dataesr/scientific-paragraphs-categorization huggingface dataset:
Plasmid Profiling Based on migration of genomic DNA on Eckhardt gels  (Vaudequin-Dransart et al. , 1995) , all A. tomkonis strains were evidenced to carry plasmids, although differences in size and number were observed between strains (Supplementary Figure  1 ). Indeed, strain CFBP 6624 presented a single plasmid of approximately 500 kb, whereas CFBP 6623, RTP8 and the three ISS strains showed one to three plasmids, with a common largest band around 260 kb. RTP8 and the three ISS strains have in common a 130-kb plasmid, and a 40-kb plasmid is present exclusively in the three ISS strains. The ISS strains do not harbor a pTi plasmid, as no classical vir genes were detected in their genomes (data not shown). Accordingly, no symptoms of crowngall diseases were observed on injured tomato plants inoculated with any of the ISS strains (data not shown).
Agrobacterium tomkonis Reads in International Space Station Metagenomes In order to evaluate the relative abundance of A. tomkonis onboard the ISS, we performed metagenomic read recruitment obtained from ISS environmental DNA shotgun datasets  (Singh et al. , 2018b)  with strain IIF1SW-B1 T genome as a reference. Visual inspection of the mapped read profiles showed that most recruited reads were located on conserved bacterial genes encoding products such as ribosomal RNA operons, ribosomal proteins, DNA-directed RNA polymerase rpoB or elongation factor Tu. Considering the high degree of conservation of these genes across distant bacterial lineages and the non-uniform read coverage of G3 contigs we conclude that despite being isolated from the same environment, A. tomkonis is not present in any detectable abundance in the ISS metagenomes.
In summary, A. tomkonis genomes are diverse, and notably carry a variety of plasmids, even though none of the G3 strain in our dataset carried a tumorigenic plasmid. As previously shown for other Agrobacterium genomic species  (Lassalle et al. , 2017) , A. tomkonis strain genomes share a number of speciesspecific genes. A. tomkonis specific gene functions notably relate to surface adhesion and could be involved in the ecological specificity of A. tomkonis suggested by their varied source of isolation  (Popoff et al. , 1984). Indeed, while most species of the Agrobacterium biovar 1 are usually isolated from soils, plant rhizospheres or as plant pathogens inducing tumors  (Barton et al. , 2018; Dodueva et al. , 2020; Weisberg et al. , 2020) , A. tomkonis strains never have been isolated as a plant pathogen, nor a tumor-inducing plasmid was observed in their genome, which we confirmed by negative infection tests on tomato plants.
5 min, and finalized by extension at 72 • C for 10 min. The amplified products were treated with Antarctic phosphatase and exonuclease (New England Biolabs, Ipswich, MA, United States) to remove 5 -and 3 -phosphates from unused dNTPs before sequencing. Sequencing was performed by Macrogen (Rockville, MD, United States) using 27F and 1492R primers for Bacteria. The resulting sequences were assembled using SeqMan Pro from the DNASTAR Lasergene package (DNASTAR Inc. , Madison, WI, United States). Bacterial sequences were searched against the EzTaxon-e database  (Kim et al. , 2012)  and identified based on the closest percentage similarity (>98. 6%) to previously identified microbial-type strains.
To determine the quality of the assembled sequences, the number of contigs, the N50 value, and the total length were calculated using QUAST v5. 0. 2  (Gurevich et al. , 2013). Default parameters were used for all software. The WGS sequencing of strains RTP8 and CFBP 6624 was carried out by Beckman Coulter Genomics (Takeley, Essex, United Kingdom). SPAdes v3. 8. 1  (Bankevich et al. , 2012)  was used to assemble all the cleaned sequences. All five new genomes were annotated using Prokka v1. 14. 5  (Seemann, 2014)  with the following options: "-force -addgenes -compliantusegenus. " Protein function was annotated by performing a blastp similarity search against a reference proteome database made of 405 high-quality genome assemblies obtained from the NCBI RefSeq, selecting all genomes belonging to the Rhizobiaceae family available as on 14th August 2018 that had a contig N50 greater than 98 kbp (Supplementary Table  7 ).
Pangenome Analysis We applied two separate pangenome analysis pipelines to obtain robust estimates of the sets of genes present and absent in each genome and to compute core and clade-specific gene sets for the various taxonomic groups represented in our dataset, including Agrobacterium biovar 1, A. tomkonis strains including ISS isolates. (1) On one hand, a pangenomic Snakemake workflow  (Köster and Rahmann, 2012)  was run in anvi'o v6. 2  (Eren et al. , 2015). Briefly, gene clusters (GC) were defined by clustering allversus-all open-reading frames BLAST similarity scores using the Markov Clustering (MCL) algorithm  (Enright et al. , 2002)  with an inflation score of 8. Open reading frames were identified with Prodigal 2. 6. 3 in single mode  (Hyatt et al. , 2010). Open reading frames were annotated using COG and KOfam databases  (Eren et al.
, 2015; Delmont and Eren, 2018). Results were displayed using anvi-display-pan function  (Delmont and Eren, 2018). Functional enrichment in the ISS and G3 strain sets was obtained using anvi-compute-functional-enrichment function, as described in  Shaiber et al. (2020) , based on the COG annotation of open reading frames. Only enrichment for which FDR adjusted p-value < 0. 05 are reported in Supplementary Table  5. (2) On the other hand, a phylogenomic database was built with the Pantagruel pipeline  (Lassalle et al. , 2019) , using the "usingGeneRax" branch of the code 3 version "df79fee577b72389926a0138353fa657c11f3368. " The following pipeline tasks were performed: init, 00-03 and 05, leading to the clustering of homologous protein families with MMseqs2  (Steinegger and Söding, 2017) , the alignment of their reversetranslated coding sequences with Clustal Omega  (Sievers et al.
, 2011)  and Python scripts using Biopython  (Weisberg et al. , 2020) , and the computation of a maximum-likelihood (ML) phylogenetic tree with RAxML 8. 1. 2 (Stamatakis et al. , 2005)  based on the concatenation of 1,089 core-genome gene alignments, as previously described  (Lassalle et al. , 2021)  (Supplementary Data File doi: 10. 6084/m9. figshare. 14792178). In addition, clade-specific sets of homologous gene clusters were determined using the "get_clade_specific_genes. r" script from Pantagruel package using the homologous gene family presence/absence matrix as input.
Metagenome Sequence Reads Recruitment to Strain IIF1SW-B1 Metagenomic datasets (n = 42) previously obtained from environmental swabbing of the ISS  (Singh et al. , 2018b)  were downloaded from NCBI-SRA BioProject PRJNA438545, quality filtered and mapped using BWA-MEM  (Li and Durbin, 2009)  against the genome assembly of strain IIF1SW-B1 T. These steps were performed using anvi'o v6. 2  (Eren et al. , 2015)  using anvi'o metagenomic workflow in reference mode and default parameters. Visual inspection of read mapping alignment was carried out using Artemis BamView  (Carver et al. , 2012).
Phylogenetic Analysis of Marker Genes For the 16S rRNA gene phylogenetic analysis, we extracted the 16S rRNA sequences from the genomes and added the reference 16S gene sequence from Rhizobium leguminosarum type strain USDA 2370 T (GenBank accession U29386. 1) and then aligned all sequences with Clustal Omega  (Sievers et al. , 2011)  (Supplementary Data File doi: 10. 6084/m9. figshare. 14792148). For the gyrB single-gene phylogenetic analyses, as well as multi-locus sequence analysis (MLSA), we used the coding sequence (CDS) alignments for the marker genes parE, gyrB, recA and rpoB from the Pantagruel database described above (see Supplementary Data Files available at doi: 10. 6084/m9. figshare. 14792169 and doi: 10. 6084/m9. figshare. 14792100). For the MLSA, we concatenated the CDS alignments of parE, gyrB, recA, and rpoB into a multilocus alignment, totaling 9,968 positions.
ML Phylogenies were then inferred based on these alignments with RAxML-NG 1. 0. 0  (Stamatakis et al. , 2005)  with the options "model GTR + G4 -all, " implying the use of the model GTR + FO + G4m, and that 20 independent inferences 3 https://github. com/flass/pantagruel/tree/usingGeneRax were run, starting with 10 random trees and 10 parsimonyoptimized tree, and retaining the best ML tree from these 20 inferences. Branch supports were estimated with 200 Felsenstein bootstrap trees. The 16S tree was rooted using R. leguminosarum USDA 2370 T sequence, whereas the gyrB and MLSA trees were rooted using the sequence from "A. albertimagni" AOL15 -a distant relative incorrectly classified in the Agrobacterium genus for which a classification in the newly proposed genus "Peteryoungia" was proposed  (Kuzmanović et al.
ACKNOWLEDGMENTS We would like to thank  Aleksandra Checinska-Sielaff  for isolating the strain and  Achintya Bharathwaj  for initial phylogenetic analyses, astronauts  Captain Terry Virts  for collecting samples aboard the  ISS  and the  Implementation Team at NASA Ames Research Center (Fathi Karouia)  for coordinating this effort, and  Ryan Kemp  ( Zymo Research Corp. ) for extracting DNA and  Dan Butler  ( Weill Cornell Medicine ) for performing shotgun sequencing using the NovaSeq platform. We acknowledged the  Jet Propulsion Laboratory supercomputing facility  staff, notably  Narendra J. Patel  (Jimmy) and  Edward Villanueva , for their continuous support in providing the best possible infrastructure for BIGDATA analysis. © 2021  California Institute of Technology , Government sponsorship acknowledged.
DATA AVAILABILITY STATEMENT The draft genome sequences of three ISS strains including type strain IIF1SW-B1 T , as well as strains RTP8 and CFBP 6624, were deposited in NCBI. The genome sequences of isolates A. tomknonis IIF1SW-B1 T , RTP8, and CFBP 6624 have been deposited to GenBank under the BioSample accessions SAMN15333190, SAMN17917486, and SAMN17918142, respectively; the WGS project accession numbers are also given in Table 1. The genome versions described in this paper are the first versions. Analysis intermediary data and result tables are available under the FigShare project available at https://figshare. com/account/home#/projects/115866 with individual items available at the following DOIs: 10. 6084/m9. figshare. 14792100, 10. 6084/m9. figshare. 14792148, 10. 6084/m9. figshare.
FUNDING Supplementary Table  5  | List of contigs specific to the clonal group of A. tomkonis strains isolated from the ISS and their characteristics. The count of genes with genetic mobility-related functions is based on the Prokka annotation of proteins. Supplementary Table  6  | COG function enriched in 3 Agrobacterium tomkonis ISS strains, compared to the 37 other Agrobacterium genomes in the dataset. Supplementary Table  7  | List of genome assemblies used as reference to annotate the new Agrobacterium genomes with Prokka.
These detailed measurements, reported with respect to specific test conditions, are highly critical to accurate capacity modeling of a given optical path within an Open Cable system and can only be collected once the system is constructed. An example table to guide the capture of critical system data to be collected is provided in  [12]. While the KPT has traditionally contained the final agreed commissioning values, it does not detail the various SNR contributions and margins used to reach these performance targets. The SNR-based optical performance budgets in the following sections are provided to capture these details.
She has been a long-time champion of the subsea Open Cables movement and in her current role with Facebook recently led a cross-industry working group focused on garnering broad industry agreement on best practices and recommendations for many key aspects of Open Cables. Alexei Pilipetskii was born in Russia, Moscow, in 1962. He received the M. S. degree in physics from Moscow State University in 1985. He received the Ph. D. degree in 1990 from General Physics Institute, Academy of Sciences Russia, for the research in nonlinear fiber optics. From 1994 to 1997, he was at the University of Maryland, Baltimore County, where his interests shifted to the fiber optic data transmission. In 1997, he joined AT&T Submarine Systems, currently SubCom. Alexei is the Leader of the Forward Looking Team focused on the transmission research and introduction of the new technologies.
, 2016) , and included some questions from a questionnaire used in Brazil  (Hopkins, 2000). In parallel, a student midwife (ACP) conducted 26 indepth qualitative interviews under the supervision of CS. These women were not included in the 'quantitative' cohort, but were recruited and interviewed at Maternity 1 during their pregnancy (n = 7) or after delivery (n = 19). Data were collected about their representation or experience of caesarean section. All 19 women interviewed after delivery had given birth by caesarean section. The aim of the qualitative interviews was to gain a deeper understanding of women's perceptions of childbirth, and to extract certain elements that could not be anticipated in the closed questionnaires.
Quantitative data analysis Information collected through the questionnaires was recorded on an anonymized Excel spreadsheet (Microsoft Corp. , Redmond, WA, USA), with a unique identifier for each woman. Characteristics of the women are presented as medians and interquartile ranges (IQRs) for continuous data, and as frequencies and percentages for categorical data. As this study focused on women's choices in terms of mode of delivery, 10 women who had no preference for or against caesarean section, 32 women who had undergone a caesarean section previously [as 55. 1% of women with a previous caesarean section have repeated caesarean sections  (Blondel et al. , 2017b) ] and five women who had a planned elective caesarean section for medical reasons were excluded from this study. Sociodemographic, obstetric and gender-related factors associated with a preference for caesarean section were explored.
Acknowledgement We would like to thank all women who participated in this study; the student midwives who administered the cohort questionnaires ( Marie Choquel ,  Elisabeth Meot ,  Jessica Nar-boni and Ines Vaudry) ;  Alexandre Dumont  for his advice on the research;  Virginie Roze  ´e for her advice on a first draft of this article;  MSH-Paris Nord  for technical and financial support; and  Fondation Mustela  who funded publication of this article.
Data analysis All interviews were fully recorded and transcribed. These transcripts were analysed using NVivo, which we found especially useful for comparative purposes. Specifically, we first engaged in a "disintegration" step to convert the massive volume of data we gathered into "units of meaning"  (Deschenaux, 2007, p. 10). This was carried out through first-level coding, in which we sought to identify the most important themes that were emerging from the data. We decided to focus our coding efforts on two specific subjects: (i) actions taken by each category of actors and (ii) their interactions with other actors. Our coding was at once deductive and inductive: deductive because we were on the lookout for actions and interactions, and inductive because we observed the emergence of sub-themes within these two main categories.
As for practical implementation, IFRS 15 requires to follow a five-step model of revenue recognition on a contract-by-contract basis, a complex approach that will "require modifications to existing accounting processes"  (Deloitte, 2018). Elaborating on this issue, KPMG (2016, pp. 4e5) adds: "Telecom entities will need to capture the additional data required under the new standard e e. g. data used to estimate stand-alone selling prices […] and to document the new processes and information appropriately, particularly as they relate to estimates and judgments". This, in turn, involves revising internal controls in relation to accounting processes. We show that Telco firms take up these considerations as they comment IASB ED on revenue recognition, insisting that the future standard will deeply (and adversely) impact their business, assuming it is practically applicable.
Acknowledgements We would like to thank  Keith Robson  (the Editor) and two anonymous reviewers for their insightful comments and support throughout the review process. We also appreciate the constructive suggestions on earlier drafts provided by participants of the 2018 Alternative Accounts Conference, the 12th Interdisciplinary Perspectives on Accounting Conference in 2018 and the 39th  Association Francophone de Comptabilit e Conference in 2018. We are also deeply grateful to all the interviewees who participated in this case study.
Learning by doing through concrete AI related activities Each of the three chapters of the course is illustrated by concrete activities that bring learners to train their own machine learning models and interact with real life applications of AI. For the first part of what artificial intelligence is, participants develop a first understanding of AI through training a visual recognition neural network model. In order to frame the activity, we designed an integrated tool to control the training of the model. This experiment is sequenced in three steps. In the first step the learners create a model that differentiates between two categories of images by feeding the model images from a dataset of samples given to them. This first step helps dispel preconceived ideas we have noticed in children with whom we did this activity such as the idea of an AI is a robot that can recognize anything in an image.
They are led to realize that the AI they have trained can not recognize categories it was not trained on by testing it on images that do not represent one of the two categories known to the AI. In the second step the children understand that with few examples the machine learning model struggles to generalize the concepts represented by the images by testing it on images that match one of the categories but in a different context or position (a drawing of a lion instead of a photo of a lion for example). The last step is making them train a model on their own dataset with the classes and examples they choose and find. The second activity around how AI is developed aims to highlight the central role of data in the making of machine learning applications through experimenting with biases in datasets.
We have created a dataset of images with two categories: women and men with an obvious bias (women have long hair and men have short hair). After training a first model with this dataset and seeing it fail, the participants are then invited to reflect on the wrong inferences and create their own dataset to train a better model. They select data, train their model, test and iterate until they're satisfied with the result. Then, to emphasize the importance of data, participants are invited to build datasets to train machine learning models to recognize subjective categories, for example "cute cat vs ugly cat" and thereby understand the responsibility that fall on the humans creating AI's and the importance of questioning the data behind the AI applications they might encounter. The last part about the interactions between AI and humans is illustrated by a series of activities where children experiment with real life AI applications.
We have also a several questions on application domains of AI (i. e. , in education, regarding democracy governance, or disabled assisting tools), which are a bit beyond what we can offer in reasonable time, because each third party domain requires a lot of prerequisites before considering AI related tools. Our idea is to offer "plugins" to other courses, i. e. , participate regarding AI issues, the first project already in preparation being on GreenIT. For all these considered extensions, the present course acts as the core curriculum. Qualitative observations, reading the different participant feedbacks available in our open data set, confirms our hypothesis that citizens now have a better understanding of AI. As pointed out, the main feedback is related to "fear" : Being unknown and mysterious, AI was frightening, while "playing" with AI allows one to make these mechanisms familiar.
CRISPR: sgRNA Design and Generation of Mouse Line Using Cas9 We used the CRISPR design tool from Feng Zhang's lab (https://crispr. mit. edu/, accessed on January 2015) to design a single-guide RNA (sgRNA) targeting mouse Parp6 (GenBank accession no. NM_001323522. 1). The candidate sequences were checked by BLAST search (https://blast. ncbi. nlm. nih. gov/, accessed on June 2018) to minimize the off-target effects, as well as CRISPR/Cas9 target online predictor (https://crispr. cos. uniheidelberg. de/, accessed on June 2018) to check for potential off-target genes. To generate the CRISPR/Cas9 vector targeting exon 18 of Parp6, the annealed oligonucleotide corresponding to the sgRNA (sense: 5 -TAGGCATTCAATCCTGCGCAAT-3 , anti-sense: 5 -AAACATTGCGCAGGATTGAATGCC-3 , Figure  1A ) was cloned into the BsaI site of pDR274 (#42250, Addgene, Watertown, MA, USA).
CRISPR: sgRNA Design and Generation of Mouse Line Using Cas9 We used the CRISPR design tool from Feng Zhang's lab (https://crispr. mit. edu/, accessed on January 2015) to design a single-guide RNA (sgRNA) targeting mouse Parp6 (GenBank accession no. NM_001323522. 1). The candidate sequences were checked by BLAST search (https://blast. ncbi. nlm. nih. gov/, accessed on June 2018) to minimize the offtarget effects, as well as CRISPR/Cas9 target online predictor (https://crispr. cos. uni-heidelberg. de/, accessed on June 2018) to check for potential off-target genes. To generate the CRISPR/Cas9 vector targeting exon 18 of Parp6, the annealed oligonucleotide corresponding to the sgRNA (sense: 5′-TAGGCATTCAATCCTGCGCAAT-3′, anti-sense: 5′-AAACATTGCGCAGGATTGAATGCC-3′, Figure  1A ) was cloned into the BsaI site of pDR274 (#42250, Addgene, Watertown, MA, USA).
For sexing the embryos, we used murine SRY primers to obtain genotyping primer pairs (http://mgc. wustl. edu/protocols/pcrt, accessed on January 2017), GenBank accession no. NM_011564. 1 (Table  S1 ), and the following PCR conditions: 94 • C for 1 min; 35 cycles of 94 • C for 30 s, 62 • C for 1 min, and 65 • C for 20 s; followed by a final 10 min extension 65 • C, obtaining a 273-bp fragment for animals with a Y chromosome. PCR products were resolved with ethidium bromide-stained 1. 8% agarose gel. Total RNA from Parp6 WT and Parp6 TR E18 embryonic brains was isolated using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's protocol. Total RNA, 1 ug, was reverse-transcribed using the ProtoScript II first strand cDNA synthesis kit (New England Biolabs, Ipswich, MA, USA) with random hexamers and oligo dT primers following the manufacturer's instructions.
Cloning: Parp6 RNA Interference and Expression Constructs The shParp6 WT -expressing vector targeting the coding sequence of Rattus norvegicus Parp6 mRNA (GenBank accession no. NM_001106828) and the shLacZ-expressing control vector were subcloned as described in  [7]. For shRNA sequences, see Table  S1. The original N-terminus GFP wild-type Parp6 (GFP-Parp6 WT ) expression construct was cloned into the pCAG vector as described in  [7] , and then transferred to pEGFPC1 (ClonTech, Takara Bio USA, Mountian View, CA, USA) by PCR amplification (primer sequences in Table  S1 ) and the restriction enzymes AgeI and BsaWI. The extra seventeen Parp6 residues found in neurons (KRHSWFKASGTIKKFRA) were subcloned into the pCMV-GFP-Parp6 WT by designing a short DNA sequence (gBlocks, Thermo Fisher Scientific, Waltham, MA, USA, nucleotide sequence in Table  S3 ) and using KpnI and BamHI restriction sites.