dataesr/scientific-paragraphs-categorization huggingface dataset:
3 in both cases). We then extracted the scores for the first db-RDA axis and tested whether they were significantly associated with PHA response and/ or Julian date using LME. We argue that this analysis method is preferable to the default anova. cca, as this function cannot effectively F I G U R E 2 Summary of barn swallow GM taxonomic content. Rare taxa (represented by <2% reads) are labeled as "others" account for pseudoreplications induced by sampling multiple individuals from the same nest. Associations between abundance of OTUs and PHA response were tested using generalized LMEs from R package BhGLM for data with negative binomial distribution of errors  (Zhang et al. , 2017). OTU-specific read counts within individual samples were included as a response, while Julian date of sample collection and PHA response were included as explanatory variables.
Log-transformed total number of reads per sample was specified as model offset (i. e. , assuming number of reads per given OTU to be proportional to total number of reads per individual sample) and clutch identity as random effect. The qvalue method  (Storey & Tibshirani, 2003)  was subsequently used to account for false discoveries due to multiple testing. To optimize sensitivity of OTU-level analyses, we applied "independent filtering" procedure  (Bourgon, Gentleman, & Huber, 2010)  using DESeq2 R package  (Love, Huber, & Anders, 2014)  and considered only OTUs that passed this step (n = 196 OTUs, representing 96% of all high-quality reads). Procrustean analysis revealed high congruence between original and subsetted microbial profiles (Procrustean r = 0. 9974, Procrustean sum of squares = 0.
Genomic analysis The NRCM performs systematically MLST analysis on all IMD cases (cultured and PCRconfirmed cases) since 2010 as previously described  (18, 20, 21). Typing data are expressed as a genetic formula: G :P1. PorA-VR1,PorA-VR2:FetA :cc that define the group (G), the two variable regions (VR1 and VR2) of the outer membrane protein PorA, and of one VR of the protein FetA, as well as the cc. Moreover, WGS is also performed systematically on all invasive cultured isolates since late 2015. For this work we retrospectively performed WGS on all cultured NmW isolates since 2010. Genomic DNA was extracted by using a Genomic-tip 20/G kit (QIAGEN, Valencia CA, USA). Sequencing was performed by Illumina HiSeq 2000 sequencer (Illumina, San Diego, CA, USA) and assembled as previously described  (21).
Sequences are available through the PubMLST database which runs on the Bacterial Isolate Genome Sequence Database (BIGSdb) platform  (22). WGS data was analyzed using a "gene-by-gene" approach available through the PubMLST Genome Comparator tool using N. meningitidis core genome v1. 0 that includes 1605 core loci  (22). SplitsTree4 (version 4. 13. 1) was used to visualise the resulting distance matrices as Neighbour-net networks  (23). For the genomic analysis we included three reference strains according to Lucidarme et al  (24) : one French isolate related to the "Anglo-French Hajj" sublineage (ID31165), one isolate related to the "original UK strain" (ID20196) and one isolate related to the "UK 2013-strain" (ID30167). The IDs of all these isolates are given in the supplementary Table to allow retrieving of WGS sequence in FASTA formats.
Data analysis Two databases were used in this study. The first one relates to IMD cases reported through mandatory notification and includes clinical, epidemiological data as well as microbiological data (technique for diagnosis and group identified by the local hospital or by the NRCM). The second database includes data on all NmW invasive strains characterized by the NRCM. These two databases are highly dependent but do not completely overlap. Cases in the database of the NRCM included PCR since 1998 while PCR was added on the criteria of the mandatory reporting in 2002. Data analysis was performed distinctly using these two databases except for the analysis of characteristics of cases by lineages/sub-lineages for which the databases were merged. Epidemiological and clinical characteristics of cases were studied by groups (NmW versus other groups) and by NmW lineages and sub-lineages identified by WGS.
Genomic analysis of NmW cultured isolates To describe the evolutionary relationships of the isolates and particularly those of the cc11, all 132 cultured isolates (from the period 2010-2016) were subjected to WGS and the data were uploaded to the PubMLST database and analysed using the BIGSdb tools in this database. Isolates grouped into two major clusters corresponding to cc11 and cc22. Isolates belonging to other cc were distinct from these two clusters (Fig. 3A ). Isolates belonging to "cc22" and to "other cc" showed a higher diversity than "cc11" as suggested by their genetic distances (Fig. 3 B  and C ). Eighty-four isolates belonged to cc11 (of which 70 were identical by MLST) and showed differentiation into two groups that corresponded to the "Anglo-French-Hajj" sublineage and to the "South American-UK" sub-lineage (Fig  3A ).
Epidemiological characteristics of NmW cases according to WGS data Of 132 isolates typed by WGS, 127 could be matched to clinical and epidemiological data collected through mandatory notification. We compared the characteristics of cases according to four groups defined using WGS data: the "original UK strain", the "UK 2013-strain", the "Anglo-French Hajj" sub-lineage and non-cc11 lineages (Table  2 ). There was no overall statistical difference in the distribution of cases by age group between these four groups. However a sharp increase in the number of cases due to the 'UK 2013-strain" was observed in 2015 and 2016 in individuals aged 15 years old and over (Fig. 5 ). In total, there were 36 IMD cases due to the "UK 2013-strain" among which 34 (94%) occurred in adults aged 15 years old and over.
Acknowledgements This publication made use of the Neisseria Multi Locus Sequence Typing website (https://pubmlst. org/neisseria/)developed by  Keith Jolley  and sited at the  University of Oxford  ( 22 ). The development of this site has been funded by the  Wellcome Trust  and  European Union. We also acknowledge the  PIBNET-P2M  platform at the  Institut Pasteur. We would like to thank all biologists and clinicians who notified cases, as well as the regional health agencies and  Santé Publique France regional units (Cire)  for their contribution to IMD surveillance, and also the persons involved in data management at  Santé Publique France (Catherine Maine  and  Yann Savitch  for data entry and  Mireille Allemand  for mapping).
A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series.
It is the purpose of the present paper to describe a method and a derived dataset that allow us to shed more light on the development of the age distribution of the Arctic sea ice. For this purpose, we have taken advantage of some new datasets on sea ice drift and concentration developed and distributed by the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF). In addition, we have developed a new Eulerian scheme of advection supported by the Sea Ice Climate Change Initiative (SICCI) project of the European Space Agency (ESA). These improvements have allowed us to avoid the problem of the tracers dispersion  (Rybak and Huybrechts, 2003)  and to produce a new SIA dataset which in each grid box contains not only the age of the oldest ice, but the actual age distribution provided as fractions of ice of different age categories (hereafter referred to as "sea ice age fractions").
Sea ice drift Information on sea ice motion was acquired from two sources. First, the National Snow and Ice Data Center (NSIDC) sea ice drift (SID) product v. 0116  (Tschudi et al. , 2016c)  was downloaded from the NSIDC portal  (Tschudi et al. , 2016b). Weekly SID fields from NSIDC were accessed on an Equal Area Scalable Earth (EASE) grid  (Brodzik et al. , 2012)  with 25 km spacing for the period from October 1978 to December 2015. As pointed out by  Szanyi et al. (2016)  this ice drift product contains artifacts due to the composition of the ice drift derived from satellite data, observed by in situ buoys and predicted using a simple free drift model. On short timescales, these artifacts result in large openings in the predicted distribution of MYI which are filled with the first-year ice (FYI).
retrievals developed at University of Bremen is based on the ECICE algorithm using brightness temperatures from AMSR2 and radar backscatter from C-band scatterometer ASCAT. Two correction schemes were applied to the initial MYI concentration: one using temperature records from atmospheric reanalysis to identify MYI anomalies caused by warm spells and replace with interpolated MYI concentrations  (Ye et al. , 2016a)  and the other utilizing mainly ice drift records to constrain the MYI changes within a plausible contour  (Ye et al. , 2016b). The improved MYI concentrations were provided as gridded products on polar stereographic grid with 12. 5 km spatial resolution for the winter months (November-April) of 2013-2017. Compared to the MYI dataset, for which the corrections were developed, the retrievals from AMSR2 and AS-CAT are much coarser (12.
5 km vs. 4. 45 km). The coarser resolution requires adjustment of the thresholds in the drift correction, which results in suboptimal performance of the drift correction and therefore could lead to unexpected problems in the final dataset. The OSI SAF sea ice type is another algorithm that combines both passive and active microwave data in a Bayesian approach  (Aaboe et al. , 2017)  that computes the probability of occurrence of the most likely ice type -first-year ice or multi-year ice. The OSI SAF type product is a near-realtime product that has been operational since 2005 with data available from the OSI SAF High Latitude processing center (http://osisaf. met. no). Since the start of operational production the algorithm has been upgraded several times by including new sensors and improving the methods.
Data availability. The data generated with the algorithm are openly available at FTP (for bulk download; ftp://ftp. nersc. no/ ArcticData/esa_cci_sea_ice_age/) and at THREDDS (subsetting and online visualization; http://thredds. nersc. no/thredds/arcticData/ esa-cci-sea-ice-age. html) in netCDF format following CF conventions containing values of sea ice age fractions concentrations, MYI concentration, and sea ice age computed using weighted average (Korosov amd Rampal, 2018). Detailed information on datasets used in this paper can be found in Sect. 2.
Of particular interest to understand the surface dynamics, SVP (Surface Velocity Program) floats were launched during each LD station to investigate the dispersion and the surface circulation relative to 15 m during the sampling period and beyond  (Lumpkin and Pazos, 2007). Three SVPs were launched during LDA, six during LDB and four during LDC. The in situ trajectories of the floats were used to validate altimetry-derived surface velocities (see Sect. 2. 2, 2. 4 and Fig. 2 ). Continuous measurements of temperature and salinity were achieved using a thermosalinograph (TSG) that pumped sea water at 5 m depth. TSG data have been corrected and calibrated using independent measurements of salinity from water bottle samples collected daily onboard RV L'Atalante (following the procedures described by  Alory et al.
side-scatter plot. Prochlorococcus were further distinguished from Synechococcus by their relative lack of a phycoerythrin signal (orange fluorescence). Using a FSC detector with small particle option and focusing a 488 nm plus a 457 nm (200 and 300 mW solid state, respectively) laser into the same pinhole greatly improved the resolution of dim surface Prochlorococcus population from background noise in unstained samples. Because the Prochlorococcus population cannot be uniquely distinguished in the SYBR stained surface samples, bacteria were determined as the difference between the total cell numbers of the SYBR stained sample and Prochlorococcus enumerated in unstained samples. Cytograms were analysed using FCS Express 6 Flow Cytometry software (De Novo Software, CA, US). These data are used to investigate the small-scale distribution of the different microbial community groups and its relation with the concomitant dynamics at submesoscale.
Satellite data Several satellite datasets were exploited during the campaign to guide the cruise through an adaptive sampling strategy using the SPASSO software package (http://www. mio. univ-amu. fr/SPASSO/, last access: 16 April 2018) following the same approach as described for previous cruises such as LATEX  (Doglioli, 2013; Petrenko et al. , 2017) and KEOPS2 (d'Ovidio et al. , 2015). SPASSO was also used after the cruise in order to extend the spatial and temporal vision of the in situ observations. Four different altimetry-derived velocity products were tested in this study to choose the product that best represents the surface circulation during the cruise. First the daily Ssalto/Duacs product  (Ducet et al. , 2000) , from the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic 3) database, for the period from 1 January 2004 to 31 December 2015, was used to extract daily delayed-time maps of absolute geostrophic velocities (1/4 • × 1/4 • on a Mercator grid, since 15 April 2014).
Thus the choice of the best surface velocity product relied on the best fit between observed and numerical trajectories (see Sect. 2. 4). To study the large-scale circulation in the WTSP, quantitative experiments were performed to find the main pathways of the waters entering and exiting the box contouring the WTSP (Fig. 3 ). Particles were launched along each section of the box (north, east, south, west) and advected forward with the total altimetry-derived surface currents. We simulated 10 years of particle trajectories by repeating 10 times the available dataset. We compared the results of this simulation with the 10-year integration of available geostrophic AVISO surface currents over this time period. The comparison between these simulations ensures that the use of a 1-year time period looped several times does not significantly modify statistical outputs.
It is clearly more interesting to use CLS data instead of commonly used AVISO geostrophic surface currents because they include the wind effect and cyclogeostrophy with higher resolution, as well as better fitting with in situ data. Another objective is to identify the mesoscale trajectories of surface water masses sampled at each of the LD stations. Backward and forward quantitative experiments were performed to identify the main pathways of the surface water masses arriving and leaving each LD station using total altimetry-derived surface currents. Almost 1 million numerical particles were initially distributed along a square box surrounding the position of the LD station. The calculations were stopped whenever the particles return to the initial box (hereafter called meanders) or were intercepted on one of the four remote sections located around the LD station.
Eddies and filaments identification To set up the mesoscale context during the OUT-PACE cruise, we used the Lagrangian averaged vorticity deviation method  (Hadjighasem and Haller, 2016; Haller et al. , 2016)  that allows identification of coherent structures from altimetry-derived surface velocity fields (code available at https://github. com/Hadjighasem/ Lagrangian-Averaged-Vorticity-Deviation-LAVD, last access: 16 April 2018). The detected features are able to trap water masses for a certain period (defined by the integration time) and transport them along their route. In this study we computed the detection with the total altimetry-derived velocity field and chose an 8-day time integration with respect to the duration of LD stations. Indeed, this time interval provides a confirmation or rebuttal of the assumption that LD stations have been performed in a coherent structure, as targeted during the cruise.
5 and -1 • C which allows us to confidently use satellite temperature. For chlorophyll, the differences are smaller than ±0. 1 mg m -3 which is also a reasonable deviation between satellite and in situ measurements, besides considering the colour bar scale of Fig. 6  with values that vary from 0 to 1 mg m -3. We can also note that the satellite data clearly underestimate chlorophyll concentrations in the Melanesian area. As both datasets are comparable, SST and chlorophyll concentrations from remote sensing are then used to investigate horizontal gradients in the WTSP (Fig. 6a  and b ). To assess the spatial scale of submesoscale gradients (typical of 1-10 km) in terms of ocean dynamics, we use a Lagrangian methodology based on the calculation of FSLE (see Sect. 2. 3. 2) that allow for front detection.
Acknowledgements. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (Moutin and Bonnet, 2015) funded by the  French national research agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER programme (CNRS-INSU) , the  GOPS programme (IRD)  and  CNES  ( BC T23 ,  ZBC 4500048836 ). Solange Duhamel was funded by the  National Science Foundation  ( OCE-1434916 ). The authors thank the crew of the  RV L'Atalante  for outstanding shipboard operations, and  Gilles Rougier ,  Dominique Lefevre  and  Francesco d'Ovidio  for their valuable help with different datasets. We also thank  Cécile Dupouy  for providing high-frequency chlorophyll concentrations. This work is supported by  CLS  in the framework of CNES funding, and the authors would like to thank  Marie-Isabelle Pujol  and  Guillaume Taburet  for providing enhanced satellite data.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/outpace/outpace. php (INSU/CNRS LEFE CYBER, 2017). www. biogeosciences. net/15/2411/2018/ Biogeosciences, 15, 2411-2431, 2018.
A c c e p t e d M a n u s c r i p t More information on the Observatory Sites can be found on the Radioecology Exchange website  [13, 14]. The Observatory Working groups were formed under the COMET project to coordinate the R&D activities performed in the four Observatories selected. For the observatories to be successful in the future there needs to be a willingness amongst ALLIANCE The intensity of future studies that will be possible at the Observatories is dependent upon funding. The ALLIANCE can act as a coordinator and help develop Observatory Sites (e. g. by making data available), but it cannot finance research at the sites.
The Virtual Laboratory provides access to methods, procedures and protocols, facts, figures and data useful to radioecology. The Information Exchange provides access to STAR and COMET partner publications, publication catalogues, newsletters, FAQ's and links to other websites of interest to radioecology. In preparation for the end of the COMET project, the bespoke STAR  [2]  and COMET  [4]  websites have been incorporated into the Radioecology Exchange. The STAR and COMET web addresses now redirect to the Radioecology Exchange. Natural Environment Research Council in Centre for Ecology and Hydrology (UK) has purchased all three website addresses until at least 2022. After the end of the COMET project, some of the pages on the Radioecology Exchange will be 'frozen' and no longer updated (e.
To ensure effective collaboration and integration, an inventory of infrastructure covering equipment, methods, bioinformatic resources, sample archives, models, expertise and facilities for radioecological research was created under STAR. The Observatory Sites are a special type of 'infrastructure'. The resultant list of the facilities available for others, either as commercial services or through research collaboration, is on the Radioecology Exchange under "Virtual laboratory, Equipment and facilities". The listing is largely restricted to STAR partners. The infrastructure-related work is at the moment being further developed under the EJP-CONCERT Work Package 6 (WP6). The four main objectives of WP6 aim to increase the visibility A c c e p t e d M a n u s c r i p t of infrastructures: (i) prepare the criteria and to compile a list of infrastructures; (ii) prepare regular information about infrastructures; (iii) develop harmonised practices and protocols to strengthen and expand databases from past radiobiological experiments and from stored biological material; (iv) to develop strategies for facilitating access to infrastructures.
T: transcription terminator. B Flow cytometry measurements and fits to a theoretical model of relative GFP expression levels, where GFP is expressed from the artificial feedback loop presented in panel (A). GFP expression is normalized by the maximal level of GFP expressed constitutively from the P PhlF promoter alone (indicated as "No PhlF"). The GFP is repressed using four different guide RNAs with, respectively, 10, 11, 14, and 20 bp of complementarity. The passage probability 1 -P(stop) associated with each of these guide RNAs was measured in parallel on a strain expressing GFP constitutively from the P 127 promoter. Adding different amounts of DAPG to the medium reduces the strength of the feedback, causing the steady-state level to increase and repression to become more efficient. The colored lines represent the GFP expression as predicted by a mathematical model that was fitted to the data (see Appendix).
For each DAPG concentration, a binding constant characterizing the strength of the feedback and a Hill coefficient were determined. Error bars: 95% confidence interval of the mean based on three biological replicates. Source data are available online for this figure. (Lee et al, 2014). By gradually lowering the levels of PBP2 and RodA, we were able to take the cells out of the buffering regime at about 30% of native expression. Together, these experiments demonstrate that cells buffer stochastic gene expression of an essential operon against fluctuations of about threefold and that cells cope with even stronger fluctuations by Source data are available online for this figure. adjusting their surface-to-volume ratio. However, once expression levels are reduced by more than fivefold, cells show severe growth defects.
Further work and larger datasets of diverse sequences will help to design new guides with predictable repression strength on arbitrary targets. The model predicts that repression is promoter-strength dependent in non-saturating conditions, in quantitative agreement with experiments (Fig  3C ). For full complementarity, our model is compatible with the hypothesis that dCas9 never leaves the target spontaneously but gets kicked out either by the RNAP or during DNA replication, consistent with the long half-life of dCas9 binding recently observed by single-molecule tracking and by restriction-protection assays  (Jones et al, 2017)  and previously also observed in vitro  (Sternberg et al, 2014). A recent high-throughput study of dCas9 off-target binding and unbinding suggests that mutations in the PAM-distal region control the unbinding kinetics of dCas9  (Boyle et al, 2017).