dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence or absence of LpoB is indicated beneath appropriate bars with a + or -, respectively. Statistical significance was tested by paired T-Test (as in panel a); N. S. , not significantly different; * , P ≤ 0. 05; ** , P ≤ 0. 005. C. Basal TPase and GTase activities of each PBP1B version correlation plot. The activity of each version was calculated relative to WT PBP1B activity as a percentage, relative GTase activity is plotted along the x-axis against relative TPase activity on the y-axis. WT PBP1B activity is displayed across the plot as coloured lines ± SD in corresponding lighter shade (TPase in Green, GTase in Blue). The dashed line represents the region on which data would fall if both activities have been altered to a similar magnitude. I202F activity is omitted from the plot as its inclusion skews the scale such that other points are illegible (basal GTase = 785% of WT, basal TPase = 128% of WT).
coli by NMR and found it adopts a fold similar to the X. campestris homologue (data deposited in BMRB and PDB, accession number 34256 and 6G5S, respectively). The domain is comprised of a series of seven helices that strongly and sequentially interact to form a TPR domain (Fig. S5 ). The structures of E. coli and X. campestris CpoB TPR only differ in the N-terminal helix orientation. We validated our previous model of the CpoB TPR (residues 139-263) interaction with PBP1B by SPR. Purified CpoB TPR bound to PBP1B immobilised to a chip surface with an apparent K D of 4. 9 ± 1. 9 μM (Table  2 , Fig. S4 ). This was a significant decrease in affinity compared to full length CpoB, which interacted with an apparent K D of 94 ± 30 nM. To explain this difference we assessed whether the N-terminal 'coiled-coil' domain is involved in the interaction, but found it to be dispensable.
, 2014; Gray et al. , 2015). The concentration of LpoB injected ranged from 19. 5 nM to 10 μM. The concentration of CpoB versions injected ranged from either 3. 9 nM to 2 μM or from 78 nM to 40 μM. To assay binding in reducing conditions 2 mM TCEP was included in the SPR running buffer, and PBP1B versions immobilised to the chip surface were incubated in this buffer for 30 min before the initiation of binding assays. The dissociation constant (K D ) was calculated by non-linear regression using SigmaPlot 11. 5 software Bocillin binding of the purified PBP1B versions was assessed by incubating 1 μg of each version in 20 μL of 20 mM HEPES/NaOH pH 7. 5, 5 mM MgCl 2 , 150 mM NaCl, 0. 05% Triton X-100 with or without 1 mM ampicillin at 37°C for 30 min. After which 50 ng of BOCILLIN TM FL (Bocillin; Invitrogen, USA) was added before further incubation at 37°C for 30 min.
Accession numbers Coordinates of 20 structures and chemical shifts of UB2H with/without LpoB have been deposited in the Protein Data Bank and the BioMagResBank under the accession codes 6G5R/6FZK and 34255/34246, respectively. Coordinates of 20 structures and chemical shifts of CpoB TPR have been deposited in the Protein Data Bank and the BioMagResBank under the accession codes 6G5S and 34256, respectively.
structure determination, NMR spectra were recorded at 20°C on a 0. 5 mM [ 13 C, 15 N]-UB2H sample in 10 mM Tris/ HCl, 200 mM NaCl buffer at pH 7. 5 containing 10% D 2 O, or a 2 mM [ 13 C, 15 N]-UB2H sample with 1. 2 molar ratio of unlabelled LpoB in the same buffer, or a 2 mM [ 13 C, 15 N]-CpoB TPR sample in the same buffer. For each sample assignment of 1 H, 13 C and 15 N backbone and side-chain resonances was performed using a set of 3D heteronuclear experiments including BEST-TROSY-HNCACB, BEST-TROSY-HN(CO)CACB, HNCO, HN(CA)CO, hCCH-TOCSY and HcCH-TOCSY. These datasets were collected on Bruker Avance spectrometers operating at 700 or 800 MHz 1H-frequency equipped with triple-resonance cryogenic probes. 3D 15 N-NOESY-HSQC, 3D aliphatic and aromatic 13 C-NOESY-HSQC experiments were acquired with mixing times of 150 ms on a Bruker Avance US2 spectrometer equipped with a triple-resonance cryogenic probe to obtain structural restraints (see below).
Acknowledgements We thank  Natalie Strynadka  for critical reading of this manuscript and  H Mori  and his lab for sharing the unpublished E. coli single gene deletion strain (ΔmrcA::cat). We also thank  Athanasios Typas  for hosting AJFE in his lab to perform quantitation of growth fitness. This work was supported by  Wellcome Trust  grant  101824/Z/13/Z  to WV; Microbiology society (UK) research visiting grant  RVG17-03  to AJFE;  JPI-AMR  grant  ANR-14-JAMR-0003  to WV and JPS; GRAL ( French Labex ) postdoctoral fellowship to RM-M. This work used the NMR platform of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
For structure determination, NMR spectra were recorded at 20°C on a 0. 5 mM [ 13 C, 15 N]-UB2H sample in 10 mM Tris/ HCl, 200 mM NaCl buffer at pH 7. 5 containing 10% D 2 O, or a 2 mM [ 13 C, 15 N]-UB2H sample with 1. 2 molar ratio of unlabelled LpoB in the same buffer, or a 2 mM [ 13 C, 15 N]-CpoB TPR sample in the same buffer. For each sample assignment of 1 H, 13 C and 15 N backbone and side-chain resonances was performed using a set of 3D heteronuclear experiments including BEST-TROSY-HNCACB, BEST-TROSY-HN(CO)CACB, HNCO, HN(CA)CO, hCCH-TOCSY and HcCH-TOCSY. These datasets were collected on Bruker Avance spectrometers operating at 700 or 800 MHz 1H-frequency equipped with triple-resonance cryogenic probes. 3D 15 N-NOESY-HSQC, 3D aliphatic and aromatic 13 C-NOESY-HSQC experiments were acquired with mixing times of 150 ms on a Bruker Avance US2 spectrometer equipped with a triple-resonance cryogenic probe to obtain structural restraints (see below).
Letters below the mean values indicate homogeneous groups between zones (small letters) or LD stations (capital letters) according to post hoc Scheffé tests. Zooplankton stocks are estimated from the cumulated biovolume of binocular-counted organisms. Swimmer biomass in sediment trap from  Caffin et al. (2018a     Sediment trap data at day 5 cannot be considered for the analysis (see  Caffin et al. , 2018a). ton biomass decreased to an average of 22 % and showed a declining trend from west to east, with the lowest value of 7 % occurring at SD-15. The integrated phytoplankton standing stock derived from the water-column-integrated content of total chlorophyll a within the euphotic layer (Table  6 ) was highest at the LD-B and the lowest in the GY, although the stock generally decreased from west to east along the transect, as seen in Fig.
Acknowledgements Grants  SAF2016-78370-R ,  CB16/11/00399  ( CIBER CV ) and  RD16/0011/0021  (TERCEL) from the  Spanish Ministry of Economy, Industry and Competitiveness (MEIC)  and a grant from the  Fundación BBVA  (Ref. :  BIO14_298 ) and  Fundación La Marató  (Ref. : 20153431) to J. L. d. l. P. J. M. P. -P. was supported by grant  RD16/ 0011/ 0030  (Tercel). The cost of this publication was supported in part with  FEDER  funds. The CNIC is supported by the  MEIC  and the  Pro-CNIC Foundation , and is a  Severo Ochoa Center of Excellence  ( SEV-2015-0505 ).
MALDI-TOF MS and 16S rRNA gene sequencing identification The individual identification of isolated colonies was first attempted using MALDI-TOF MS, as previously described  [5, 6]. The reference spectrum obtained for each colony was compared with the Bruker database using the MALDI BIOTYPER software version 3. 0 (Bruker Daltonics, Bremen, Germany). Any score <1. 9 was considered unreliable. In this case, colonies were subjected to 16S rRNA gene amplification and sequencing, using a GeneAmp 2720 thermal cycler (Applied Biosystems, Foster City, CA, USA) and an ABI Prism 3130xl Genetic Analyzer capillary sequencer (Applied Biosystems) as previously described  [8]. Each 16S rRNA nucleotide sequence was compared with the nr database of the National Center for Biotechnology Information using the BLAST software (https:// blast.
ncbi. nlm. nih. gov/). We used the 16S rRNA sequence similarity thresholds of 95% and 98. 65% proposed by Kim et al. to consider bacterial isolates as putatively belonging to a new genus or a new species without performing DNA-DNA hybridization  [9]. Finally, to determine the phylogenetic position of strain Marseille-P3237 with regard to species with standing in nomenclature, its 16S rRNA gene sequence was compared with the 16S rRNA database of the 'All-Species Living Tree' project of Silva (LTPs121)  [10]. Sequence alignment was obtained using Muscle  [11]  and phylogenetic relationships were inferred with the maximum-likelihood method within the FastTree software  [12].
An automated cluster generation and sequencing run were performed in a single 39-h run in a 2 × 251-bp. Total information of 11. 1 Gb was obtained from a 1332 K/mm 2 cluster density with a cluster passing quality control filters of 87. 9% (21 937 000 passing filter paired reads). Within this run, the index representation for strain Marseille-P3237 was determined to be 3. 05%. The 668 978 paired-reads were trimmed and then assembled. The genome of strain Marseille-P3237 was assembled, annotated and compared with other closely related species as previously described  [5]. The genomic comparison included Olsenella profusa (GenBank accession number NZ_AWEZ00000000. 1), Olsenella uli (NZ_JQCO00000000. 1), Slackia exigua (ACUX00000000), Atopobium vaginae (NZ_ACGK00000000. 2), Atopobium parvulum (NC_013203.
Strain identification and phylogenetic analysis The identification of strain Marseille-P3237 using MALDI-TOF MS failed. The generated reference mass spectrum (Fig. 1 ) was added to the URMS database (http://www. mediterraneeinfection. com/article. php?larub=280&titre=urms-database). Strain Marseille-P3237 exhibited a 93. 05% sequence identity with O. uli (GenBank accession number AF292373), the phylogenetically closest species with a validly published name (Fig. 2 ). This 16S rRNA gene sequence divergence being greater than 5%, we investigated whether strain Marseille-P3237 could be the representative strain of a new species within a new genus (Table  1 )  [13]. The 16S rRNA gene sequence from strain Marseille-P3237 was submitted to EMBL-EBI under accession number LT598582. The MALDI-TOF MS spectrum of strain Marseille-P3237 was compared with those of other members of the Coriobacteriaceae family (Fig.
In addition, it was urease negative and could not acidify The major fatty acid is 9-octadecenoic acid (18:1n9, 38 %), followed by hexadecanoic acid (16:0, 28 %) and octadecanoic acid (18:0, 11 %). The 16S rRNA gene and genome sequences are deposited in the EBI/EMBL database under accession numbers LT598582 and LT671675, respectively. The G+C content of the genome is 65. 46%. Habitat: human digestive tract. Type strain Libanicoccus massiliensis strain Marseille-P3237 T (= CSUR P3237 = CCUG 71182).
Experimental design The model domain extends in latitude from 5 • S to 30 • N and in longitude from 34 to 78 • E, thus encompassing the whole Arabian Sea OMZ as well as the key dynamical features of the circulation of the region such as the Somali and Omani upwelling systems and the Great Whirl. The model horizontal grid has a resolution of 1/12 • with an average grid spacing that is about 5 to 20 times smaller than the local first baroclinic Rossby deformation radius  (Chelton et al. , 1998). This permits an explicit representation of a large fraction of the region eddies. The vertical grid consists of 32σ layers with an improved resolution near the surface. The seafloor topography is derived from the ETOPO2 dataset supplied by the National Geophysical Data Center  (Smith and Sandwell, 1997).
The model is forced with a monthly climatology. The absence of interannual variability in the atmospheric forcing enables us to quantify the role of internal variability associated with mesoscale eddies. The temperature, salinity and currents are initialized and laterally forced using the Simple Ocean Data Assimilation (SODA) ocean reanalysis. Oxygen and nitrate initial and boundary conditions are derived from  Garcia et al. (2010a, b)  dataset. The atmospheric bound-ary conditions for heat and freshwater fluxes are based on the Comprehensive Ocean-Atmosphere Data Set (COADS; da  Silva et al. , 1994). We applied surface restoring to COADS-observed surface temperatures and salinities using kinematic flux corrections described in  Barnier et al. (1995). The model is forced with wind stress data from the QuikSCAT-derived Scatterometer Climatology of Ocean Winds (SCOW;  Risien and Chelton, 2008).
Finally, comparing the modeled export fluxes in the deep ocean (500 m above the seafloor) to sediment trap data at the same sites yields a good agreement between the model and the observations in all stations (Fig. A6b ). It is worth noting that these deep export flux estimates can be considered as more robust than those at 100 m, as they are based on a larger number of independent measurements (20-40 measurements at each site). The simulated surface distribution of nitrate is consistent with observations from the World Ocean Atlas dataset in both seasons (Fig. A7 ). In particular, the high surface concen- trations associated with the summer upwelling in the western Arabian Sea and the winter convection in the northern Arabian Sea are captured by the model. Finally, the simulated oxygen distributions at depth compare generally well with observations (Fig.
3 ). In particular, the location, the size and the depth of the Arabian Sea OMZ (defined here by O 2 < 60 mmol m -3 ) are correctly reproduced. However, the model overestimates the intensity of the OMZ in the northern Arabian Sea and off the Indian west coast. This might be driven by the model underestimated eddy activity that may lead to underestimated OMZ ventilation as shown by  Lachkar et al. (2016). This might also partly result from the misrepresentation and likely underestimation of the injection of the Gulf waters into the northern Arabian Sea at intermediate depths. A more quantitative evaluation of model skill is performed using in situ observations from the World Ocean Database (2013) that we binned into a 0. 5 • monthly climatol-ogy. No spatial interpolation is made, and the model and observations are compared at the observation points.
69 in summer to 0. 74 in winter (Fig. 4a ). Despite the underestimation of the penetration of the warm and salty surface waters in the northern Arabian Sea at depth, the largescale distributions of temperature and salinity in the upper ocean are captured by the model as indicated by low rms errors and very high correlations with in situ observations (r > 0. 91) for both temperature and salinity (Fig. 4b ). Additionally, the model shows an overall high skill in reproducing the observed large-scale distributions of both nitrate and oxygen, with correlations with the World Ocean Database ranging in the upper 200 m from 0. 88 for nitrate in summer months to around 0. 93 for oxygen in both seasons (Fig. 4b ). In summary, despite some local biases, the model generally shows reasonable skill in reproducing the large-scale features of the circulation in the Arabian Sea as well as the seasonal dynamics of phytoplankton blooms in the region.
The reference point of the Taylor diagram corresponds to (a) SeaWiFS observations for chlorophyll, AVHRR data for surface temperature and AVISO climatology for sea surface height anomalies and (b) World Ocean Database (2013) for all four variables. The radius (distance to the origin point) represents the modeled standard deviation relative to the standard deviation of the observations. The angle between the model point and the x axis indicates the correlation coefficient between the model and the observations. Finally, the distance from the reference point to a given modeled field represents that field's centered rms with respect to observations. soon, and vice versa. This will also allow us to disentangle the impacts of the changes in each monsoon season and determine their respective contributions to the overall response.
Version5. 2/. The AVISO geostrophic velocity anomalies can be accessed at https://www. aviso. altimetry. fr/index. php?id=1272. The surface drifter climatology of  Lumpkin and Johnson (2013)  can be downloaded from ftp://ftp. aoml. noaa. gov/phod/ pub/lumpkin/drifter_climatology/version_2. 06/. The NPP data can be downloaded from http://www. science. oregonstate. edu/ocean. productivity/custom. php. Finally, the SeaWiFS chlorophyll a data can be accessed at https://podaac. jpl. nasa. gov/dataset/SeaWiFS_ L3_CHLA_Monthly_9km_R. ). (d) Simulated MOC at 8 • N under different wind scenarios using two model resolutions. We compute the meridional overturning circulation in oxygen coordinates following  Lachkar et al. (2016). This approach allows us to characterize the ventilation of the OMZ driven by both the mean meridional circulation and eddy mixing along isopycnals.
Acknowledgements. Support for this research has come from the  Center for Prototype Climate Modeling (CPCM) at New York University Abu Dhabi (NYUAD). This research was carried out on the high-performance computing (HPC) resources at NYUAD. We thank  Benoit Marchand ,  Muataz Al Barwani  and the whole NYUAD HPC team for technical support. We are thankful to  Nicolas Gruber  for allowing access to the biogeochemical model code. Edited by: Tina Treude Reviewed by: two anonymous referees.
Code availability. The model code can be accessed online at http: //www. croco-ocean. org/. The model outputs are available from the authors upon request. Data availability. The Simple Ocean Data Assimilation (SODA) reanalysis data can be downloaded from http://apdrc. soest. hawaii. edu/datadoc/soda_2. 2. 4. php. The World Ocean Atlas 2009 data can be downloaded from https://www. nodc. noaa. gov/OC5/ WOA09/pr_woa09. html. The World Ocean Atlas 2013 data can be accessed at https://www. nodc. noaa. gov/OC5/woa13/. The Comprehensive Ocean-Atmosphere Data Set (COADS) data can be downloaded from https://data. nodc. noaa. gov/cgi-bin/iso? id=gov. noaa. nodc:ICOADS-netCDF. The Scatterometer Climatology of Ocean Winds (SCOW) data can be downloaded from ftp://cioss. coas. oregonstate. edu/pub/scow/.
g. Fettweis et al. , 2011 Fettweis et al. , , 2013b;; Rae et al. , 2012). This raises the question of how RCM-based projections of future GrIS SMB are affected by the GCM forcing if the recent shift to negative NAO phases in summer persists through the next decades? To address this issue, we use the Modèle Atmosphérique Régional (MAR), especially developed for polar regions, to perform a sensitivity study based on the analysis of MARderived GrIS SMB anomalies resulting from various forcing experiments with ERA-Interim reanalysis and three GCMs from the CMIP5 database (Climate Model Intercomparison Project Phase 5;  Taylor et al. , 2012). 2 Data and methodology 2. 1 The regional climate model MAR The model MAR is a RCM specifically developed for simulating polar climate specificities (e. g.
GCM forcing We performed three additional experiments in which MAR was forced over 1980-2060 with three GCMs from the CMIP5 database, using the historical scenario over 1980-2005 and the future RCP4. 5 scenario  (Moss et al. , 2010)  over 2005-2060. Then, for each GCM, three 20-year periods (different for each GCM) characterised by a climate about ∼ +1, ∼ +1. 5, and ∼ +2 • C warmer on average than the climate over the reference period (as represented by the GCM) of 1980-1999 were considered (Table  1 ). The differences between the GCM periods are only due to the timing required to reach each relative warming. Note that each warming was computed as the mean JJA (June-July-August) temperature anomaly compared to 1980-1999 over the computation domain (100-0 • W, 55-85 • N) at four vertical levels in the free atmosphere (850, 700, 600, and 500 hPa).
Conclusions The goal of this study is to assess the impact of unresolved recent atmospheric circulation change in GCMs on RCMbased projections of future GrIS SMB. For this purpose, we used the RCM MAR and performed forcing sensitivity experiments with the ERA-Interim reanalysis and large-scale fields from three selected GCMs of the CMIP5 database to investigate the influence of a warmer atmosphere in the context of a circulation change inducing more frequent blocking events. We used the annual SMB produced with each original forcing over the climatologically stable period of 1980-1999 as a reference to compute mean annual SMB anomalies relative to each forcing experiment. The first experiments consisted of increasing the atmospheric temperature in the ERA-Interim reanalysis by +1, +1. 5, and +2 • C at the MAR lateral boundaries over two distinct  periods, i.
Acknowledgements. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. FNRS)  under grant no. 2. 5020. 11and the Tier-1 supercomputer (Zenobe) of the  Fédération Wallonie Bruxelles  infrastructure funded by the  Wallonia region  under the grant agreement no. 1117545. Finally, we would like to thank the two anonymous reviewers for their constructive remarks that helped to improve the paper. Edited by:  Marco Tedesco Reviewed  by: two anonymous referees.
Data availability. All MARv3. 8 outputs presented here are available upon request by email (alison. delhasse@uliege. be), and the source code of MARv3. 8 is available at ftp://ftp. climato. be/fettweis/ MARv3. 8/. src/  (Fettweis et al. , 2017). The ECMWF reanalyses (ERA-Interim) were downloaded from http://apps. ecmwf. int/ datasets/.
We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval. lri. fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.
Most of these methods aim to find known modules in new structures. A few methods aim to search for modules without any prior knowledge of their geometry or topology  (11, 15) , but they only consider local interactions. Databases, as the RNA 3D Motif Atlas  (6) , and RNA Bricks  (21)  store information on the RNA modules which have been found in experimentally determined RNA tertiary structures. Regarding especially RINs, apart a preliminary attempt  (22) , no automated method has been developed up to now to detect them in tertiary structures and to classify them without any a priori knowledge of their geometry or topology. We developed a graph-based methodology to extract all RINs in crystallized RNA tertiary structures and to cluster them according to their similarity. We applied our methodology to a large set of experimentally resolved RNA structures.
Not only we retrieved the known RINs (as the different types of A-minors), but we also extracted new ones. Our method gives a global view on interaction networks and their modularity, by organizing them in families according to their inclusion relations. The publicly accessible database CaRNAval http://carnaval. lri. fr allows to visually explore and study all the interaction networks and their intricate relationships. We further analyze our data and expose the remarkable diversity of the well known A-minor networks. In particular, we show that an unexpected number of unrelated structures form the exact same intricate network of interactions. Furthermore, the diversity of the molecules in which several of these networks are found (e. g. ribosomes, ribozymes and other non-functionally related RNAs) underlines the universality and fundamental nature of these recurrent architectures.