dataesr/scientific-paragraphs-categorization huggingface dataset:
Gap Filling of NRPS Scaffold Sequences in Pseudomonas sp. COW3 Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit from Promega. The region of interest was amplified by PCR using primers: COW3G_F (GTTCGGTTTCGATGCGATGG) and COW3G_R (GATTTCCATTTCGCCGACCG) designed with Geneious 11. 1. 5 software (https: //www. geneious. com). PCR reaction was carried in a 50 µL mixture containing 10 µL 5× Go Taq Reaction buffer, 0. 2 µL Go Taq DNA Polymerase (Promega), 1 µL of 10 mM dNTP mixture, 2 µL of 10 µM COW3G_F primer, 2 µL of 10 µM COW3G_R primer, 2 µL genomic DNA, and 32. 8 µL double-distilled water. Amplification was performed using a FlexCycler Block Analytikjena 2010 with the following program: 94 • C for 2 min, 30 cycles of 94 • C for 30 s, 65 • C for 1 min, 72 • C for 1 min, and extension at 72 • C for 10 min, and finally kept at 4 • C.
Phylogenetic Analyses of Pseudomonas Strains For multi-locus sequence analyses (MLSA), rpoD, rpoB, 16S rDNA and gyrB sequences of our bananamide D-G-producing Pseudomonas strains (COW3 and COW65) were extracted from their corresponding draft genome sequences. In addition, draft genomes of putative bananamide-producing strains, and sequences of selected Pseudomonas type strains were retrieved from GenBank (Table  A5 ). The sequences were aligned using MUSCLE  [68]  in MEGA6  [69]. A phylogeny tree was constructed by maximum likelihood with 1000 bootstrap replication, and P. aeruginosa was used as outgroup. rpoD, rpoB, 16S rDNA and gyrB trees were generated separately after which a concatenated tree was obtained by combining the aligned sequences of the four genes.
P. myriotylum CMR1 assay was incubated for 3 days, while P. oryzae plates were incubated for six days at 28 • C. Microscopic slides were assessed for hyphal distortion phenotypes under an Olympus BX51 microscope. The efficacy of treatments of each bananamide variant was evaluated by measurement of mycelial growth diameter compared with those obtained with the P. myriotylum and P. oryzae control. Percentage inhibition of the fungal pathogens by bananamide variants and for each concentration was calculated relative to the mycelial growth in the control. Percentage inhibition was calculated according to the following formula: (Growth diameter untreated control -Growth diameter treated control)x100 growth diameter untreated control Data obtained were analyzed using SPSS 25 statistical software.
Feyisara Eyiwumi Oni (née Olorunleke) was funded by the Schlumberger Faculty for the Future PhD Fellowship and acknowledges postdoc funding by the EOS grant 30650620. Matthieu Duban, Mickaël Chevalier, Christophe Flahaut, and Valérie Leclère were funded in the framework of Alibiotech project which is financed by European Union, French State and the French Region of Hauts-de-France. Authors would like to thank European Union funding through the INTERREG Va FWVL BioScreen/ SmartBioControl Project and the bioinformatics platform bilille. This work has been carried out thanks to the support provided by the Ministries of Europe and Foreign Affairs (MEAE) and Higher Education, Research and Innovation (MESRI) through the program Hubert Curien, Germaine de Staël. The 500 MHz NMR equipment was funded by the Hercules Foundation (AUGE09/006).
The protozoan parasite Leishmania possesses a single flagellum, which is remodelled during the parasite's life cycle from a long motile flagellum in promastigote forms in the sand fly to a short immotile flagellum in amastigotes residing in mammalian phagocytes. This study examined the protein composition and in vivo function of the promastigote flagellum. Protein mass spectrometry and label free protein enrichment testing of isolated flagella and deflagellated cell bodies defined a flagellar proteome for L. mexicana promastigote forms (available via ProteomeXchange with identifier PXD011057). This information was used to generate a CRISPR-Cas9 knockout library of 100 mutants to screen for flagellar defects. This first large-scale knockout screen in a Leishmania sp. identified 56 mutants with altered swimming speed (52 reduced and 4 increased) and defined distinct mutant categories (faster swimmers, slower swimmers, slow uncoordinated swimmers and paralysed cells, including aflagellate promastigotes and cells with curled flagella and disruptions of the paraflagellar rod).
All of these datasets comprise many "hypothetical" proteins still awaiting functional characterisation in addition to well-characterised core components of the microtubule axoneme, associated motor proteins and regulatory complexes. Insights into conserved ciliary biology have helped elucidation of flagellar function in eukaryotic microbes, with a particular focus on human pathogens  [11, 12]. Among these, flagella have been most extensively studied in the causative agent of African trypanosomiasis, Trypanosoma brucei  [13] , which uses flagellar motility for locomotion and immune evasion  [14]  and exhibits close spatio-temporal coordination between flagellum assembly and cell morphogenesis during division  [15]. The T. brucei bloodstream form is particularly sensitive to the loss of flagellar function  [6, 16] , highlighting a potential Achilles' heel that might be exploitable for new anti-parasitic treatments.
Comparison with existing datasets validates flagellar proteome To validate the data, we mapped well-characterised flagellar proteins onto the enrichment data plot (Fig  2A ). Axonemal, paraflagellar rod (PFR), flagellar tip and flagellar membrane proteins mapped to the F I and F S quadrants. Basal body, FAZ and tripartite attachment complex (TAC) proteins mapped to the CB I and CB S quadrants because F fractions contained exclusively the cell-external portion of the flagellum. Intraflagellar transport (IFT) proteins clustered around the midpoint of the plot, indicating their abundance was similar in the F and CB fractions, which is consistent with their known dynamic association with the flagellar basal body and axoneme. We also found substantial overlaps between L. mexicana proteins in the F I quadrant and proteins detected in previously published flagellar proteomes of L.
MS data analysis and SINQ enrichment plot MS-data were converted from. RAW to. MGF file using ProteoWizard (S6 Table ) and uploaded to the Central Proteomics Facilities Pipeline (CPFP  [88] ). Protein lists were generated by using CPFP meta-searches (S6 Table ) against the predicted L. mexicana proteome (gene models based on  [29] , followed by label-free SINQ quantification (S1 and S6 Tables). For SINQ enrichment plots detected GeneIDs were filtered (p � 0. 95, � 2 peptides) and plotted using normalized spectral indices. For missing indices pseudo spectral indices of 10 -10 were inserted. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [89]  partner repository with the dataset identifier PXD011057.
The final library was spiked with 1% PhiX DNA and the Illumina sequencer was loaded with 8 pM to allow low cluster density (~800 K/mm 2 ). Sequencing was performed using MiSeq v3 150 cycles kit following the manufactures instructions with paired-end sequencing (2x75 cycles, 6 and 8 cycles index read). MiSeq raw files were de-multiplexed using bcl2fastq (Illumina). De-multiplexed samples were subjected to barcode counting using a bash script. Each gene in the Leishmania genome was assigned a unique barcode-the number of times each of these barcodes appeared in the sequencing output was recorded (S9 and S10 Tables). The criteria for barcode counting was a 100% match to the 17 nt total length. Counts for each barcode were normalized for each sample by calculating their abundance relative to all 25 barcodes.
Acknowledgments We like to thank  Svenja Hester ,  Philip Charles  and  Benjamin Thomas  ( Central Proteomics Facility at the Sir William Dunn School of Pathology ) for help with protein mass spectrometry,  Errin Johnson  ( Dunn School Bioimaging Facility ) for assistance with electron microscopy,  Amanda Williams  ( University of Oxford ) for help with Illumina sequencing,  Maxime Cesca  ( Universitede Paris Sud ) and  Laura Makin  ( University of Oxford ) for assistance with mutant characterisation,  Oliver Billker  ( Wellcome Sanger Institute ) and members of O. B. 's lab for helpful discussions about the bar-seq method,  Sebastien Pomel (Universitede Paris Sud)  for drawing our attention to the PMI, PMM and GDP-MP mutants,  Diane McMahon-Pratt  ( Yale University ) for antibody 2E10 (F-4) and  Keith Gull  ( University of Oxford ) for advice and helpful comments on the manuscript, antibodies  L8C4  and  L13D6  and access to equipment, and members of K.
Funding: EG and JSm were jointly funded by the  UK Medical Research Council (MRC)  and the  UK Department for International Development (DFID)  under the  MRC/DFID Concordat  agreement; grant no. MR/R000859/1  (https://mrc. ukri. org/). EG was supported by a  Royal Society University Research // royalsociety. org/). RW was supported by the  Wellcome Trust , grant nos. [211075/Z/18/Z,  103261/Z/13/Z ,  104627/Z/14/Z ] (https://wellcome. ac. uk/). PV, JSa and TL were supported by  European Regional Development (ERD) Funds , project  CePaViP  ( CZ. 02. 1. 01/0. 0/0. 0/16_019/ 0000759 ; https://ec. europa. eu/regional_policy/en/funding/erdf/). TB was supported by  MRC  PhD studentship ( 15/16_MSD_836338 ; https://mrc. ukri. org/) and an  Erasmus grant  (https://www. erasmusplus. org. uk/study-abroad).
JV was supported by  MRC  PhD studentship ( 13/ 14_MSD_OSS_363238 ; https://mrc. ukri. org/)HJ was supported by by  BBSRC Interdisciplinary Biosciences   DTP studentship  (https://www. ox. ac. uk/admissions/graduate/courses/interdisciplinarybioscience?wssl=1) and an  Oxford Radcliffe Scholarship. SS was supported by by  BBSRC Interdisciplinary Biosciences   DTP studentship  (https://www. ox. ac. uk/admissions/graduate/ courses/interdisciplinary-bioscience?wssl=1). FD was supported by an Erasmus grant (https://www. erasmusplus. org. uk/study-abroad). NA was funded by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre  (BRC; https://oxfordbrc. nihr. ac. uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Observations over the ocean is very challenging compared with land-based measurements due to the mobility of the platform and severe weather conditions. The Maritime Aerosol Network (MAN), as a component of the AErosol RObotic NETwork (AERONET), is the largest long-term aerosol observation network over the ocean  (Smirnov et al. , 2009). It has provided a unique dataset about aerosol optical depth (AOD) and precipitable water vapor (PWV) over the ocean even from Arctic to cruises (see Fig. 2 ). The MICROTOPS II measurements were conducted on the bridge (see Fig. 2 ). It should be noted that the 'anthropogenic' smoke from the chimney could contaminate the shipborne CE318-T measurements. However, this was a compromise between avoiding strong head winds, sea spray and smoke. Nevertheless, we only found an AOD shift of 0.
Acknowledgements. The authors acknowledge funding from  ACTRIS  under grant agreement no. 262254 ,  ACTRIS-2  under grant agreement no. 654109  from the  European Union 's  Horizon 2020 research and innovation programme, Labex CaPPA  (The  CaPPA  project  (Chemical and Physical Properties of the Atmosphere)  and  ESA/IDEAS program. We sincerely thank the  Alfred Wegener Institute  and the RV Polarstern crewmembers for their huge support and effort in PS113 and  PS116  (acknowledgement no. AWI_PS113_00 ,  AWI_PS116_00 ). We also appreciate the effort of the  AERONET MAN, HYSPLIT Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2019-132Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 4 April 2019 c Author(s)  2019. CC BY 4. 0 License.
Data availability. Radiosonde and lidar data for the two cruises are available at the Leibniz Institute for Tropospheric Research and can be accessed upon request. MICROTOPS II data can be downloaded from the AERONET MAN database (MAN,  2019). The shipborne CE318-T data can be accessed through contact with Philippe (philippe. goloub@univ-lille. fr). Author contributions. ZP performed the lidar data analysis and prepared the manuscript supported by AA. MR, CJ and ZP set up the instruments for PS113 and were responsible for the lidar measurements. AH, KO and KH set up the instruments during PS116 and were responsible for the lidar measurements during PS116. PG, LB, GD, SV and FM built up the prototype of the shipborne CE318-T and were responsible for the corresponding data analysis. All authors contributed to scientific discussion and in this way to the manuscript preparation.
1 program available at the Center for Genomic Epidemiology server 2. De novo assembly of paired-end fragments, search of open reading frames (ORFs), and gene annotation were performed by using Basic Local Alignment Search Tool BLASTn 3  and CLC genomics Workbench 10. 0. 1 software (Qiagen Bioinformatics, Redwood city, CA, United States). Schematic representation of integrons (Figure  2 ) was drawn with SnapGene 4. To establish the plasmid pNECK1 backbone, longer contigs were generated from strain Pae1156 by PacBio SMRT sequencing (CD Genomics, Shirley, NY, United States). The IMP-63-encoding plasmids have been deposited in the NCBI database under the accession numbers MK047609 (pNECK1), MK047610 (pTROUS1), MK047611 (pTROUS2), and MK047608 (pROUE1).
Strain Typing The sequence types (STs) of P. aeruginosa isolates were determined according to the scheme proposed by  van Mansfeld et al. (2009). Internal sequences of housekeeping genes acsA, aroE, guaA, mutL, nuoD, ppsA, and trpE, as determined by whole genome sequencing or specific PCRs  (van Mansfeld et al. , 2009) , were uploaded to MLST web site 5 to define STs. In addition, the clonality of the strains involved in hospital H1 outbreak was investigated by MLVA (Multiple-Locus Variable-Number Tandem-Repeat Analysis) targeting microsatellites  ms142, ms211, ms212, ms213, ms214, ms215, ms216, ms217, ms222, and ms223 (Vu-Thien et al. , 2007). The size of individual amplicons was determined for each strain by agarose gel electrophoresis. The banding patterns of strains Pae1151 to Pae1156 were strictly identical, but different from that of the late isolate Pa2567 (data not shown).
All of them yielded a positive double-disk synergy test between Zn 2+ chelator EDTA and imipenem, suggesting the acquisition of a metallo-beta-lactamase (MBL). Use of a PCR targeting known transferable MBL-encoding genes led to the identification of bla IMP-63 , which codes for a new variant of IMP-12. This latter carbapenemase was detected in an Italian strain of P. putida in year 2000  (Docquier et al. , 2003) , and to the best of our knowledge has never been reported since then in the literature. IMP-63 (accession number AOR06148) differs from IMP-12 by a Gly-to-Ser substitution at position 214 and is relatively distant from other IMP-type enzymes, with the closest relatives being IMP-8 and IMP-2 (87. 3 and 86. 9% sequence identity, respectively). The phylogeny of IMP proteins including IMP-63 is represented Figure  1.
The gene bla IMP-63 sequence was deposited in GenBank under the accession number KX821663. 1. Interestingly, additional investigations showed that  Genetic Environment of bla IMP-63 in P. aeruginosa Pae1156 Most bla IMP genes are carried by class I integrons 6. Whole genome sequencing of strain Pae1156 confirmed the presence of bla IMP-63 in the variable region of a novel 6-kb integron, dubbed In1297. This integron which contains a unique array of gene cassettes and a truncated integrase gene intI lacking 110bp at its 3 end, is interrupted upstream of canonical qacE 1 and sul1 genes by a sequence encoding a Thr63Ala, Arg91His, Gly229Ser variant of insertion sequence ISPsp7 (named ISPsp7v in Figure  2 ). The ISPsp7 element which belongs to the IS30 family was described in the environmental Pseudomonas sp.
The transferred plasmid, dubbed pNECK1, was completely sequenced and assembled in a single contig of 28,859-bp (Figure  4 ). Its GC content was equal to 63. 21%. Search in databases revealed that the replicon is related, though distantly, to several broad-host range IncP plasmids such as pNOR-2000 and pAX22  (Bonnin et al. , 2013; Di Pilato et al. , 2014). In these plasmids, the defective Tn402-like integron platform is flanked at its 5 end, immediately downstream of integrase gene intl1, by a copy of insertion sequence ISPa17 and a divergently transcribed resolvase gene (Figure  5 ). The same module exists in pNECK1 though inserted at the 3 end of In1297, next to a gene of unknown function, orf6 (Figure  4 ). As noted elsewhere, ISPa17 exhibits an atypical insertion sequence structure as it contains the genes coding for two putative transposases and a type II toxin-antitoxin system.
, 2014). Annotation analysis also revealed that most pNECK1 backbone genes involved in fundamental functions such as plasmid partioning (parA), replication (repA), maintenance (kfrA), and transfer (traA, traC, traD, traG) have homologs and a similar linear arrangement in various plasmids including pNOR-2000 and pAX22 (Figure  5 ). However, the closest matches were obtained with DNA contigs from a Sphingomonas histidinilytica strain called UM2 (accession number FUYM01000026) and a Serratia marcescens strain, AR_0027 (accession number CP0267703). While pNECK1 lacks the parB gene present in pNOR-2000 and pAX22, it contains a copG-like gene possibly involved in plasmid replication control  (Brantl, 2015) , as well as several other ORFs of unknown functions. Interestingly, pNECK1 shares a 5,376-bp fragment with an unnamed plasmid detected in a cystic fibrosis strain of Burkholderia cenocepacia isolated in Canada (GenBank accession number CP019671.
, 2006 ; Figure  5 ). The two replicons share a sequence identity from 94 to 99% over the backbone genes. Plasmid pCT14 which consists of 51 ORFs and is 55,216 nucleotide long, does not harbor integron-like structures but possesses mercury resistance determinants and a copy of ISPa17. More importantly pTROUS1 carries the same integron In1573 as in plasmid pTROUS2, flanked by a copy of ISPa17 next to orf6. No DRs adjacent to IRR of ISPa17 and to IRi of Tn402::In1573 could be identified in pTROUS1 and pTROUS2. It is important to mention that these two plasmids differ in their structures and do not derive from each other (data not shown). While the transfer of pTROUS1 to PU21 could not be obtained by conjugational mating, this was possible by electro-transformation. As indicated in Table  1 , the susceptibility profile of transformant PU21(pTROUS1) was fairly similar to that of PU21(pNECK1).
The occurrence of gcuF1 in class I integrons is quite rare, which makes this determinant a good marker for epidemiological studies when present in clinical strains (see text footnote 6). The products of gcuF1 (strain Pp917) and gcu170 (strain Pae1156) have little in common at the sequence level. Alignment of the DNA reads obtained by high throughput sequencing with sequences from the NCBI database allowed us to gain insight into the genetic support and environment of Pp2223 integron. This element is located on a TnCP23-like structure of 25,062-bp and 61. 41% GC, distinct from pNECK1 (Figure  5 ). The backbone genes of this fragment (32 ORFs in total), that was named pROUE1, display sequence identities of 99-100% with their TnCP23 homologs. In some clone C P. aeruginosa, plasmid TnCP23 is embedded in an integrative conjugative element (ICE) called pKLC102, itself inserted in the bacterial chromosome, in a tRNA Lys gene  (Klockgether et al.
DISCUSSION Since the first description of carbapenemase IMP-1 in a Serratia marcescens strain from Japan in 1991  (Osano et al. , 1994) , reports on the characterization of novel IMP-type ß-lactamases in various bacterial species including P. aeruginosa have multiplied worldwide. Despite rather similar Zinc-dependent enzymatic activities, these proteins form a heterogeneous group at the sequence level (as represented by Figure  1 ). However, some of the IMP-types deposited in the NCBI database (see text footnote 3) differ by limited sequence variations and thus are clearly related, as it is the case of IMP-63 and IMP-12. Sequential acquisition of resistance determinants by epidemiologically successful clones (also called "high-risk clones") of P. aeruginosa represents a serious public health problem, leaving clinicians with very few therapeutic options to fight nosocomial infections (Del  Barrio-Tofino et al.
Socioeconomic emissions scenarios and IAMs in the IPCC AR5 IAMs appear to have played a significant role in the AR5  (Edenhofer et al. , 2014: 48) , where they are described as "invaluable to help understand how possible actions or choices might lead to different future outcomes"  (Edenhofer et al. , 2014: 51) , that is as guides for political decision. Two types of IAMgenerated scenarios appear in the AR5. First, four 'Representative Concentration Pathways' (RCPs) representing contrasted possible emission trajectories to 2100 served as input for the elaboration of new climate change projections by climate models  (IPCC, 2013: 164; 1060). They were produced by four IAMs. Second, IPCC WG III collected a database of 1184 peer-reviewed socioeconomic scenarios  (IPCC, 2014b). Thirty models contributed to the scenario database, with eleven providing 966 out of 1184 scenarios (IPCC, 2014a: 1309-10).
e. between 2004 and 2014). A new feature in this period was the increasing role of European projects funded by the Commission within the 7 th research Framework Programme, which pushed for evaluation of the 2°C target as it was the backbone of EU climate policies  (Lövbrand, 2011). EU-funded projects largely focused on estimating the possibility to comply with the 2°C objective and the related mitigation costs according to the ambition of countries' commitments or the availability of low carbon technologies among major emitters from North and South countries (Table  1 ). The outcomes of these projects were collected into dedicated databases, published in peer-reviewed publications, and assessed by the IPCC WG III (Fig. 2 ). These scenarios provided the backbone for the WG III report on mitigation (IPCC, 2014a; interview 5).
They now shape the way IAM research is carried out, and delineate the IAM community: they constitute a 'repertoire' enabling the continuation of collective work, framing common goals and standards, and ensuring the transferability and legitimacy of IAM results  (Leonelli and Ankeny, 2015). In this last part, we focus on the three main features of this repertoire: the IAMC, the organisation of work in Modelling Intercomparison Projects, and scenario databases. All address the same core challenge of IAM research: extracting a policyrelevant message out of diverse modelling approaches and philosophies.
Scenarios are generated by models and results analysed and compared in each work package, and each work package leader has scrutiny over the protocol and the processing of data. MIPs entailed intense collaborations, fostering mutual learning about the specificities of each IAM. They drove improvements in model documentation and evaluation that helped to map and characterise differences between models. They contributed to the development of common modelling practices across teams and fostered innovation. MIPs result in the production of sheer numbers of scenarios, and the need to manage them contributed to the development and stabilisation of another key element of the repertoire of the IAM Community: scenario database.