dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We acknowledge the financial support given to MG from project  S3-23000846  funded by  Région Auvergne-Rhône-Alpes  and  Fonds Européens de Développement Régional (FEDER). This study was designed to support the  FBEA  project ( Filière Bovins Engraissement Auvergne-Rhône-Alpes) in collaboration with ARIA-ARA (Association Régionale des Industries Agro-Alimentaires d'Auvergne-Rhône-Alpes ) and  Chambre Régionale d'Agriculture d'Auvergne-Rhône-Alpes. We especially thank the different  INRA  research units, namely  INRA-UE232 (Bourges) , for help with animal handling and data collection, as well as all the individuals involved, especially  Dr Gilles Renand. We thank  INRA Herbipôle  for animal management and slaughtering. We also thank the two anonymous reviewers for their helpful and constructive comments that greatly helped improve the presentation of this article.
Funding: This Study was funded by the Royal Society of Horse Encouragement in Morocco (SOREC). Acknowledgments: This study would not have been possible without the financial support and interest of the  Royal Society of Horse Encouragement in Morocco (SOREC) , the Fal Stud owner;  Azeddine Sedrati  who allowed us to carry out this study in his stud farm and all the people who have participated to this work. This study will be submitted as a part of a Ph. D. thesis by  Mohamed Dilai  to the  Hassan II Institute for Agronomy and Veterinary Medicine in Morocco.
Results Landscape of TF binding during flower development. To gain a comprehensive view of TF DNA-binding during flower development, we integrated ChIP-seq data for 15 key floral regulators, including 85 ChIP-seq datasets from 15 studies (Supplementary Table  1  and Supplementary Data 1), which can be assigned to five representative developmental stages, or to mixed tissues (Fig. 1a, b ). An analysis pipeline that consists of quality control, peak calling, and target identification to re-analyze all raw data in a standardized and uniform manner was developed to obtain comparable TF-binding maps (see Methods; Supplementary Fig. 1 ). In this regard, the IDR (Irreproducible Discovery Rate) framework  39  was adopted as a unified approach to derive highly stable thresholds based on reproducibility.
Principal component analysis (PCA) of ChIP-seq signal profiles revealed that replicate datasets, or datasets for TFs with similar functions, tend to group together (Supplementary Fig. 2 ). It is notable that our re-analysis recovers the majority of target genes from the correspondingly original studies (Supplementary Fig. 3a ) and reveals general outperformance in terms of identifying the true TF-bound genes by integration of differential expression data upon perturbation of the corresponding TF (Supplementary Fig. 3b ). Overall, the number of TF-binding sites (TFBSs; called "peaks") varies greatly for different regulators or for the same regulator across different stages (Fig. 1c ; Supplementary Data 2). BLR, LFY, and SEP3 show the highest number of binding sites, consistent with the fact that these TFs possess multiple and diverse roles in reproductive development  [40] [41] [42].
SEP3 gains significant binding sites across flower developmental stages, in agreement with its roles during floral organ development and with its expression levels (Fig. 1c ). On the other hand, AP1 loses binding sites during the developmental time-course, consistent with its important role as FM identity factor at early stages of flower development, and a more restricted expression at later time points (reducing the ChIP efficiency). We then classified potential target genes of the investigated regulators (Fig. 1d ; Supplementary Data 3) into protein-coding and miRNA gene loci. A target gene was counted only when a TF-binding event was present within 3 kb of the start of the gene. We found that most TF-binding peaks are located within 64 bp upstream of protein-coding gene TSSs (Supplementary Fig.
3b ). This observation suggests that miRNA-mediated regulation enhances the topological robustness  50  of the floral GRN. Note that colors representing stages are consistently used across all the figures if applicable. b Currently available genome-wide TF-binding maps for floral regulators. The datasets are roughly assigned to the five floral stages as defined in a; the source of ChIP-seq data is labeled below the bar and also indicated in Supplementary Table  1. c Bar chart showing the number of binding sites for the investigated TFs. TFs are colored according to the development stages. d Bar chart showing the percentage of target genes as defined in three classes: transcription factors (TF; blue), miRNA-related genes (MIRs; orange) and other protein-coding genes (gray). The number of target genes is shown above bars.
4f ). Organ-specific GRNs. A recent study  61  showed that DNAbinding specificity of homeotic MADS-domain proteins contributes to organ-specific gene regulation. We asked how floral organ GRNs become specialized through combinatorial control by floral homeotic proteins and other TFs that function in floral organ morphogenesis and growth. Here, we excluded vegetative MADS factors (FLC, FLM, SOC1, and SVP) from the analysis. For AP1 and SEP3, only binding data at stage 4 were considered. Therefore, binding maps of 11 TFs were included in subsequent analysis. Based on domain-specific mRNA translatome data  62  , we identified 1013 organ-specific genes that were highly expressed either in AP1-specific (sepal), AG-specific (carpel), AP1/AP3common (petal), or AP3/AG-common (stamen) domains (see Methods).
The luciferase activity was detected with the Dual-Luciferase Reporter Assay System E1960 (Promega, USA). Network analysis. To assess the robustness of networks containing miRNAs, we analyzed and compared the network structure of two kinds of networks: the "meta-network" consisting of both TF-directed and miRNA-mediated regulation, and the network without miRNA regulation. In principle, more robust network shows a higher degree of tolerance against the removal of nodes. We used the swan_combinatory function in the NetSwan package in R to calculate the resistance of networks due to the node removal, using a cascading scenario to remove nodes in the decreasing order of their betweenness. The loss of connectivity along with the fraction of nodes removed was plotted (Fig. 3b ). Next, we systematically examined the enrichment of motifs in the metanetwork.
We focused on the analysis of autoregulation (1-node), feedback loop (2-node), and 3-node motifs, which have particular biological implications  51. The 1-and 2-node network motifs were searched by custom code, while the enrichment of 3-node motifs was performed by the FANMOD algorithm  54. The significance of motifs were determined by comparing the number of observed motifs with the number found in 1000 randomly shuffled networks, as implemented in FANMOD. To determine if two targets in an SIM have possible physical interactions, protein-protein interaction (PPI) data were obtained from ref. 79. For each master regulator, the percentage of its targets with potential PPIs in the associated SIM motifs was calculated. As a control, the same number of genes as the investigated master regulator were sampled from the whole list of target genes for all the 15 master regulators, and a similar percentage value was calculated as above.
The TF-binding score x ij for TF j at gene i was defined the normalized peak score. TF binding data were normalized and mean centered before modeling. Lasso regression was performed using the glmnet package in R to calculate the direct contribution (i. e. , the parameter β) of each TF to the expression change. The optimal model was chosen by five repeats of ten-fold cross-validation using the caret package in R. The above analysis was performed via the Shiny application HTPmod  80. Statistical analysis and data visualization. If not specified, all statistical analyses and data visualization were done in R. t-SNE (t-distributed stochastic neighbor embedding) analysis was performed by the Rtsne library and the output was visualized by our HTPmod online tool  80  (https://www. epiplant. hu-berlin.
Acknowledgements We would like to thank  Johanna Müschner  for her assistance with RNA-seq, and  Lei Wang  from  Huazhong Agricultural University  for the help with the luciferase reporter gene experiments. We would like to thank the  Center for Information Technology and Media Management (ZIM) at Potsdam University  for providing high performance computing resources. K. K. wishes to thank the  Alexander-von-Humboldt Foundation  and the  Federal Ministry of Education and Research  for support.
2015). Moreover, the majority of the sequenced diatoms were relatively large and small-celled taxa (10-20 μm) were excluded. A final means of obtaining barcodes for reference libraries is direct sequencing of environmental samples in order to avoid the laborious procedures involved in the first two methods (isolation, culturing, single cell sorting). Examples of two different approaches have already been published. One is simple direct Sanger sequencing of samples presenting very low species diversity, as in some Chilean rivers with Didymosphenia geminata (Lyngbye) Mart. Schmidt blooms  (Jaramillo et al. 2015). However, this method is relatively imprecise and several rbcL sequences published do not correspond to the targeted species announced in the paper (e. g. NCBI accession numbers: KR066780, KR066784).
Is it possible to relate environmental sequences to the target species observed by microscopy and to do so with high reliability? 2. What are the advantages/disadvantages associated with the use of sequences from uncultured diatoms? 3. Which material, data and metadata must be stored with these sequences to ensure good traceability? Several examples will be given from Mayotte island, a French tropical island of the Comoros archipelago situated in the Mozambique Channel (Africa), where 98 environmental samples from rivers were sequenced using a HTS technology, Ion-Torrent PGM, and also observed with light and scanning electron microscopy. The open-access barcoding library R-Syst::diatom  (Rimet et al. 2016 ) is used as the host database to store the data (www. rsyst. inra. fr/) presented in this paper and the Thonon Culture Collection (TCC) to store the material (www6.
Ligation of library adapters to purified amplicons was done using 2µL of P1 adapter (NEB kit) and 2µl of A-X tag adapter provided in Ion Express TM Barcode adapters (Life Technologies, Carlsbad, USA) using 1 tag per amplicon. The quality, size and concentration of the libraries were checked using the 2200 TapeStation with D1000 High Sensitivity screen tape and reagents. Each library was diluted to 100 pM and all were pooled together with 50 libraries from other environmental samples from Mayotte in a unique mix sequenced using 1 Ion 318 TM Chip Kit V2 (Life Technologies, Carlsbad, USA) on a PGM Ion Torrent machine by the "Plateforme Génome Transcriptome" (PGTB, Bordeaux, France). Sequence data processing. Demultiplexing and adapter removal steps were made by the Sequencing Platform which provided a single fastq file for each of the 55 libraries.
DNA reads were filtered for length and quality using mothur software  (Schloss et al. 2009 ) in every fastq file with the following settings: a minimum length of 250 bp, a Phred quality score higher than 23 over a moving window of 25 bp, a maximum of 1 mismatch in the forward primer sequence, homopolymers shorter than 8 bp, and absence of ambiguous bases. Reads which were not fully aligned with the rbcL barcode were removed. The resulting files were analysed together. Denoising of sequencing error was performed with the pre. cluster command by creating read clusters allowing one nucleotide difference between DNA reads. Chimera removal was done with UCHIME algorithm  (Edgar et al. 2011). The R-Syst::diatom database  (Rimet et al. 2016 ) (database version v5, http://www. rsyst. inra. fr/en), restricted to our 312-bp rbcL barcode, was used as the reference database.
Taxonomic assignment of DNA reads at species level was made using this reference database and the Naïve Bayesian method  (Wang et al. 2007 ) with a confidence score threshold of 85%. Only DNA reads assigned to the Bacillariophyta phylum (diatoms) were used in further analyses. After dereplication, uncorrected pairwise distances were calculated between aligned reads to generate a similarity distance matrix. Based on this distance matrix, reads were clustered in Operational Taxonomic Units (OTUs) using the Furthest Neighbour algorithm at 100% similarity level in order to have each OTU represented by a single sequence. Singletons were removed. Then, for each of the four low-diversity samples, a Blastn was run on the entire NCBI database with each of the 15 to 20 most abundant 312-bp sequences.
Phylogenetic analyses. For each of the four samples, the selected 312-bp sequences were aligned with a selection of sequences from R-Syst::diatom database using Muscle  (Edgar 2004)  in Seaview  (Gouy et al. 2010). This selection of sequences was done based on their taxonomic proximity to the 312-bp sequences. The lengths of the Sanger sequences from R-Syst::diatom were at least 1000 bp. The best substitution model was then tested in MEGA7  (Kumar et al. 2016). A first phylogenetic tree was calculated following the best substitution model with raxmlGUI  (Silvestro & Michalak 2012)  and the sequence selection of R-Syst::diatom, after which we calculated a second phylogenetic tree, adding the 312-bp sequences in the phylogeny and enforcing its topology with the topology of the first tree. This analysis is also available in raxmlGUI under the "enforce constraint menu" and "define topological constraint".
Trees were drawn in MEGA7. Material and data accessibility. All material is accessible through the Thonon Culture Collection (TCC: https://www6. inra. fr/carrtel-collection_eng/) and at the Botanischer Garten und Botanisches Museum Berlin-Dahlem, Freie Universität Berlin (B) and also at the Conservatoire et Jardin Botaniques of Geneva (G) (for the newly described species, all nomenclatural acts have been submitted to PhycoBank for registration of new scientific names; http://phycobank. org). TCC culture collection hosts algal cultures, but also uncultured samples containing species of interest (as permanent slides, and raw and treated material). All metadata are stored in the open-access R-Syst::diatom reference database; a detailed description of this database and its management is given in  Rimet et al.
A few species and varieties belonging to former Rhopalodia are heteropolar, in particular, R. hirudiniformis and its varieties var. parva O. Müller, var. turgida Fricke, and also the species R. rhopala (Ehrenberg) Hustedt. All these taxa were described from east African lakes  (Cocquyt 1998; Cocquyt et al. 2018). Regularily, some publications and databases incorrectly spell this species "hirundiniformis". The length of the valves found in the Soulou waterfall (site 1, Fig. 1 , Fig. 4 ) ranged from 73. 6 to 215 µm and the maximum width from 7 to 14 µm. However, the smallest width measurements may be erroneous as the valves were not always flat during light microscopy. In our sample, the striae and fibulae densities were 11-12 and 5-6 in 10 µm, respectively. These densities correspond to the description of R.
sequences were blasted on NCBI. Two sequences had high similarity with Halamphora montana (Krasske) Levkov (96%) and their length was 312-bp. These sequences were selected to build a phylogeny. The list of sequences used as constraints for the 312-bp sequences is given in Supplementary data S1 and the alignment used in the phylogeny is in Supplementary data S5. No indels or stop codons appeared in the 312-bp sequences after alignment. The best model selection was the GTR+G+I model (General Time Reversible model with gamma distribution and Invariable sites) which showed the lowest AICs (Akaike Information Criterion, corrected). A maximum likelihood constrained phylogeny with GTR+G+I model and 1000 rapid bootstraps was calculated and is drawn in Fig  6. The two 312-bp sequences are included in a group supported by a high bootstrap value (97%).
The 20 most abundant sequences were blasted on NCBI and R-Syst::diatom. 10 sequences had high similarity with Gomphonema acuminatum Ehrenberg (95-96%) and their length was 312 bp. Three other sequences also had high similarities with species of Gomphonema genus: G. bourbonense (one sequence matching with rbcL sequences of strains TCC441, TCC460, TCC513, TCC450, TCC514, TCC453, TCC452, TCC451) and G. lagenula (two sequences matching with rbcL sequences of strains TCC470, TCC500, TCC440, TCC432, TCC431, TCC429 and NCBI sequences HG530055, HG530054); these sequences were rejected from further analyses. Sequences showing high similarities with G. acuminatum were selected to build a phylogeny. The list of sequences used as constraint for the 312-bp sequences is given in Supplementary data S2 and the alignment used in the phylogeny is in Supplementary data S6.
This last sequence accounted for 0. 5% of the total abundance of the sequences. The list of sequences used as constraits for the 290-bp sequences is given in Supplementary data S3 and the alignment used in the phylogeny is in Supplementary data S7. After alignment, 9 of the 290-bp sequences showed deletions and were removed from the following analyses. 5 sequences were selected and showed no stop codons. The best model selection was the GTR+G+I model which showed the lowest AICs. A maximum likelihood constrained phylogeny with GTR+G+I model and 1000 rapid bootstraps is given in Fig. 8. The five 290-bp sequences form a monophyletic clade supported by a high bootstrap value (100%). They are inside a larger clade composed of other Epithemia species and supported by a high bootstrap value (79%).
Therefore only five sequences out of the fourteen 290bp sequences were kept for R-Syst::diatom. Gomphonema parvulum (Fig. 9 ) 1053 different environmental sequences were obtained from the sample from the Mouala river near Mirereni city of which 516 were singletons. The 20 most abundant were blasted on NCBI. 14 sequences had high similarity with Gomphonema parvulum sensu lato and their length was 275-bp. These were used to build a phylogeny. The list of sequences used as constraint for the 275-bp sequences is given in Supplementary data S4 and the alignment used in the phylogeny is in Supplementary data S8. No indels and no stop codons appeared in the 275-bp sequences after alignment. The best model selection was the GTR+G+I model which showed the lowest AICs. A maximum likelihood constrained phylogeny with GTR+G+I model and 1000 rapid bootstraps is given in Fig.
What are the advantages/disadvantages associated with use of uncultured diatom HTS sequences to enrich barcode reference libraries? Unlike macroorganisms such as aquatic insects, where the DNA barcodes of individuals can be easily Sanger sequenced from a part of their body (e. g. insect legs in  Sweeney et al. 2011) , diatom cells need to be isolated and cultured to get their barcodes, which is laborious and not always successful. Using HTS for natural samples encompassing several millions of diatom cells and several species makes it potentially possible to obtain thousands of barcode sequences in one HTS run. One advantage of using HTS is shown by the examples of Epithemia hirudiniformis, Halamphora ghanensis and Gomphonema clavatuloides reported here. In each case, from 2 to 10 sequences were kept for each species in the reference database R-Syst::diatom.
Such differences must be taken into account during data processing. When integrating sequences from HTS in reference databases, sequence quality criteria have to be defined. In this case, we set four criteria: Criterion 1: only the 15-20 most often sequenced reads were selected because such sequences are more likely to be the best representatives of the population and also are likely to be sequences showing the lowest probabilities of sequencing mistakes  (Bragg et al. 2013). Criterion 2: since rbcL is a coding region for a gene, no indels were accepted when aligning the HTS sequences with Sanger sequences from R-Syst::diatom. Sanger sequencing is, until now, considered to be the reference technology in terms of sequencing quality (e. g. Montoya et al. 2016; Khalifa et al. 2016 ). Such problems were particularly common in the case of Epithemia hirudiniformis, where many sequences showed deletions, and were therefore not included in the reference database.
Acknowledgements Sampling, microscopy and sequencing were funded by the  Agence française pour la biodiversité. Sequencing was carried out in the  Genome Transcriptome Facility of Bordeaux (INRA Pierroton ) and we thank  Alain Franc ,  Philippe Chaumeil ,  Jean-Marc Frigerio  and  Franck Salin  for helpful discussions. We also thank  DNAqua-Net  (European Cost Action  CA15219 ) which helped discussion between some of the authors.
biofilm samples, the material data as well as the linked metadata have to be deposited in curated collections (herbaria such as B, BM, G, P, etc. see index of herbaria inHolmgren et al. 1990) and have to be available through public scientific databases (e. g. R-Syst, AlgaTerra, GGBN, GBIF, BOLD, INSDC). This includes eDNA material deposition (e. g. DNA Bank Network / GGBN) and pos- sible linked voucher material, information concerning the HTS methodology (e. g. DNA extraction, primers, PCR, library preparation, HTS chemistry, HTS platform, paired-end reads or not), details of the bioinformatics pipeline and the algorithms used (e. g. sequence trimming, chimera treatment, thresholds for OTU clustering, modus operandi of species assignment), and availability of the raw reads. As for reference sequences from unialgal diatom cultures, Zimmermann et al.
Phage genome sequencing and analysis High titer lysates (10 9 -10 11 PFU/ml) were used for phage DNA extraction. Briefly, DNase I (1 μg/ml final concentration) and RNase I (1 μg/ml final concentration) were added to the lysates for 30 min at room temperature to degrade any host nucleic acids in the lysate. Phage DNA was then isolated using the Promega Wizard DNA Clean Up Kit (Promega Corp. , Madison, WI, USA), as described previously  [22]. Purified genomic DNA was subjected to whole-genome sequencing using the Illumina MiSeq platform by the Technology Center for Genomics and Bioinformatics (TCGB) at UCLA. The approximate coverage obtained for each phage is as follows: Anatole, 11,161; B3, 9371; B22, 9019; Doucette, 3409; E1, 1000; E6, 14,437; and G4, 13,283. Raw Sequence reads were assembled using Newbler Assembler and Consed or the ABySS software package with manual correction.
Genome ends were deduced from large read-start buildups located on opposite strands, separated by a 12-base region of lower coverage. Finished sequences were analyzed and annotated using the genome editor DNAMaster (http:// cobamide2. bio. pitt. edu). GenBank accession numbers are listed in Table  1. Genome comparisons were performed and represented using Phamerator and the database ' Actinobac-teriophage_685'  [23].
Our results indicate that these strains are resistant to infection by all phages tested. Collectively, these host range data demonstrate that the P. freudenreichii phages show a higher degree of host specificity than the phages infecting C. acnes  [5]. Genomic DNA characterization of P. freudenreichii phages Total genomic DNA was prepared from high titer P. freudenreichii phage lysates, and the complete genome sequences were determined. We observed that the genome sizes among these sequenced phages are similar and vary from 35,209 bp to 38,555 bp, which is larger than those of the C. acnes phages that average 29,432 bp in length. However, the overall genome sizes for the P. freudenreichii phages and C. acnes phages are only about half the average size of the mycobacteriophages, whose hosts are related members of the high GC Gram-positive Actinobacteria  [25].