dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The BOUSSOLE mooring station provides a dataset with a high temporal resolution at a fixed point, while the BGC-Argo floats at least allow the partial resolution of the spatial variability (certain regions contain no floats), although with a lower spectral resolution than that of BOUSSOLE. The OASIM model is useful for the integration of all this information. 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 the 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).
https://doi. org/10. 5194/os-2020-108 Preprint. Discussion started: 25 November 2020 c Author(s) 2020. CC BY 4. 0 License. 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 Cote d'Azur dataset is much larger than that of the ERA-Interim coarse data at 1 degree, as previously reported by Stopa and Cheung (2014)  using data from the U. S. National Data Buoy Center. Large differences occur more frequently during the cold season (from November to April).
https://doi. org/10. 5194/os-2020-108 Preprint. Discussion started: 25 November 2020 c Author(s) 2020. CC BY 4. 0 License. relative humidity [%] following ECMWF ERA-Interim * , the relative humidity [%] is computed as the ratio between the water vapour pressure (which depends on the 2-metre dewpoint temperature, d2 m [K]) and saturation water vapour pressure (which depends on the 2-metre temperature, t2 m [K]), multiplied by 100 *https://www. ecmwf. int/en/faq/do-era-datasets-contain-parameters-nearsurface-humidity ozone [DU] ERA-Interim total column ozone -tco3 [kg m -2 ] divided by a factor of 2. 1414 x 10 -5.
400 https://doi. org/10. 5194/os-2020-108 Preprint. Discussion started: 25 November 2020 c Author(s) 2020. CC BY 4. 0 License. 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 of the French Oceanographic Fleet (https://doi. org/10. 18142/1). This study was supported by the following research projects funding BGC-Argo floats: NAOS (funded by the Agence Nationale de la Recherche in the frame of the French 'Equipement d'avenir' program, grant agreement ANR J11R107-F), remOcean (funded by the European Research Council, grant agreement 246777), Argo-Italy (funded by the Italian Ministry 440 of Education, University and Research), and the French Bio-Argo program (Bio-Argo France; funded by CNES-TOSCA, LEFE Cyber, and GMMC).
We acknowledge the ECMWF for the ERA-Interim dataset (source: www. ecmwf. int). These data are published under a Creative Commons Attribution 4. 0 International (CC BY 4. 0), https://creativecommons. org/licenses/by/4. 0/. ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising 445 from their use. Authors thank Valentina Mosetti (OGS) for figures editing.
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  SEDOO/Observatoire Midi-Pyrénées)  for their help in accessing the data.
Code availability The OASIM Fortran code is publicly downloadable, together with the reference input data, from http://gmao. gsfc. nasa. gov/research/oceanbiology Data availability ECMWF ERA-Interim: https://www. ecmwf. int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim BOUSSOLE mooring buoy: http://www. obs-vlfr. fr/Boussole/html/project/boussole. php Cote d'Azur mooring buoy: https://mistrals. sedoo. fr/HyMeX/ BGC-Argo: https://www. ocean-ops. org/board?t=argo http://www. coriolis. eu. org/Data-Products http://www. argodatamgt. org/.
Slingo model (cloud cover and cloud liquid water path), OASIM requires the following atmospheric input data: surface pressure,  [mb], wind speed,  [m/s], relative humidity, ℎ [%], precipitable water (absorption by water vapour),  [cm], and ozone,  [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 (, , ℎ, and ) were acquired from the National Centers for.
Data collection We collected data from the Aude and Var administrative departments (southern France), both subject to major floods that have impacted the winegrowing sector  (Vinet, 2003; Bauduceau, 2001; Collombat, 2012; Chambre d'agriculture Var, 2014). Data were collected from several sources in order to identify common patterns and plausible hypotheses related to CWSs. The sources include qualitative interviews with winegrowers and heads of cooperative wineries  (RETINA, 2014 (RETINA, -2016) )  in both departments, that provided useful insights into soil productivity, the stages of production, the behavior of the agents, plausible business sizes and governing rules. On these subjects we also counted on the works of  Biarnès and Touzard (2003) ;  Chevet (2004) ; Agreste (2010);  Battagliani et al.
Code and data availability. The COOPER model and the data needed to perform the present analysis are available online at the CoMSES computational model library (https://www. comses. net/codebases/6038/releases/1. 0. 1/). Author contributions. DNM, FG, and JR developed COOPER model, DNM and FG performed the analysis and drafted the first version of the manuscript. All authors discussed the results and edited the manuscript.
Acknowledgements The daily board of the  Medical Ethics Committee Erasmus MC  ( METC-2015-306 ) of Rotterdam, The Netherlands, has reviewed the above mentioned research. As a result of this review, the Committee informs you that the rules laid down in the Medical  Research Involving Human Subjects Act  (informed consent), do not apply to this research proposal. The samples are from anonymized leftover material from patients for the purpose of improving diagnostics. All experiments were performed in accordance with relevant guidelines and regulations.
Vol. :( 0123456789 ) Scientific Reports | (2021) 11: 947 |  https: //doi. org/10. 1038/s41598-020-80363-5 www. nature. com/scientificreports/ Experiment 3: Further validation on the NCBI dataset. We downloaded data from NCBI 30 on March 17th, 2020, with the following query: "virus" AND host = "homo sapiens" AND "complete genome", restricting the size from 1000 to 35,000 bps (NCBI-B). The query returns 20,603 samples, of which only 32 belong to SARS-CoV-2, and 20,571 are from other taxa, including Hepatitis B, Dengue, Human immunodeficiency, Human orthopneumovirus, Enterovirus A, Hepacivirus C, Chikungunya virus, Zaire ebolavirus, Human respirovirus 3, Orthohepevirus A, Norovirus GII, Hepatitis delta virus, Mumps rubulavirus, Enterovirus D, Zika virus, Measles morbillivirus, Enterovirus C, Human T-cell leukemia virus type I, Yellow fever virus, Adeno-associated virus, rhinovirus (A, B and C), for a total of more than 584 other viruses (not considering strains and isolates).
In addition, we downloaded 26 sequences from GISAID repository of other hosts (manis javanica, rhinolophus affinis, canine and felis catus) to make a comparison in the sequences from experiment 2 and 3. Laboratory validation of the candidate primer set. Experiment 5: Design of the candidate primer set. After the analysis carried out on the deep learning model, we ran an analysis with Primer3plus 47 , to see which of the sequences could be used as a forward primer, using sample NCBI NC045512. 2 as the reference SARS-CoV-2 sequence. We uncover the sequence TAG CAC  TCT CCA AGG GTG  TTC that shows a frequency of appearance of 99. 57% in viral genomes available from different countries in GISAID 31 and 100. 0% in the NCBI 30 datasets. Using the reference SARS-CoV-2 sequence, we identify that this discovered sequence is located between nucleotides 25,604 and 25,624 in the ORF3a gene.
In SARS-CoV, this gene encodes a protein of 274 aa, that is related with necrotic cell death 48,49 , chemokine production like interleukin 8 (IL-8) and RANTES/CCL5, NFκB activation resulting in an inflammatory response 50 and may play an important role in the virus life cycle 51. We design a specific primer set for detection of SARS-CoV-2 using Primer3plus 47. We use TAG CAC TCT CCA AGG GTG TTC as forward primer and  GCA AAG CCA AAG CCT CAT  TA as reverse primer, obtaining an amplicon size of 179 bps. Then, we run an in-silico PCR test using FastPCR 6. 7 52 with default parameters in NC045512. 2 used as a reference SARS-CoV-2 sequence, this yields Tm = 56. 2 • C for the forward primer, Tm = 53. 1 • C for the reverse primer and Ta = 58 • C. In addition, we calculated the frequency of appearance of different primers sets' sequences used in SARS-CoV-2 RT-qPCR tests developed by WHO referral laboratories and compared it to our primer design sequences in 52,645 sequences from the GISAID repository and the 583 samples of different coronaviruses from the NGDC dataset from experiment 1.
The average amount of total base raw data was approximately 9 GB. Comparative analysis of the ScGAMYB sequence revealed its high level of homology to wheat and barley orthologues. Single nucleotide polymorphisms (SNPs) detected among rye inbred lines allowed the development of allele specific-PCR (AS-PCR) markers for ScGAMYB that might be used to detect this gene in wide genetic stocks of rye and triticale. Segregation of the ScGAMYB alleles showed significant relationship with α-amylase activity (AMY). Conclusions: The research showed the strong similarity of rye GAMYB sequence to its orthologues in other Graminae and confirmed the position in the genome consistent with the collinearity rule of cereal genomes. Concurrently, the ScGAMYB coding sequence (cds) showed stronger variability (24 SNPs) compared to the analogous region of wheat (5 SNPs) and barley (7 SNPs).
Sequence and structure of ScGAMYB examined in rye inbred lines of various origins The GAMYB sequence was established for six rye inbred lines (DS2, RXL10, M12, L35, 541, and Ot1-3) collected from the Department of Plant Genetics, Breeding, and Biotechnology of the West-Pomeranian University of Technology, Szczecin. Four of these lines were used in the entire transcriptome sequencing experiment. RNA was isolated from six plants of the DS2 and M12 and from four plants of the L35 and RXL10. RNA integrity number (RIN) measured before sequencing was appropriate for 17 samples, indicating good RNA quality. RIN of three RNA samples was below 5. 5 and these ones were excluded from the experiment. The average amount of total base raw data was: 9. 6 Gb, 9. 4 Gb, 8. 4 Gb, and 8. 1 Gb for line M12, DS2, L35, and RXL10, respectively.
Read count ranged from 68, 259,118 to 115,869,622 paired end reads. Raw data were subjected to quality control. More than 95% bases were of high quality (Table  1 ). The raw reads data for 17 samples in FASTQ format were deposited in the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the accession number from SRX2636904 to SRX2636920. After removing the adapter and low quality sequences from the raw data, from 66. 7 to 112. 9 million high-quality reads were retained (Table  1 ). The quality of the sequences measured with Q30 ranged from 96. 95 to 97. 75% (Table  1 ). The 17 set clean reads of the four genotypes were de novo assembled into one transcriptome with the Trinity program  [20]. The constructed reference sequence pool counted 218,935 transcripts with a total nucleotide number of 183.
8 million (Table  2 ). The average contig length was 840 nt, with a minimum of 200 and a maximum of 16,024 nt. The statistics N50 was 1366 base pairs, including all transcripts obtained (Table  2 ). About 150,000 genes with longest isoform was identified (Table  2 ). Functional annotations performed using GO and eggNOG databases revealed 32,152 transcripts (15%) linked to biological processes, 25,998 (12%) -to cellular components, and 25,281 (11%) -to molecular function. A number of 135,503 transcripts were not assigned any function. Based on the annotation, several contigs identified as MYB were identified, but it was not known which might correspond to the GAMYB, so it was decided to search the whole database of 218,935 transcripts at the nucleotide sequence level, to reveal a contig containing the GAMYB TF sequence.
The cds of the GAMYB identified in barley (AY008692. 1) was used for this purpose. The alignment performed by the Geneious software version 10. 2. 4. with megablast tool, resulted in identification of contig No. c81081_g3_i1 containing the GAMYB cds (Additional file 1). The annotation assigned to the contig pointed to the following functions: evidence of DNA binding, evidence of chromatin binding, evidence of transcription factor activity binding DNA sequence, nucleus evidence, transcription regulation, DNA template evidence, evidence of gibberellic acid mediated signaling pathway, pollen cell evidence of differentiation positive regulation, ABA activated signaling pathway, positive regulation of evidence for programmed cell death, positive transcription regulation, evidence based on DNA template, negative regulation of growth evidence, evidence of GA biosynthesis.
The length of contig c81081_g3_i1 was 1816 nt and its sequence contained both exon 2, 3 and 4, together with the 5 'UTR sequence. As this contig did not cover the entire sequence of the GAMYB described in wheat and barley (lack of exon 1), the search of the contig database was repeated, using only the first exon sequence identified in barley (AY008692. 1). Based on this activity, contig c81081_g4_i1 (Additional file 1) was identified, containing only the exon 1 sequence. Mapping both contigs to the GAMYB sequence of related species and the rye sequence of the Lo7 line  [21]  allowed to assemble the full mRNA sequence of the ScGAMYB. The alignment of the two rye ScGAMYB cDNA sequences gave a total length of 2241 nt for M12 and DS2 lines. Sequences for RXL10 and L35 were shorter. Based on the raw data of reads, length of exon 1 in RXL10 and L35 lines was determined to be 240 and 54 nt respectively.
These four sequences contained the entire cds of ScGAMYBof 1659 nt. The 769 nt fragment of ScGAMYB of lines 541 and Ot1-3 was amplified from genomic DNA, with primers pair F: 5′-tatgcaccacagctttcagc-3′, R: 5′-gtgcaggaggaattttggag-3′, because these lines were not included in the rye transcriptome study. This fragment corresponds to exon 3 of the studied gene. ScGAMYB nucleotide sequences showed 99-100% identity with orthologous genes of wheat and barley deposited in the NCBI database (Table  3 ). The resulting ScGAMYB mRNA sequence was aligned to the whole genome shotgun sequence assembly of rye inbred line Lo7  [21]. The Sc170168 was a DNA fragment (scaffold) containing the entire sequence of ScGAMYB located on chromosome 3R at position 92. 15706326 cM on the rye genetic map constructed previously  [21].
The identity coefficients between the six rye inbred lines GAMYB sequences and those found within the scaffold were from 96. 56 to 97. 46%. The structure of ScGAMYB (Fig. 1 ) was determined by comparing the sequencing nucleotide data reported in this study with the rye DNA sequences presented by Haseneyer et al. [12]. It has a total length of 3700 nt and contains four exons and three introns similarly to the cds of orthologous genes in wheat and barley. The particular exons of ScGAMYB sequence contain: 248 nt, 387 nt, 1009 nt, and 597 nt being interspaced by three introns of 618 nt, 829 nt, and 82 nt lengths with the cds spanning from exon 2 to exon 4 (Fig. 1 ). Upon comparing the mRNA sequence of the ScGA-MYB of the DS2, RXL10, L35, and M12 rye lines, we noted that exon 3 has the highest variability (Additional file 2, Table  4 ).
This 1659 nt cds was translated into a 552 amino acid (aa) protein sequence (Additional file 3, Table  4 ) using Geneious software. The analysis revealed that some SNPs resulted in a change of the aa sequences at positions 76 (F/N), 87 (A/I), 233 (P/Q), 367 (S/C), and 459 (S/P) (Additional file 3, Table  4 ). In addition, the protein structure prediction conducted by EMBOSS 6. 5. 7 plug showed that these polymorphisms affected the secondary structure of ScGAMYB (Fig. 2 ). Moreover, two SNPs resulted in a change of protein sequence in the 44-93 aa region that codes the functional MYB domain and is responsible for DNA-binding. The second region of DNAbinding domain (97-144 aa) was highly conserved. The similarity relationships between rye inbred lines and related species (T. aestivum, H. vulgare, A.
The set of 4181 (2919 + 1262) DArTseqs and ScGA-MYB segregation was subjected to grouping analysis. The maximum LOD value at which ScGAMYB remains associated with most markers was 20. After removing the loci with identical segregations, the linkage group consisted of 1843 markers and was subjected to the mapping procedure. From the set of markers linked with ScGAMYB in Multipoint software, 331 "delegate markers" were selected. Only the delegate markers were used as a skeleton map construction (Fig. 4 , Additional file 4). Polymorphic AS-PCR markers of ScGAMYB were used to access segregations within the three rye-mapping populations. The segregation ratios were consistent with monogenic model (1:1, in RIL-K and RIL-L; 3:1, in BSR-F 2 ). ScGAMYB was mapped on the proximal part of the long arm of chromosome 3R between DArTseq-SNP 3586202_5:C > T and DArTseq-silico 5,210,184 on the BSR-F 2 map (72.
Due to the lack of a complete reference genome of rye, transcripts were assembled de novo using the Trinity package. We obtained over 96% bases of high quality (Table  1 ) for all lines. Low quality bases and adapter sequences were trimmed before further analysis. N50 statistics are widely used to assess the quality of the assembly  [20]. The observed value of N50 (1366 nt) was higher than the average length of the contig (840 nt) suggesting a good assembly quality  [25]. The assembly of rye sequences is associated with various challenges related to the large size of the genome, high repetition rate, and the lack of a reference genome. Although the de novo transcriptome assembly approach is beneficial, because it allows detection of new gene isoforms, as well as a better understanding of the mechanisms of alternative gene assembly in the species studied  [26] , there is also a risk of incomplete assembly and fragmentation of transcripts  [27].
First, the poly-A containing mRNA molecules using poly-T oligoattached magnetic beads were purified. The mRNA was fragmented into small pieces using divalent cations under elevated temperature. The cleaved RNA fragments were copied into the first strand of cDNA using reverse transcriptase and random primers. These cDNA fragments went through an end repair process, the addition of a single 'A' base, and then ligation of the adapters. The products were then purified and enriched by PCR to create the final cDNA library. Sequencing was performed on an Illumina HiSeq2000 based on the Solexa's Sequencing-by-Synthesis method. Illumina reads of all samples had been submitted to the Sequence Read Archive at the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/sra), under accession numbers from SRX2636904 to SRX2636920, under BioProject PRJNA371298 (ID: 371298).
Sequence quality control was performed at FastQC software (http://www. bioinformatics. babraham. ac. uk/ projects/fastqc). Trimmomatic (0. 32) (http://www. usadellab. org/cms/?page=trimmomatic) -multithreaded command line tool was used to trim and crop Illumina (FASTQ) data as well as to remove adapters. De novo reconstruction of transcriptomes from RNA-seq data was performed using Trinity (http://trinityrnaseq. sourceforge. net) [26]. RSEM (1. 2. 15) software (http://deweylab. biostat. wisc. edu/rsem/) tool was used for quantifying transcript abundances from RNAseq data. Transcripts annotation based on BlastX bioinformatic tool (http:// blast. ncbi. nlm. nih. gov/Blast. cgi? CMD = Web&PAGE_ TYPE = BlastDocs&DOC_TYPE = ProgSelectionGuide) was done. This program compares the six-frame conceptual translation products of a nucleotide query sequence (both strands) against a protein sequence database (Gene Ontology, EGGNOG).
Finally, the contigs covering the entire cds of ScGAMYB was revealed using Geneious software version 10. 2. 4. The same software was used to map the fragment of ScGAMYB to the whole genome shotgun sequence assembly of rye inbred line Lo7  [21]. EMBOSS package 6. 5. 7 plug in Geneious software was applied for protein structure prediction. This approach allowed verification of the gene structure and its chromosomal location.
SNPs identification Identification of SNPs within the cds of the ScGAMYB of six rye lines was carried out with the Geneious software version 10. 2. 4. From the readings deposited in the SRA database, those that covered the contigs containing the sequences of the described gene were selected. The reading sequences from the RXL, DS2 and L35 lines were then aligned to the sequences of the assembled contigs of the M12 line to identify point mutation sites. The sequences of lines 541 and Ot1-3 were also aligned to the sequence of contigs assembled for the M12 line. The SNPs were described by their position in the nucleotide sequence and the type of mutation. It was then checked whether the mutation changed the type of aa in the protein sequence.
In comparison to the array version of DArT, DArTseq results in higher marker densities and two types of data are generated: SilicoDArT and SNP  [41]. SilicoDArTs are microarray markers that are dominant and scored for the presence or absence of a single allele (restriction fragment). DArTseq based SNPs are co-dominant markers utilizing differences in the sequence of restriction fragments. The segregations of DArTseqs in BSR mapping population were applied for mapping the ScGAMYB locus. The JoinMap5 package  [42]  was used to establish a group of markers linked to the ScGamyb (grouping tree command) and to remove identical loci to simplify the mapping procedure. The genetic map construction was conducted using Multipoint 3. 2 software  [43]. The group of DARTseq markers in accordance to "b, d" (GAMYB segregation) and "a, h, b" schemes were used to construct the genetic map.
The "order" command was applied for marker linkage groups formed at a maximum threshold level of recombination frequencies at 0. 005. The "control of monotony" command was used for detection and removing problematic markers that caused neighborhood instabilities. Finally, the ordering was repeated with the Kosambi mapping function and a genetic map was generated. The genetic maps of the RIL-K and RIL-L populations were constructed previously using classic DArTs and PCR markers  [22]. Segregation of the ScGAMYB was added to the set of RIL-K and RIL-L markers and remapped. The mapping procedure applied Multipoint 3. 2 software as with the BSR population map. To reduce the inflation of genetic distances on a high-density genetic map, the average length of the consensus map  [22]  was used for scaling obtained linkage groups as previously described  [44, 45].
Statistical analysis and QTL mapping The statistical relationship between the segregation of molecular markers and AMY was analyzed with the nonparametric Kruskal-Wallis' test using STATISTICA v. 12. 0 software (http://www. statsoft. com), and by QTL mapping performed using SMA based on Fisher's test and CIM procedure with the software package WinQTL Cartographer 2. 5  [47] , assuming significance levels p ≤ 0. 05.