dataesr/scientific-paragraphs-categorization huggingface dataset:
PacBio long reads were used in the next steps to generate haplotypes of these variations to split the genomic locations that had been merged. Long reads were aligned to contigs, and the alignments of double coverage contigs were processed with HapCut2 94 (v. 1. 0) using the following parameters: extractHAIRS -ont 1 and HAPCUT2 -ea 1. For each contig, a haplotyped VCF file was produced. Some of these files contained more than one haplotypic segment. These contigs have been split according to the haplotypic segment information found in the VCF file, using an in-house script. The resulting haplotyped VCF files were then processed with fgbio (v. 0. 7. 0 using standard parameters)  95  to generate VCF files, separated by haplotype. These VCF files and the reference were used to produce haplotypic contigs using vcf-consensus from the bcftools 96 package v.
Repeat annotation and TE analysis. To search for repeated elements, the sterlet genome and raw Illumina reads were used as input. The assembled genome was used in the RepeatModeler open-1. 0. 11 tool 100 with standard settings. LTRretriever v. 2. 5 (ref. 101 ) was used to search for full-length LTR elements, and the data were used as input derived from the LTRharvest 102 (-similar, 90; -vic, 10; -seed, 20; -seqids, yes; -minlenltr, 100; -maxlenltr, 7,000; -mintsd, 4; -maxtsd, 6; -motifmis, 1) and LTR_FINDER  103  (-D, 15,000; -d, 1,000; -L, 7,000; -l, 100; -p, 20; -CM, 0. 9) tools. To exclude non-LTR (-linelib) and DNA transposons (-dnalib), protein sequences of these TEs from the RepeatPeps database of the RepeatMasker tool  104  were used. This also excluded protein sequences that were not related to TEs.
The SWISS-PROT sequence library  105  was also used (-plantprotlib). The sequences obtained using the previous steps were combined into a single FASTA file using CD-HIT-est 106 (-aS, 1; -c, 1; -r, 1; g, 1; p, 0). The resulting FASTA file was aligned against the RepBase v. 24. 07 (ref. 107 ) and FishTEDb 108 databases using blastn (-evalue, 10 × 10 -100 ) and against SWISS-PROT and RepeatPeps using blastx (-evalue, 10 × 10 -100 )  109  to filter incorrectly annotated sequences. Raw reads were used in the TAREAN tool  110  , which is part of RepeatExplorer 111. The reads were first trimmed using the fastp tool  112  to remove low-quality and adapter sequences (detect_adapter_for_pe -g -c -l 50 -5 -3), after which RepeatExplorer was used with standard settings. We saved only satellite sequences with high confidence and added them to the library of repeated sequences.
In addition, using REXdb 113 , a database of TE domains implemented in RepeatExplorer2, the correctness of the previous TE annotation was further verified. The content of repeated elements in the genome was estimated using RepeatMasker open-4-0-9-p2 (-s -no_low -lib). To build the Kimura plot, the createRepeatLandscape. pl script from the RepeatMasker tool was used. To analyse the expression of TEs, raw reads from RNA-seq were used. The reads were trimmed using fastp (-detect_adapter_for_pe -g -c -5 -3) and then aligned against the FASTA file containing TE sequences obtained in the previous step using bowtie2 v. 2. 3. 5. 1 (ref. 114 ) (-very-sensitive -dovetail). The raw read count for each superfamily was calculated. The raw counts were normalized to the total number of sequences (reads per million, the number of aligned reads for each superfamily × 1000000/total number of reads), and then the proportion of superfamilies in the transcriptome was calculated (reads per million × 100/ total number of aligned reads).
To compare the RNA-seq data with the genome proportion of the respective TE superfamily, the proportion of TEs in the genome was calculated (the number of nucleotides occupied by superfamily in the genome × 100/total nucleotides occupied by TEs in the genome). The results were transformed to the log 10 values and visualized with ggplot2 115 and MATLAB  116. Genome annotation. Genome annotation was done by an in-house pipeline (Supplementary Fig. 25 ) improved from a previous version  24. First, the pipeline assessed the assembly quality using BUSCO on the basis of the Actinopterygii odb9 database  117. The parameter -long was used for the first training of AUGUSTUS v. 3. 2. 3 (ref. 118 ). The pipeline then identified and masked repeat elements from the assembly. Repeat elements were identified using blastx v.
2. 2. 28+ with the protein repeat database RepeatPeps (http://www. repeatmasker. org/), and using RepeatMasker with two nucleotide repeat databases, one produced by RepeatModel (http://www. repeatmasker. org/), and the other an in-house fish repeat database combining our annotation and the one from Shao et al. 108. Simple and low-complexity repeats were then softmasked, while those with known family were hardmasked. After repeat masking, the pipeline collected gene evidence from homology annotation, de novo annotation and RNA-seq annotation. For homology annotation we first pooled protein sequences from SWISS-PROT (www. uniprot. org) and 13 Ensembl genomes (v. 95, http://www. ensembl. org): human (Homo sapiens), mouse (Mus musculus), coelacanth (Latimeria chalumnae), spotted gar (Lepisosteus oculatus), zebrafish (Danio rerio), cod (Gadus morhua), tilapia (Oreochromis niloticus), medaka (Oryzias latipes), platyfish (Xiphophorus maculatus), fugu (Takifugu rubripes), tetraodon (Tetraodon nigroviridis), stickleback (Gasterosteus aculeatus) and sea lamprey (Petromyzon marinus), and reduced the redundancy using CD-HIT (http://www.
All gene evidence obtained from the three kinds of annotation was collected and transferred to EVidenceModeler v. 1. 1. 1 (ref. 126 ), where gene models confirmed by all lines of evidence were extracted as high-quality gene models. They were used for the second training of AUGUSTUS. Finally, the AUGUSTUS specially trained for sterlet took all the hints from BUSCO, repeat masking and all three annotations to predict the final set of gene models for sterlet. Some broken or artificial chimaeric gene models were found and replaced by comparing the AUGUSTUS prediction with the homology gene evidence. Low-quality gene models were removed afterwards. To assign gene symbols, their protein sequences were blasted to the SWISS-PROT database (www. uniprot. org/e) (blastp v. 2. 2. 28+ (ref. 127 ); percentage of identical matches, >20%; e-value, <1 × 10 -5 ), and the symbol of the best hit was taken (https://biodbnet-abcc.
Codon alignments for the protein-cDNA sequence pairs were constructed using pal2nal v. 14 (ref. 148 ). The resulting sequences were aligned by MUSCLE  137  (option: -fastaout), and poorly aligned positions and divergent regions of cDNA were eliminated by Gblocks v. 0. 91b (ref. 149 ) (options: -b4, 10; -b5, n; -b3, 5; -t = c). An in-house script was used to convert the Gblocks output to paml format. For the generation of a phylogenetic tree as input for the detection of positive selection, sequences from all homologous genes, detected by inparanoid, were concatenated after the selection of conserved blocks by Gblocks and aligned using MUSCLE. The tree was generated using Phylip v. 3. 696  150  with Callorhinchus milii (comparison1-3) or L. chalumnae (comparison4) as the outgroup (Supplementary Table  14 ).
For differential gene expression analysis, reads were aligned to the sterlet genome using STAR (-quantMode GeneCounts)  152. Owing to the sequence similarity between ohnologues, the mapping results were further filtered for uniquely mapped reads and reads with no mismatches to be able to obtain a reliable read assignment. To compare expression between different genes from an ohnologue pair, we used transcripts per million (TPM) values. For further analyses, genes not expressed (TPM < 5) in both ohnologues and in all included organs or ohnologue pairs without sufficient discriminating single nucleotide polymorphisms were excluded. Ohnologues were considered to be expressed at different levels if the absolute value (ohnologue1(log 2 TPM + 1) -ohnologue2(log 2 TPM + 1)) was greater than one (representing a twofold difference) in at least two different sterlet organs and developmental stages.
For functional clustering, the web tool DAVID (https:// david. ncifcrf. gov/) was used, on the basis of human orthologues and all ohnologues as background. RAD-tag sequencing and analysis of sex-specific tags. The genomic DNA of 31 females and 30 males was extracted from 90% ethanol-preserved fin clips using a classical phenol/chloroform protocol. The sterlet RAD-tag library was built according to standard protocols  153  , using Sbf1 as a single restriction enzyme, and sequenced on a single lane of Hiseq 2500, using the v4 SR100nt mode. The resulting read file was then demultiplexed using the process-radtags. pl script of STACKS software v. 1. 44 (ref. 154 ) with default settings. Demultiplexed reads were analysed with RADSex v. 0. 2. 0  155. RADSex sorts reads from the demultiplexed dataset into groups sharing the exact same sequence, and reads that would belong to the same polymorphic locus using standard analysis software are simply split into multiple markers.
As a result, RADSex markers are non-polymorphic, thus allowing straightforward presence-absence comparison between individuals. First, a table of depth for each RADSex marker in each individual from the dataset was generated using radsex process with default settings. The distribution of markers in males and females was then computed with radsex distrib, using a minimum depth of 10 (--min-cov 10) to consider a marker present in an individual, and a tile plot was generated from this distribution using the plot_ sex_distribution() function from RADSex-vis (https://github. com/RomainFeron/ RADSex-vis). The same analysis was performed with minimum depths of 1, 2 and 5, but the results were not qualitatively affected. A total of 176,735 markers were obtained that were present in at least one individual with a minimum depth of 10.
Acknowledgements This work was supported by grants supplied by the  Deutsche Forschungsgemeinschaft  to  M. Schartl  (nos. DFG SCHA 408/14-1 ,  SCHA 408/10-1  and  Scha408/16-1 ),  J. M. W. ( DFG WO-2165/2-1 ) and  A. Meyer  ( DFG Me1725/24-1 ); by the  German Federal Ministry of Food and Agriculture  through the  Federal Office for Agriculture and Food  (grant no. 2816ERA04G ) to  M. Stöck , S. W. , J. G. and M. Schartl; by funds from the  Agence Nationale de la Recherche  (grant no. ANR-13-ISV7-0005 ,  PhyloSex  project) to Y. G. ; and by funds from the  Russian Science Foundation  to V. T. , A. M. , I. K. and D. P. (grant no. RSF 18-44-04007 ). We thank the  Leibniz-IGB  for supporting the sterlet genome sequencing project. The GeT core facility was supported by  France Génomique National  infrastructure, funded as part of the  Investissement d'avenir programme , managed by the  Agence Nationale pour la Recherche  (contract no.
Data availability The Acipenser ruthenus Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number VTUV00000000. The version described in this paper is version VTUV01000000. Genomic and transcriptomic reads are deposited in the Sequence Read Archive under accession numbers SRR10188515-10188518 and SRR11013451-11013458.
This data article contains milling itineraries and granulometric properties of the resulting powders obtained from a collection of by-products from crops (flax fibers, hemp core, rice husk, wheat straw) and woods (pine wood pellets, pine bark, pine sawdust, Douglas shavings, chestnut tree sawdust) representative of currently used lignocellulosic biomass. Samples provided in the form of large pieces (hemp core, pine bark, Douglas shavings) were successively milled using different mills to progressively reduce the matter into coarse, intermediate and finally fine powders. The other samples, supplied as sufficiently small format, were directly processed in the fine powder mill. The machine characteristics and their operating parameters were recorded. The granulometric properties of the powders were analyzed with a laser granulometer and the main indicators related to the particle size distribution (PSD) are presented: (i) d10, d50 (or median diameter) and d90 which are the 10th, 50th and 90th percentiles of the cumulative volume distribution; (ii) the span, which evaluates the width of the particle size distribution; (iii) the calculated specific surface area of the powders which represents the sum total surface exhibited by the particles per unit of gram and for some powders.
Data format Raw and analyzed Parameters for data collection Oven or microwave dried samples were milled to reach median particle sizes in between 10-20 μm. Depending on the input size of samples, 3 successive steps of milling (coarse, intermediate, and fine milling) or 1 (fine milling) were applied. The kinetics of particle size reduction were measured by laser granulometry (dry or liquid mode) after periodical sampling. PSD indicators were recorded.
Description of data collection Data from the milling of 9 different biomass samples (crop and wood by-products) were collected. Table  1 : Processing parameters (processing itineraries, machine operating conditions, milling time…) for 9 crop by-products itineraries and 10 wood by-products itineraries. Table  2 : Data from granulometric characterization of all ground powders (d10, d50, d90, span, Specific Surface Area).
Value of the Data • The fine milling of lignocellulosic biomasses is not trivial due to a strong anisotropy and an elastoplasticity behaviour  [ 5 , 6 ]. These data obtained with different types of mill and from various biomasses might guide the choice of starting feedstock and processing technologies to better exploit biomass potential according to the targeted applications as petrol replacers. • The data can benefit to scientists in the sectors of plant and wood products valorization, bio-based energy vectors  [7]  , bio-sourced chemicals  [8]  and materials  [9]. Companies manufacturing plant and wood based powders for food/non-food applications and mill suppliers may be interested. The information from the dataset can help to optimize the processes and the quality of the end-products from lignocellulosic biomass.
• The set of data can allow classifying biomasses according to the milling type and the milling time required to reach a targeted mean particle size. As milling equipment used are batch mills, the milling time is directly related to the milling energy. The dataset can be used to compare new biomass samples and other types of mill. It can also serve as a basis for understanding the mechanism of comminution. • According to the nature of the starting biomass material and the type of mechanical load applied in the mill, variability in particle characteristics can be introduced  [10]. This variability influences the properties of the products from lignocellulosic biomass. These data can be used either to produce tailor-made powders for targeted applications or to better control the quality of the end products.
Data Description Data are stored in two datasets (see Data accessibility in the Specification Table  above ): the first one contains wood by-products data and the second one crop by-products data. In each dataset, the data are gathered in four files. File Table  1  provides a precise description of milling itineraries associated with produced powder samples. File Table  2  reports the granulometric characteristics of all produced powder samples. The kinetics of particle size reduction for a subset of produced powder samples are gathered in file Table  3. An additional file Table  4  contains the whole set of full particle size distributions of all samples of Table  3. In this Excel file, each sample is stored in a separate thumbnail. In Table  1 , a milling itinerary is described by a set of unit operations and associated with a unique numerical identifier (column Experience number ).
PB4 in Table  3  corresponding to the final sampling time is the same sample than in Table  2. In Table  4 , are stored the whole set of full PSD for all samples and associated graphical representations of the evolutions of PSD along the fine milling process of the samples. Fig. 1  provides an example of the milling kinetics of Pine bark (Sample Product PB4) excerpt from Table  4  to reach a median particle size of 5. 614 μm. The corresponding milling times are also available in table  4. These data are stored in INRAE dataverse and replicated in @Web data warehouse  [4]  ( https: //www6. inra. fr/cati-icat-atweb/ ) in which the data structuration and vocabulary standardization are controlled by BIOREFINERY ontology  [ 3, 4 ]. BIOREFINERY ontology is available in INRAE dataverse ( https://doi.
Samples were dispersed in a mixture of purified water/ethanol (50% v/v) in the Hydro 20 0 0S system with a pomp speed of 30 0 0 rpm. The refractive index of the solvent mixture (1. 345) and sawdust (1. 53), were used to proceed the data based on the Mie Theory. Measurements were carried out in triplicates with a delay of 3 s between each measurement. Standard operating procedure 4: Measurement were realized in liquid way. Samples were dispersed in ethanol (96 % v/v) in the Hydro 20 0 0S system with a pomp speed of 30 0 0 rpm. The refractive index of ethanol (1. 45) and sawdust (1. 53), were used to proceed the data based on the Mie Theory. Measurements were carried out just once during the procedure. Standard operating procedure 5: Measurement were realized in liquid way in ethanol (96 % v/v) in the Hydro 20 0 0S system.
An ultrasonic treatment was applied prior to the measurement to deagglomerate the powder. 3 min of sonication using the granulometer internal ultrasonic probe at 100% of its power (75W) was applied to a suspension of approximatively 0. 1 g of powder in 200 ml of ethanol. The suspension was then stirred at 3000 rpm during 3 min to remove eventual bubbles prior to the measurement. Measurements were carried out in triplicate with a delay of 20 s between each measurement. The refractive index of ethanol (1. 45) and sawdust (1. 53), were used to proceed the data based on the Mie Theory.
Data accessibility Data are accessible in a public repository Repository name: INRAE dataverse ( https://data. inrae. fr/ ) Data identification number: 10. 15454/S660LH, 10. 15454/YZJXET Direct URL to data: https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/S660LH (v9. 0) corresponding to wood by-products data, https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/YZJXET (v8. 0) corresponding to crop by-products data.
Results and discussion We selected 156 V. cholerae O1 isolates from European autochthonous cholera cases (Supplementary Data 1). We subsequently found that 12 of these isolates were actually from imported cases, or even from cases occurring in Africa and Asia. They were nevertheless included, to enrich the global phylogenetic context. We contextualized these sequenced V. cholerae O1 isolates within a global collection of 1168 7PET genomic sequences (Supplementary Data 1) and constructed a maximum likelihood phylogeny of 1324 genomes-including 172 European genomes-using 13,544 single-nucleotide variants (SNVs) evenly distributed over the non-repetitive, non-recombinant core genome (Supplementary Fig. 1 ). A time-scaled phylogeny was also constructed from 1283 of these 7PET genomic sequences (Fig.
This sublineage continued to circulate in Africa until the mid-1990s  10. All but one of the 22 European isolates collected in the early 1970s and studied here belonged to sublineage EUR1/AFR1. These isolates include four Eastern European isolates collected in 1970 that clustered together. In particular, all these isolates had a specific synonymous SNV (T → C) at nucleotide position 2955669 according to GenBank accession number LT907989. One was collected from Odessa in the Black Sea region (now Ukraine) and three were collected from the eastern part of former Czechoslovakia (now Slovakia). The first of these isolates is a representative of the cholera outbreak that began in Odessa in August 1970, with more than 300 cases reported for this year in Ukraine  12. The other three isolates are representative of a small outbreak (four cases and one death were reported to the WHO but underreporting by the authorities is suspected) that occurred in late October around Vojany, among workers involved in the building of a power plant  13.
Instead, the 1974 isolates were derived from Angolan strains isolated in 1971 and 1972. Interestingly, all but two of the 10 Portuguese and Angolan isolates sequenced had a non-synonymous SNV (G103A; alteration C03 according to the nomenclature of ref. 10  ) in the wbeT gene associated with the Inaba serotype (Supplementary Data 1). The other two isolates, obtained from two members of the same family (one case and one carrier) contaminated in Portugal in 1974, belonged to serotype Ogawa (wildtype wbeT gene). Both could be classified as true Ogawa revertants of unknown significance. Our phylogeny is consistent with the stationing of colonial troops in the city of Tavira, where the outbreak began, in 1974 (ref. 19 ). These troops had been traveling back and forth between Portugal and its African colonies, such as Angola, where 934 cholera cases were reported in 1974 (more than three times the number of cases observed in 1972 and 1973).
Comparison of genomic data with other typing schemes. Ribotyping was used in previous studies investigating the global population structure of V. cholerae O1 (refs. 26, 27 ) or to investigate 7PET European outbreak isolates  6, 7. In our dataset, ribotyping data were available for 51 isolates from the five sublineages introduced into Romania (Table  5  and Supplementary Data 1). Up to five ribotypes were observed among isolates from the same sublineage (EUR4/AFR6), whereas some ribotypes, such as B5a and B21a, were common to isolates from two or even three sublineages (B5a in EUR3/AFR4 and EUR4/AFR6 and B21a in EUR3/AFR4, EUR5, and EUR6/AFR8). This confirms the irrelevance of non-genomic methods for inferring true genetic relationships between 7PET isolates and for accurate studies of the spatiotemporal spread of 7PET.
Given these limitations, antimicrobial susceptibility testing (AST) should be used with caution for distinguishing and tracking 7PET strains over time. Antimicrobial resistance susceptibility testing was previously performed on hundreds of V. cholerae O1 isolates collected between 1977 and 1995 in Romania  5, 31. These studies documented a gradual increase in antibiotic resistance, with the acquisition of resistance to nitrofurans in 1990-1991, tetracycline in 1993-1994, cotrimoxazole in 1993-1995 and nalidixic acid in 1995. Our genomic and phenotypic data are largely consistent with both these previous studies. However, in our dataset, the first isolates resistant to nitrofurans-caused by point mutations in the VC_0715 and VCA0637 nitroreductase genes  11  -were isolated in 1991 (EUR5 sublineage) and not in 1990 (EUR4/AFR6).
Acknowledgements This study was supported by the  Institut Pasteur ,  Cantacuzino NMMIRD, Santé publique France , the  Fondation Le Roch-Les Mousquetaires , the  French government 's  Investissement d'Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' (grant number  ANR-10-LABX-62-IBEID ), and " Institut Convergence for the study of Emergence of Pathology Through Individuals and Populations " (INCEPTION, grant number  PIA/ANR-16-CONV-0005 ). D. C. was supported by a traineeship grant from the  Calmette and Yersin Program  of the  Institut Pasteur International Network. M. O. was supported by a mobility grant from the  Romanian Ministry of Research and Innovation CNCS -UEFISCDI , project no. 495/2018 , within PNCDI III. The  Institut Pasteur Biomics platform  is supported by  France Génomique  ( ANR10-INBS-09-08 ) and IBISA.