dataesr/scientific-paragraphs-categorization huggingface dataset:
Groups of three sex-matched mice were housed in sterile OptiMICE cages (Animal Care Systems, Centennial, CO). Mice were gavaged with 200 µl of bacteria swabbed from a plate (to a final OD 600 of approximately 0. 4) into medium to ensure viability of the toxin-producing strain. Bacteria were either wild-type BtCL15T12C11 or the isogenic deletion mutant lacking the EH213_01844 bacteroidetocin B gene. After six days, fecal samples were collected and concentration of viable bacteria was quantified by dilution plating. A two-tailed Mann-Whitney test as calculated by Prism version 8. 0. 1 for 64-bit Windows (GraphPad Software, San Diego, CA) was used for statistical analysis of colonization levels between the two groups. Mouse fecal samples from the above experiment were diluted 1:10 in PBS and allowed to separate by gravity for 5 min to separate fibrous material, then, 20 µl from the top material was run on a 12% SDS-PAGE Bis/Tris gel (Invitrogen) and immunoblotted and probed with antibodies as described above.
Acknowledgements Many strains used in this study were obtained through  BEI Resources, NIAID, NIH  as part of the  Human Microbiome  Project (indicated in Supplementary Table 1). We thank  M. Waldor ,  J. Lee  and  E. Martens  for providing additional strains. We thank  V. Yeliseyev  and  R. Lavin  for assistance with mouse experiments. We thank  Ross Tomaino  for mass spectrometry analyses and helpful discussions. The gnotobiotic mouse facility is supported by  NIH  grant  P30DK034854  to the  Massachusetts Host-Microbiome Center at Brigham and Women's Hospital  with additional funding from the  Massachusetts Life Sciences Center. This work was supported by  Public Health Service  grant  R01AI093771  from the  NIH/National Institute of Allergy and Infectious Diseases. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Data availability Data underlying Figs 3c, 3d, 4b, 4c, 4e, 5b, 6a, 6b, and Supplementary Fig. 2  are provided as Source Data files. The metagenomics data sets containing all the raw data and associated metadata underlying Supplementary Data 1 are available as detailed in ref. 27. The three genomes described herein were deposited in GenBank under the following identifying numbers: Bacteroides thetaiotaomicron CL15T12C11, Genbank accession RWHY00000000, BioProject ID PRJNA506939, BioSample ID SAMN10476872; Bacteroides vulgatus CL01T12C17, Genbank accession RWHZ00000000, BioProject ID PRJNA506941, BioSample ID SAMN10476933; and Bacteroides vulgatus CL14T03C19 Genbank accession RWIA00000000, BioProject ID PRJNA506943, BioSample ID SAMN10477097. The authors declare that all other data supporting the findings of this study are available either within the paper (and its Supplementary Information files), or from the corresponding author on request.
In the context of mesoscale and sub-mesoscale interactions, the AlborEX dataset constitutes a particularly valuable source of information to infer mechanisms, evaluate vertical transport, and establish relationships between the thermal and haline structures and the biogeochemical variable evolution, in a region characterised by strong horizontal gradients provoked by the confluence of Atlantic and Mediterranean waters, thanks to its multi-platform, multi-disciplinary nature. The dataset presented in this paper can be used for the validation of high-resolution numerical models or for data assimilation experiment, thanks to the various scales of processes sampled during the cruise. All the data files that make up the dataset are available in the SOCIB data catalog at https://.
, 2018) , the Balearic Current system  (July and November 2007 , April and June 2008 , Bouffard et al. , 2010)  and the coastal current off the west of Ibiza  (August 2013 , Troupin et al. , 2015). Similar studies comparing almost synchronous glider and SARAL/AltiKa altimetric data on selected tracks have also been carried between the Balearic Is-lands and the Algerian coast  (Aulicino et al. , 2018; Cotroneo et al. , 2016). Recently, the efforts carried out by data providers and oceanographic data centres through European initiatives such as SeaDataNet (http://seadatanet. org/, last access: 24 December 2018) makes the creation and publication of aggregated datasets covering different European regional seas possible, including for the Mediterranean Sea  (Simoncelli et al. , 2014) , upon which hydrographical atlases are being built (e.
g. Simoncelli et al. , 2016; Iona et al. , 2018b). These atlases are particularly useful for the description of the general circulation, the large-scale oceanographic features, or for the assessment of the long-term variability  (Iona et al. , 2018a). However, their limitation to temperature and salinity variables (as of July 2018) and their characteristic spatial scale prevent them to be employed for the study of sub-mesoscale features. The AlborEx multi-platform experiment was performed in the Alboran Sea from 25 to 31 May 2014, with the objective of capturing meso-and sub-mesoscale processes and evaluating the interactions between both scales, with a specific focus on the vertical velocities. The observing system, described in the next section, is made up of the SOCIB coastal R/V, 2 underwater gliders, 3 profiling floats, and 25 surface drifters, complemented by remote-sensing data (sea surface temperature and chlorophyll concentration).
The AlborEx mission The mission took place from 25 May to 31 May 2014 in the Alboran Sea frontal system  (Cheney, 1978; Tintoré et al. , 1991 , see Fig. 1 ), the scene of the confluence of Atlantic and Mediterranean waters. The mission itself is extensively presented in  Ruiz et al. (2015)  and the features and processes captured by the observations are discussed in  Pascual et al. (2017). Olita et al. (2017)  examined the deep chlorophyll maximum variation, combining the bio-physical data from the gliders and the profiling floats. The present paper focuses solely on the description of the original dataset, graphically summarised in Fig. 1.
General oceanographic context The definitive sampling area was not firmly decided until a few days before the start of the mission. Prior to the experiment, satellite images of sea surface temperature (SST) and chlorophyll a concentration were acquired from the Ocean Color Data server (https://oceandata. sci. gsfc. nasa. gov/, last access: 3 August 2018) in order to provide an overview of the surface oceanic features apparent in the Alboran Sea. A well-defined front separating Atlantic and Mediterranean wa- ters and exhibiting filament-like structures was selected as the study area (see rectangular boxes in Figs. 1  and 2 ). The pair of images indicates that the front position slightly changed between 25 and 30 May. An anticyclonic eddy centred around 36 • 30 N, 0 • 30 W, according to altimetry data (not shown), slowly followed an eastward trajectory in the following days.
Data reuse Three main types of data reuse are foreseen: (i) model validation, (ii) data assimilation (DA), and (iii) planning of similar in situ experiments. With the increase of spatial resolution in operational models, validation at smaller scales requires high-resolution observations. Remote-sensing measurements such as SST or chlorophyll a concentration provide a valuable source of information but are limited to the surface layer. In the case of the present experiment, the position, intensity (gradients), and vertical structure of the front represent challenging features for numerical models, even when data assimilation is applied (Hernandez-Lasheras and  Mourre, 2018). The AlborEx dataset can be used for DA experiments, for example, assimilating the CTD measurements in the model and using the glider measurements as an independent observation dataset.
The assimilation of glider observations has already been performed in different regions (e. g. Melet et al. , 2012; Mourre and Chiggiato, 2014; Pan et al. , 2014)  and has been shown to improve forecast skills. However, the assimilation of high-resolution data is not trivial: the background error covariances tend to smooth the small-scale features present in the observations and the high density of measurements may require the use of super-observations (averaging the observations in the model cells). Another complication arises from the fact that the observational errors are correlated, while data assimilation schemes often assume those errors are not correlated. Finally, other observing and modelling programs in the Mediterranean Sea can also benefit from the present dataset, for instance the Coherent Lagrangian Pathways from the Surface Ocean to Interior (CALYPSO) in the southwestern Mediterranean Sea  (Johnston et al.
Processing levels For each of the platforms described in Sect. 2, different processing is performed, with the objective of turning raw data into quality-controlled, standardised data that are directly usable by scientists and experts. Specific conventions for data managed by SOCIB are explained below. All the data provided by SOCIB are available in different, so-called processing levels, ranging from 0 (raw data) to 2 (gridded data). The files are organised by deployments, a deployment being defined as an event initiated when an instrument is put to sea and finished once the instrument is recovered from sea. Table  1  summarises the deployments performed during the experiment and the available processing levels. Level 0 (L0). This is the level closest to the original measurements, as it is designed to contain exactly the same data as the raw files provided by the instruments.
Quality control Automated data QC is part of the processing routine of the SOCIB Data Center: most of the datasets provided with this paper come with a set of flags that reflect the quality of the measurements, based on different tests regarding the range Missing data of measurements, the presence of spike, the displacement of the platform, and the correctness of the metadata. The QC is based on existing standards for most of the platforms. They are extensively described in the Quality Information Document  (SOCIB Data Center, 2018). The description platform by platform is provided in the next Section.
It is worth mentioning the tests described above are not yet applied on the glider data, since their processing is done outside of the general SOCIB processing chain, but the tests have been implemented in the glider toolbox  (Troupin et al. , 2016 , and available at: https://github. com/socib/glider_ toolbox; last access: 24 December 2018) and will be made operational once they have been properly tested and validated. As the new files will not be available before a full reprocessing of all the historical missions, the decision was taken to provide the data files in their current state. A new version will be uploaded as soon as the processing has been performed.
Configuration The CTD rosette was equipped with the following instruments: a Sea-Bird SBE 911Plus, two conductivity and temperature sensors, and one pressure sensor units; a SBE 43 oxygen sensor; and a Seapoint  [FTU]  fluorescence and turbidity sensor. The GEONICA METEODATA 2000 weather station measured the following variables: air pressure, temperature, humidity, and wind speed and direction, with a resolution of 10 min. The continuous, near-surface measurements of temperature and salinity are provided by a SeaBird SBE21 thermosalinograph.
Gliders To collect measurements addressing the sub-mesoscale, two gliders were deployed on 25 May inside the study area. The  coastal glider carried out measurements up to a 200 m depth and the deep glider up to 500 m. The horizontal resolution was about 0. 5 km for the shallow glider and 1 km for the deep glider. The initial sampling strategy consisted of two 50 km long, meridional tracks, 10 km away from each other, and to repeat these tracks up to 4 times during the experiment. However, due to the strong zonal currents in the frontal zone, different tracks (Fig. 6 ) crossing the front several times were made instead. On 25 May at 19:24 (UTC), the deep glider payload suffered an issue with the data-logging software, resulting in no data acquisition during a few hours, during which the problem was being fixed.
Quality control Before deployment, the glider compass was calibrated following  Merckelbach et al. (2008). The thermal-lag happening on the unpumped Sea-Bird CTD sensors installed on the deep and coastal gliders was corrected using the procedure described in  Garau et al. (2011). The checks not yet applied but planned for the next release of the Glider toolbox include the removal of N aN values; the detection of impossible dates or locations; valid ranges (depending on depth) for the variables, spikes, and gradients; and constant values over a large range of depths in the profiles. The tests performed that showed a constant value check proved useful for conductivity (and hence density and salinity). A new version of the present dataset will be released once these new checks are made operational.
Finally, oxygen concentration measurements (not shown here) seem to exhibit a lag. According to  Bittig et al. (2014) , this issue is also related to the time response of oxygen optodes. As far as we know, there is not yet an agreement from the community on how to correct this lag, this is why the data are kept as they are in the present version, though we do not discard the possibility that an improvement in the glider toolbox could address this specific issue.
Quality checks The vessel's velocity is 1 or 2 orders of magnitudes greater than the currents that have to be measured, hence this type of current measurements requires a careful processing in order to get meaningful velocities from the raw signal. The QC procedure for the VM-ADCP is complex as it involves tests on more than 40 technical and geophysical variables (SO-CIB Data Center, 2018). The different tests are based on the technical reports of  Cowley et al. (2009)  and  Bender and Di-Marco (2009) , which are aimed primarily at ADCP mounted on moorings. The procedure can be summarised as follows. 1. Technical variables. Valid ranges are checked for each of these variables: if the measurement is outside the range, the QF is set to 4 (bad data). Examples of technical variables are bottom track depth, sea water noise amplitude, and correlation magnitude.
2. Vessel behaviour. Vessel pitch, roll, and orientation angles are checked and QF are assigned based on specific ranges. In addition, the vessel velocity is checked and anomalously high values are also flagged as bad. 3. Velocities. Valid ranges are provided for the computed current velocities: up to 2 m s -1 , velocities are considered as good; between 2 and 3 m s -1 , probably good, and above 3 m s -1 , bad. The application of all of these tests led to Fig 12, which illustrates the QF during the whole mission. The three main periods during which the ADCP was turned off are shown as grey areas. In addition, no measurements are available in the first metres of the water column, due to the position of the ADCP on the ship, at a depth of approximately 2 m. Overall, the quality of the data tends to deteriorate when the depth increases, as reflected by the bad and missing values.
Description of the database The AlborEx mission generated a large amount of data in a region sparsely sampled in the past. The synergy between low-resolution (CTD, drifters, floats) and high-resolution data (ADCP, gliders) makes this dataset unique for the study of sub-mesoscale processes in the Mediterranean Sea. Moreover its multidisciplinary nature makes it suitable to study the interactions between the physical conditions and the biogeochemical variables.
File naming In order to keep the file names consistent with the original database, it was decided to keep the same file names as those assigned by the SOCIB Data Center. As an example, we deconstruct one file name -dep0007_socib-rv_scb-sbe9002_L1_2014-05 -25 -into its different parts below. dep0007 indicates the number of the deployment, where deployment is the equivalent to the start of a mission or survey with a given platform. The deployment ends when the mission is over or if the platform stops acquiring data. socib-rv is the code for the platform, in this case the SOCIB coastal research vessel. scb-sbe9002 is the instrument identifier, here the CTD SeaBird 9Plus. Note that the instrument is described in the metadata of the netCDF file. L1 is the processing level (see Sect. 2. 4). 2014-05-25 is the deployment date (year-month-day).
Data reading and visualisation The standard format (netCDF) in which the data files are written makes the reading and visualisation straightforward. A variety of software tools such as ncview, ncBrowse, Earth Syst. Sci. Data, 11, 129-145, 2019  www. earth-syst-sci-data. net/11/129/2019/ Table  5. Platform corresponding to the different files. or Panoply are designed to visualise gridded fields. Here the data provided consist of trajectories (surface or 3dimensional), profiles, and trajectory-profiles, which can be easily read using the netCDF library in different languages (Table  6 ). Examples of reading and plotting functions, written in Python, are also provided  (Troupin, 2018). They allow users or readers to get the data from the files and reproduce the same figures as in the paper, constituting a good starting point to carry out further specific analysis.
Code and data availability Following SOCIB general policy, the data are made available as netCDF files through the SOCIB Thematic Real-time Environmental Distributed Data Services (THREDDS) data server, a standard way to distribute metadata and data using a variety of remote data access protocols such as OPeN-DAP, Web Map Service (WMS), or direct HTTP access. In addition, the whole AlborEx dataset has been assigned a digital object identifier (DOI) to make it uniquely citable. The most recent version of the dataset is accessible from http://doi. org/10. 25704/z5y2-qpye (last access: 24 December 2018;  Pascual et al. , 2018) , and the nutrient data, in the process of being included in the catalog, are available at https://doi. org/10. 5281/zenodo. 2384855 (last access: 24 December 2018  ; Troupin, 2018).
Upgrades will be performed periodically with the implementation of fresh or better QCs on sensors such as the ADCP, CTD, or gliders. The new releases will be available using the same Zenodo identifier, but will be assigned a dif- ferent version number, each version having its own DOI. Files not available at the time of the writing will also be appended to the original database. Concerning the improvement of the quality control, it is worth mentioning the new tests that will be implemented in the SOCIB Glider Toolbox  (Troupin et al. , 2016). The checks performed on the ADCP velocities involve a set of parameters that can also be fine-tuned to improve the relevance of the quality flags. Nevertheless, noticeable changes are not expected with respect to the quality flags displayed in Fig. 12.
Finally, the quality of the CTD and the glider profiles can be improved by using the salinity measurements of water samples collected during the mission. This type of correction might not be essential for the study of mesoscale processes but is crucial when one is focused on long-term studies and when a drift can be observed in the salinity measurements. A set of programs in Python to read the files and represent their content, as in the figures presented through the paper, are available at https://github. com/ctroupin/AlborEX-Data (last access: 24 December 2018). The programs are written in the form of documented Jupyter notebooks, a web application that combines code fragments, equations, graphics, and explanatory text (http://jupyter. org/, last access: 14 August 2018), so that they can be run step by step.
Conclusions and perspectives The AlborEx observations acquired in May 2014 constitute a unique observational dataset that captured mesoscale and sub-mesoscale features in a particularly energetic frontal zone in the western Mediterranean Sea. The potential uses of the dataset can be separated into the following different topics. -Hydrodynamic model validation. With their increasing resolution, models are becoming able to properly reproduce small-scale structures, but the correct timing and location of these features remain a challenging topic. -High-resolution remote-sensing data validation. Highquality in situ measurements of the sea surface are essential for the validation of operational products such SST or Ocean Color. -Study of mechanisms. The Mediterranean Sea is often referred to as a laboratory for oceanography and the Alboran Sea in particular is a stage for intense processes of mixing, subduction, and instability.
-Assessment of mechanisms responsible for intense vertical motions. The version of the dataset described in the present paper contains files that have been processed and standardised so that they are directly usable by scientists without having to perform unit or format conversions from the manufacturer's raw data files. Updates will be performed when new files or new versions of the existing files are made available. Author contributions. CT prepared the figures and the first version of the manuscript. AP and SR edited the manuscript. JGF and MAR lead the data management and the creation of a DOI in Datacite. CM implemented the processing of the ADCP data. GN helped with the processing of the drifters and profiling floats. EA and AT processed and provided the biochemical data. IR finalised the QC documentation.
Nature PlaNts B-genome is a target for banana breeding programmes  2. Here, we highlight that the B-genome is of great importance in ethylene biosynthesis and post-harvest banana ripening, which will further our understanding of ripening mechanisms in polyploid fruit. The M. balbisiana genome assembly, along with our previously acquired M. acuminata genome data, may aid in the discovery of cultivar-specific sequences that are related to important cultivarspecific traits, including shelf life, quality and stress tolerance. Thus, these resources can be used to build molecular characterization strategies for various cultivars to assist in rapid quality control and the conservation of biodiversity. The data from this study also pave the way for whole-genome association studies, germplasm improvement and genetic modification of bananas to meet increasing commercial demands.
34 Gb (166× coverage) of high-quality data. For long-read DNA sequencing, 5. 79 million SMRT long reads (58. 99 Gb data, 113× coverage) were sequenced using the PacBio Sequel system with libraries of 20-kb insert size; sub-reads had a mean length of 10. 2 kb and N50 length of 16. 6 kb. One Hi-C library was prepared and sequenced on Illumina NovaSeq 6000 to generate 71. 96 Gb (138× coverage) of high-quality data  63  (Supplementary Table  1 ). Additional details are available in the Supplementary note. Genome assembly. De novo assembly of DH-PKW was performed using wtdbg (v. 1. 2. 8; https://github. com/ruanjue/wtdbg) based on PacBio data (only reads longer than 1 kb were used in assembly). The assembled genome was corrected for two rounds using the 'wtdbg-cns' programme in the wtdbg package.
We then used the algorithm Arrow (https://github. com/PacificBiosciences/GenomicConsensus), which takes into account all of the underlying data and the raw quality values inherent in SMRT sequencing, to polish the assembly again for the final consensus accuracies. The final consensus contigs were scaffolded using the SSPACE-standard programme  64  (v. 3. 0) with meta-pair reads from libraries of insert size 2-20 kb. Based on Hi-C data, 430. 02-Mb scaffolds were anchored to 11 pseudo-molecules using LACHESIS software 13 (Supplementary Fig. 2 ). Chromosomes were numbered according to the linkage group nomenclature adopted for M. acuminata. Additional details regarding genome assembly are provided in the Supplementary note. Evaluation of assembly quality. BUSCO (v. 3) was used to assess assembly completeness  11.