dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing and data processing Raw FASTQ files were de-multiplexed and quality-filtered using QIIME (version 1. 9. 1)  (Caporaso et al. , 2010)  with the following criteria: (i) The 300-bp reads were truncated at any site that obtained an average quality score of < 20 over a 10-bp sliding window, and the truncated reads shorter than 50 bp were discarded; (ii) exact barcode matching, two nucleotide mismatch in primer matching, and reads containing ambiguous characters were removed; (iii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. Reads that could not be assembled were discarded. Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered using UPARSE (version 8. 0. 1517)  (Edgar, 2010) , and chimeric sequences were removed using UCHIME.
The taxonomy of each 16S rRNA gene and ITS sequence was analyzed against the Silva and Unite database at a confidence threshold of 70%, respectively. The rarefaction analysis based on Mothur v. 1. 21. 1 was conducted to reveal the diversity indices, including the Chao, Shannon, and coverage indices. The raw sequence reads data have been deposited in the National Genomics Data Center under the GSA accession number CRA004278 that is publicly accessible at https://ngdc. cncb. ac. cn/gsub/submit/gsa/ subCRA005845/finishedOverview.
Although many indices could be used to test the goodness of SEMs, there is no universally accepted single index. Hence, we used a combination of χ2 test, root mean square error of approximation (RMSEA) test, and comparative fit index (CFI) to assess the goodness of SEMs. A nonsignificant χ2 and RMSEA test, and CFI> 0. 9 indicate a good fit of the model to the data  (Kline, 2011). Finally, the effects of herbaceous and edaphic dissimilarity between stages on bacterial and fungal dissimilarity between stages were extracted from each SEM, and the relationship between the effects and the successional gradient were fitted with linear regression. All data were analyzed using R software, version 3. 5. 3 (R Core Team, 2019). Shannon diversity, dissimilarity indices, and the permutation test were conducted with the 'lmPerm' package  (Wheeler and Torchiano, 2016)  and 'rcompanion' package  (Mangiafico and Mangiafico, 2017) , while the SEMs were conducted using the 'lavaan' package  (Rosseel, 2012).
Acknowledgments This work was funded by the  Scientific Project of Gansu Province  ( 20JR5RA548 ), the  Project of Qinghai Science and Technology Department  ( 2020-NK-125 ,  2020-ZJ-Y40 ), and the  Science and Technology Innovation Program of Gansu Forestry and Grassland Administration  (No. 202001 ). The authors would like to thank the colleagues,  Dr. Xiande Liu ,  Shunli Wang  and  Jian Ma , from  Gansu Province Academy of Qilian Water Resource Conservation Forests Research Institute  for their help in fieldwork. The authors also appreciated the support from the  Supercomputing Center of Lanzhou University.
Data Our dataset is composed of an analysis of total cloud cover (cf. section 2. 2. 2) and modeled data: ARPEGE NWP forecasts concerning weather fields (cf. section 2. 2. 3) and SURFEX for terrain data (cf. section 2. 2. 4). The analysis is considered as the ground truth and is used to evaluate the ARPEGE and post-processed TCC forecasts, as well as to train the machine learning algorithms. All the data were produced on a regular grid of 0. 1 o × 0. 1 o over an enlarged region of Europe (cf. section 2. 2. 1). Only data at 15h00 UTC are considered across a two years period  (2017 -2018). After removing the dates for which data are missing, there are 662 days left.
ARPEGE data ARPEGE (Action de Recherche Petite Echelle Grande Echelle) is the global operational NWP system operated by Météo-France  (Courtier et al. , 1991). ARPEGE forecasts run with a time step of 9 minutes on a stretched grid allowing a 7. 5 km resolution grid mesh over France. The vertical discretization is performed on 105 levels, with the lowest one at 10 m. ARPEGE is initialized by a 4D-Var data assimilation scheme. We used the operational weather forecasts of the years 2017 and 2018. The version of the model did not evolve very much during that period, making the forecasts consistent during the 2-year period. The data are produced on a regular 0. 1 o × 0. 1 o latitude/longitude grid on a domain encompassing Europe, North Africa and part of the Atlantic Ocean (see figure  1  for the extent of the region).
For numerical estimation of the linear coefficients, we take advantage of the lqm function of the R lqmm package  (Geraci, 2014). The function maximizes the loglikelihood of a Laplace regression. This is equivalent to the minimization of the weighted sum of absolute residuals  (Koenker and Bassett, 1978 ). We faced many numerical problems when estimating our quantile regression: the current operational ARPEGE MOS application required estimating 20 × 10 6 equations corresponding to the number of grid points times the number of lead times -thus requiring a very robust estimation procedure. We found out that the optimization algorithm based on the gradient of the Laplace log-likelihood implemented in lqm function  (Bottai et al. , 2015)  met our requirements in terms of robustness. Lastly, variable selection is performed using the BIC  (Schwarz, 1978)  criterion and a simple backward procedure.
The input patches are then overlapped and the outputs are cropped to remove the boundaries of the output patches, resulting in 48 × 48 output patches from the 64 × 64 input patches. There is no activation function after the final 1 × 1 convolutional layer in order to produce a regression. Finally, the loss function used is the mean square error (MSE) since the U-Net is designed to perform a regression of the TCC value. Additional modifications are described in the next sections. We used the PyTorch library of Python for the deep learning step, and optimizations concerning the learning phase are described in Kivachuk Burdá et al. (n. a. ).
Loss functions As explained before, the TCC has a U-shaped distribution. The values different from 0 and 100 % are then underrepresented, which can prevent the good representation of these values by the U-Net. A common way to balance the dataset is over (or under) sampling. It consists of duplicate (or remove) underrepresented values (overrepresented values). This is very delicate to apply to our dataset because the targets are 2D continuous data. Another common way, more adapted to our data, is the weighted loss function. It consists of increasing the importance of the underrepresented values by increasing the importance of the errors made on these values. This is done by multiplying the loss function by a weight depending on the value of the corresponding target value, the weight increasing with the rarefaction of the target value.
Cloud cover forecast evaluation The evaluation of cloud forecasts follows the World Meteorological Organization's (WMO) guidelines  (World Meteorological Organization, 2012). Firstly, they recommend that the truth and model distributions are analyzed and compared. They also recommend that data and results be stratified (lead time, diurnal cycle, season, geographical region, cloud cover threshold). We chose a threshold of 10 % to distinguish clear to overcast skies because Earth observation from space needs very low cloud amount. Moreover, performances are calculated for 3-month seasons corresponding to meteorological seasons (Dec-Jan-Feb, Mar-Apr-May, Jun-Jul-Aug and Sep-Oct-Nov) as well as monthly. Performances are also calculated and represented as maps in addition to regional metrics (see figure  1 ) in order to perform a spatial evaluation.
CFD computations In the present study, the input parameters of the models are retrieved from two different RANS simulations. The first one is a 3D viscous mixing-plane simulation performed by Nallasamy and Envia in 2005  [36]  using the code APNASA. A modified  - turbulence model  [74]  was used to account for the effect of turbulence. The mesh is composed of about one million cells: 407 cells in the streamwise direction, and 51 cells in both azimuthal and radial directions. The second was performed by  Leonard et al. in 2016 [66]  as part of a comprehensive numerical study on the SDT configuration, involving both RANS and LES simulations. The simulation was performed using the ANSYS CFX v15. 0 solver. Similarly to the previous simulation, a mixing-plane was used at the rotor-stator interface.
Acknowledgments This work was supported by several funding sources: D. L. received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 690714  (project  TurbonoiseBB ), J. d. L. received funding from  NSERC (Natural Sciences and Engineering Research Council of Canada ) and V. M. was funded by  Airbus  through the  Industrial Chair in Aeroacoustics at Université de Sherbrooke. This work was performed within the framework of the  LABEX CeLyA  ( ANR-10-LABX-0060 ) of  Université de Lyon , within the  program 'Investissements d'Avenir'  ( ANR-16-IDEX-0005 ) operated by the  French National Research Agency (ANR). The computational resources were provided by  CERFACS. The authors would like to thank  E. Envia  from  NASA  for providing the experimental data in the framework of the AIAA benchmark.
In recent decades, seasonal allergic rhinitis (SAR) prevalence has increased and recent studies have shown that air pollutants, such as diesel exhaust particles (DEP), can increase inflammatory and allergic biomarkers. The aim of this study was to investigate the effects of DEP on SAR symptoms induced by ragweed and to evaluate the efficacy and safety of fexofenadine HCl 180 mg versus placebo. This phase 3, single-centre, sequential, parallel-group, double-blind, randomised study (NCT03664882) was conducted in an environmental exposure unit (EEU) during sequential exposures: Period 1 (ragweed pollen alone), Period 2 (ragweed pollen+DEP), and Period 3 (ragweed pollen+DEP+single-dose fexofenadine HCl 180 mg or placebo). Efficacy and safety were evaluated in Period 3. Primary endpoints were the area under the curve (AUC) of total nasal symptom score (TNSS) from baseline to hour 12 (AUC 0-12 ) during Period 1 and Period 2; and the AUC of the TNSS from hour 2 to 12 (AUC 2-12 ) during Period 3.
Of note, DEP have been implicated in exacerbating long-distance spread of viral infection, for example during the coronavirus disease 2019 pandemic  [8]. Additionally, they can interact with allergens to enhance allergen-induced responses, up to 50-times more than allergens alone  [9]. Fexofenadine hydrochloride (HCl) is a second-generation, selective histamine H1-receptor antagonist, approved to relieve SAR symptoms in adults and children  [10] , and is marketed in approximately 100 countries worldwide, including the USA, Japan and Europe. Depending on the country, the approved single daily dose is either 120 mg or 180 mg (Allegra/Telfast; Sanofi, Paris, France)  [11, 12]. This study (NCT03664882) was a prospective, sequential and parallel-group, double-blind, single-dose, placebo-controlled, randomised clinical investigation of the effects of DEP on SAR ragweed-induced symptoms and evaluating the efficacy and safety of fexofenadine HCl 180 mg versus placebo in alleviating such pollution-aggravated symptoms.
Acknowledgments: The authors thank the subjects who took part in this study. The authors would also like to acknowledge  Lisa Steacy  and  Daniel Adams  for their roles as study manager/coordinators,  Terry Walker  (all of the  Allergy Research Unit, Kingston Health Sciences Centre, Kingston, ON, Canada ), who was responsible for EEU operations, and  Mikhail Melikov ,  MPH in Biostatistics and Epidemiology of IT&M STATS , who reviewed the statistical analysis. The authors would also like to acknowledge  Claire Lydon of iMed Comms , an  Ashfield Company , part of  UDG Healthcare plc , and  Martina Klinger Sikora  and  Mark Davies  of inScience Communications, for medical writing support that was funded by  Sanofi.
To our knowledge, such an association has never been investigated. Although this test per se does not prove that viruses regulate CEE, we consider the association is worth being tested because such an association is a necessary condition for the global model of viral shunt and shuttle and, under its absence, we would have to reconsider the model. Deep sequencing of planktonic community DNA and RNA, as carried out in Tara Oceans, has enabled the identification of marker genes of major viral groups infecting eukaryotes  (Hingamp et al. , 2013; Carradec et al. , 2018; Culley, 2018; Endo et al. , 2020). To examine the association between viral community composition and CEE, we thus used the comprehensive organismal dataset from the Tara Oceans expedition  (Carradec et al. , 2018; Sunagawa et al.
Detection of diverse eukaryotic viruses in Tara Oceans gene catalogs We used profile hidden Markov model-based homology searches to identify markergene sequences of eukaryotic viruses in two ocean gene catalogs. These catalogs were previously constructed from environmental shotgun sequence data of samples collected during the Tara Oceans expedition. The first catalog, the Ocean Microbial Reference Gene Catalog (OM-RGC), contains 40 million non-redundant genes predicted from the assemblies of Tara Oceans viral and microbial metagenomes  (Sunagawa et al. , 2015). We searched this catalog for NCLDV DNA polymerase family B (PolB) genes, as dsDNA viruses may be present in microbial metagenomes because large virions (> 0. 2 μm) have been retained on the filter or because viral genomes actively replicating or latent within picoeukaryotic cells have been captured.
Viruses positively correlated with CEE tend to interact with silicified organisms The phylogeny-guided, network-based host prediction analysis correctly predicted known virus-host relationships (for viruses infecting Mamiellales, Prymnesiales, and Chaetocerotales) using our large dataset, despite the reported limitations of these cooccurrence network-based approaches  (Coenen and Weitz, 2018). This result prompted us to further exploit the species co-occurrence networks (Table  S4 ) to investigate functional differences between the eukaryotic organisms predicted to interact with positive VIPs, negative VIPs, and viruses less important for prediction of CEE (VIP score < 2) (non-VIPs). For this purpose, we used literature-based functional trait annotations associated with eukaryotic meta-barcodes (see Transparent Methods).
, 2018). In the same J o u r n a l P r e -p r o o f bloom, viral infection stage was found to proceed with water column depth  (Sheyn et al. , 2018). No EhV-like PolB sequences were detected in our dataset, which was probably due to sampled areas and seasons. Laboratory experiments suggest that viruses closely related to positive VIPs, such as prasinoviruses, have infectious properties that may drive carbon export. Cultures of Micromonas pusilla infected with prasinoviruses showed increased TEP production compared with non-infected cultures  (Lønborg et al. , 2013). The hosts of prasinoviruses (Mamiellales) have been proposed to contribute to carbon export in the western subtropical North Pacific  (Shiozaki et al. , 2019). Some prasinoviruses encode glycosyltransferases (GTs) of the GT2 family.
Conclusions Eukaryotic virus community composition was able to predict CEE at 59 sampling sites in the photic zone of the world ocean. This statistical association was detected based on a large omics dataset collected throughout the oceans and processed with standardized protocols. The predictability of CEE by viral composition is consistent with the hypothesis that "viral shunt" and "shuttle" are functioning at a global scale. Among 83 viruses with a high importance in the prediction of CEE, 58 viruses were positively and 25 negatively correlated with carbon export efficiency. Most of these viruses belong to Prasinovirus, Mimiviridae, and Picornavirales and are either new to science or with no known roles in carbon export efficiency. Thirty-six of these "select" viruses were predicted to infect ecologically important hosts such as green algae of the order Mamiellales, haptophytes, diatoms, and copepods.
Limitations of the study The observed statistical associations between viral compositions and examined parameters (i. e. , CEE, CE and NPP) do not convey the information about the direction of their potential causality relationships, and they could even result from indirect relationships as discussed above. Certain groups of viruses detected in samples may be over-or under-represented because of the technical limitations in size fractionation, DNA/RNA extraction and sequencing. Our custom R script used to test for association between viruses and environmental variables (CEE, CE 150 , NPP and T 100 ) is available along with input data at the GenomeNet FTP: ftp://ftp. genome. jp/pub/db/community/tara/Cpump/Supplementary_material/PLSreg/. The Taxon Interaction Mapper (TIM) tool developed for this study and used for virus host prediction is available at https://github.
Acknowledgements We thank the  Tara Oceans consortium , the projects Oceanomics and  France Genomique  (grants  ANR-11-BTBR-0008  and  ANR-10-INBS-09 ), and the people and sponsors who supported the  Tara Oceans Expedition  (http://www. embl. de/taraoceans/)for making the data accessible. This is contribution number  110  of the Tara Oceans Expedition 2009-2013. Computational time was provided by the  SuperComputer System, Institute for Chemical Research, Kyoto University. We thank  Barbara Goodson, Ph. D. , and  Sara J. Mason ,  M. Sc. , from  Edanz Group  (https://enauthor-services. edanzgroup. com/)for editing a draft of this manuscript. This work was supported by  JSPS/KAKENHI  (Nos. 26430184 ,  18H02279 , and  19H05667  to H. O. and Nos. 19K15895  and  19H04263  to H. E. ),  Scientific Research on Innovative Areas  from the  Ministry of Education, Culture, Science, Sports and Technology (MEXT) of Japan  (Nos.
Data and Code Availability The authors declare that the data supporting the findings of this study are available within the paper and its supplemental files, as well as at the GenomeNet FTP: ftp://ftp. genome. jp/pub/db/community/tara/Cpump/Supplementary_material/. J o u r n a l P r e -p r o o f 151_SRF 152_SRF rdrp_8855752 rdrp_36496887 rdrp_9164163 rdrp_54294427 rdrp_35713768 polb_013433452 rdrp_8626697 rdrp_9164160 rdrp_32150309 rdrp_32150057 polb_000172102 polb_000159717 polb_013294654 rdrp_35179764 rdrp_33049404 polb_000161220 polb_004804559 polb_000249217 rdrp_30787766 polb_010288541 polb_001175669 polb_000079111 polb_001527691 polb_003319665 polb_007771300 polb_000042601 polb_000200745 polb_003580241 rdrp_32202687 polb_007102163 polb_001064263 polb_015514497 polb_014364115 polb_000396610 polb_000490625 polb_000061559 polb_000061999 rdrp_36505302 polb_015907472 polb_000230224 polb_002503270 polb_000194282 polb_008001141 polb_000503865 polb_002682999 polb_000030837 polb_000328966 Rep_38177659 polb_000129518 polb_000079365 polb_000229407 polb_004312996 polb_000912507 polb_000435873 polb_000239928 polb_002035391 polb_007503502 polb_000248170 polb_000073352 polb_000054135 polb_000249074 polb_000232032 polb_000073545 polb_000026723 polb_003145223 rdrp_77677770 rdrp_84897402 polb_004775027 polb_000240662 polb_007423474 polb_000844241 polb_000673383 polb_000079078 polb_000110630 rdrp_49554577 rdrp_105714054 polb_000236849 rdrp_59731273 rdrp_42335229 rdrp_107558617 polb_000251540 rdrp_77677810 polb_000495602 polb_000172102 polb_000159717 polb_013294654 polb_004804559 polb_000079111 polb_007771300 polb_000200745 polb_000396610 polb_002503270 polb_000194282 polb_000229407 polb_000435873 polb_000239928 polb_002035391 polb_000248170 rdrp_8855752 rdrp_36496887 rdrp_9164163 rdrp_54294427 rdrp_35713768 polb_013433452 rdrp_8626697 rdrp_9164160 rdrp_32150309 rdrp_32150057 polb_000172102 polb_000159717 polb_013294654 rdrp_35179764 rdrp_33049404 polb_000161220 polb_004804559 polb_000249217 rdrp_30787766 polb_010288541 polb_001175669 polb_000079111 polb_001527691 polb_003319665 polb_007771300 polb_000042601 polb_000200745 polb_003580241 rdrp_32202687 polb_007102163 polb_001064263 polb_015514497 polb_014364115 polb_000396610 polb_000490625 polb_000061559 polb_000061999 rdrp_36505302 polb_015907472 polb_000230224 polb_002503270 polb_000194282 polb_008001141 polb_000503865 polb_002682999 polb_000030837 polb_000328966 Rep_38177659 polb_000129518 polb_000079365 polb_000229407 polb_004312996 polb_000912507 polb_000435873 polb_000239928 polb_002035391 polb_007503502 polb_000248170 polb_000073352 polb_000054135 polb_000249074 polb_000232032 polb_000073545 polb_000026723 polb_003145223 rdrp_77677770 rdrp_84897402 polb_004775027 polb_000240662 polb_007423474 polb_000844241 polb_000673383 polb_000079078 polb_000110630 rdrp_49554577 rdrp_105714054 polb_000236849 rdrp_59731273 rdrp_42335229 rdrp_107558617 polb_000251540 rdrp_77677810 polb_000495602 CEE J o u r n a l P r e -p r o o f.
Literature Search and Data Management A thorough literature search was conducted using several electronic bibliographic databases with different keywords such as "Dechlorane" OR "Flame retardants" AND "food" OR "fish". At present there are still few publications reporting data on DRC food contamination. In this review, only data on food are considered. Data on food supplements, animals or parts thereof not used as food were not included. In particular, for fish and seafood, both the work on dietary intake of DCRs and those carried out on edible species for environmental monitoring purposes were considered. The tables report data on the most evaluated DRCs in the literature such as Mirex, Dec-601, Dec-602, Dec-603, Dec-604, CP, DP-syn and DP-anti. For the calculation, raw data present in the supplementary files, if available, were used, excluding the values not consistent with the criteria described above.
In animal fat and vegetable oil Dec-602 is the compound that gave the highest mean value among the other DRCs in Lebanese vegetable oil (3. 0-11. 8 pg g -1 ww LB-UB)  [49] , followed by data from sub-Saharan countries (from 1. 5 to 3. 0 pg g -1 ww)  [51]. The highest mean value of Dec-603 was recorded in samples from Benin (4. 5-6. 6 pg g -1 ww LB-UB). The greatest contamination of CP was measured in Lebanon (3. 2-3. 7 pg g -1 ww LB-UB)  [49] , Mirex showed the higher value (0. 43 pg g -1 ww) in animal fat from Belgium  [39]. Dec-601 was always below the LOQ. Finally, in the "other foods" category, DRCs were quantified only in two studies: one in bread and cereals in Latvia  [40]  one in products from on sub-Saharan countries  [51]. In the first work, both Dec-602 (3. 83-3. 86 pg g -1 ww LB-UB) and Dec-603 (3.
PCR products were purified using USB® ExoSap-IT® (Thermo Fisher Scientific) in accordance to the manufacturer's instructions and sequenced using the same PCR primers plus two additionally MC5 and MC3 (CCTGAGAAACGGCTACCA-CATCCA, GATTAGCCTGACAGATCACTCCACGA respectively) primers used to overlap the obtained amplification fragments  [23]. Sequencing was performed using a BigDye® Terminator v3. 1 Cycle Sequencing kit (Applied Biosystems Inc. , California, USA) in an ABI 3130 automatic DNA analyzer (Applied Biosystems Inc. ™) at the Human Genome Research Center (HGRC), at the University of São Paulo. The obtained sequence was visualized, assembled and edited using BioEdit 7. 1. 3. 0 software  [24]. A standard nucleotide BLAST search was conducted to verify the similarity of the sequence obtained in this study with other sequences available in GenBank  [25].