrator was enhanced to consider both sink strength and sink priority to reflect carbohydrate reserve as a concurrent competing sink.
2003)  and therefore would need to be adapted for other applications. Such an approach may be difficult in existing grapevine farming system models. The Agricultural Production System sIMulator Next Generation (APSIM Next Generation)  (Holzworth et al. 2018 ) platform has advantages over other modelling platforms, allowing scientists to construct and configure a crop model visually, by dragging and dropping components in the user interface (UI)  (Brown et al. 2014). It combines a range of tools in the UI to assist model developers to build, run, test, evaluate and auto-document the model (Fig. 1 ; see Supporting Information-Fig. S1 ). The APSIM Next Generation plant modelling framework provides a collection of generic software classes, e. g. plant organs and functions that can be assembled and parameterized in different ways to create different plant models  (Brown et al.
Model description A series of developments were undertaken to adapt the APSIM Next Generation source code to enable simulation of perennial fruit crops. The purpose of the model presented here was to simulate the phenology, canopy development, light interception, carbohydrate assimilate and allocation, carbohydrate storage and remobilization, water uptake, organ growth and yield formation in a row-planted vineyard (Fig. 2 ; see Supporting Information-Method S1 APSIM Next Generation grapevine model documentation). The basic principle of the model is that phenology is largely driven by temperature and growth is dynamically driven by both environmental factors and differential allocation of carbohydrate among organs. It includes the following novel features: • A new carbohydrate allocation method to distribute carbohydrate to structural and non-structural organ components simultaneously based on sink strength and priority • A yield module that uses the weather conditions in both the previous and current season as well as the carbohydrate status to determine potential bunch number per shoot, berry number per bunch and berry weight • A row and alley strip configuration to represent a typical vineyard set-up with rows of grape vines growing in a strip of bare ground, interspersed with alleys of short grass [see Supporting Information-Fig.