dataesr/scientific-paragraphs-categorization huggingface dataset:
While FluxEngine is able to calculate sea-to-air fluxes of both N 2 O and CH 4 , we confined our analysis to N 2 O because of the sparsity of CH 4 data. In situ and 1 • × 1 • gridded monthly mean atmospheric and ocean partial pressure of N 2 O, sea surface temperature, and salinity were obtained from the MEMENTO database for the Atlantic Meridional Transect (AMT) cruise (AMT-24, JR303), which took place between September and November 2014  (Brown and Rees, 2018). These data were supplemented with Earth observation wind speed, U 10 , from the CCMP dataset and modelled air pressure from the European Centre for Medium-Range Weather Forecasts (ECMWF). All input data were gridded to monthly means with a 1 • × 1 • resolution. A custom gas transfer velocity parameterisation was implemented following the template provided in the toolbox to calculate the gas transfer suppression due to biological surfactants in surface waters.
The overall mean (and standard deviation) flux was 5. 7 × 10 -2 (±5. 7 × 10 -2 ) g N 2 O m -2 d -1 (no suppression) and 5. 0 × 10 -2 (±4. 8 × 10 -2 ) g N 2 O m -2 d -1 (suppression), indicating in both cases a small net flux into the ocean. There was a mean flux suppression due to surfactants of 13 % for the entire dataset. 3. 4 Case study 4: gas transfer velocity parameterisation using turbulent kinetic energy dissipation rate The gas transfer velocity, k in Eqs. (  1 ) and (  2 ), is determined by the turbulent mixing near the ocean surface  (Jähne et al. , 1987). While it is common to estimate gas transfer using a polynomial relationship with wind speed, turbulence in the upper ocean is influenced by additional physical processes that are independent, or not solely dependent, on the wind.
FluxEngine was then used to calculate monthly sea-toair CO 2 fluxes, globally, for 2010. All inputs to FluxEngine were provided as monthly averages with a 1 • × 1 • resolution. The additional input data were wind speed data from WAVEWATCH III reanalysis forcing field (WAVE-WATCH III development group, 2016), sea surface temperature from Reynolds Optimum Interpolation Sea Surface Temperature dataset (OISST;  Reynolds et al. , 2007) , salinity data from the NOAA World Ocean Atlas  (Zweng et al. , 2018) , xCO 2 from the GLOBALVIEW-CO2 dataset (GLOBALVIEW-CO2, 2013), and f CO 2 data from the SO-CAT derived sea-to-air CO 2 flux reference dataset for 2010  (Woolf et al. , 2019). Since the  Zappa et al. (2007)  relationship was parameterised in low to moderate wind speeds and in shallow marine environments, a mask was set in the configuration file to constrain the calculation to grid cells with wind speeds less than 10 m s -1 and shelf sea water depths between 20 and 200 m; then the analysis repeated with depths between 20 and 500 m.
Future developments The FluxEngine toolbox will continue to be developed in response to new advances in research and further user uptake will be encouraged through the provision of iPython Jupyter Notebook tutorials. There are currently four interactive Jupyter Notebook tutorials available within the Flux-Engine v3. 0 download and these correspond to the first three case studies presented in this paper as well as an additional introductory tutorial. These interactive notebooks allow users to investigate the toolbox without the need to install any additional software. Users are able to modify and rerun the notebook and immediately see the impact of any changes. This approach has been previously used by the authors for supporting collaborative research and summer school teaching. Additional Jupyter Notebook tutorials could be written to provide worked examples of (i) driving FluxEngine with a custom Python script to perform a sensitivity or ensemble analysis, (ii) using the FluxEngine to study a freshwater environment, or (iii) using the verification tools module to verify custom changes and extensions to the toolbox.
Conclusions FluxEngine is an open-source and freely available software toolbox that provides standardised and verified calculations of gas exchange and net integrated fluxes between the ocean and atmosphere, and the toolbox is now being used by in situ, Earth observation, and modelling scientific communities. The development of the toolbox was driven by the desire to reduce duplication of effort, to facilitate collaboration between different research communities, and to accelerate advancements in air-sea gas flux research and monitoring. Building on  Shutler et al. (2016) , who demonstrated the toolbox and verified the accuracy of the calculations, this paper demonstrates new capabilities that considerably broadens the scope of research questions that can be addressed using FluxEngine. Version 3.
0 can now be easily installed and executed on a desktop or laptop computer and does not require specialist hardware or software libraries. It can be used as a Python library or as a set of stand-alone command-line utilities. The toolbox now includes an extensive suite of tools for calculating gas fluxes directly from in situ data. Collectively, these improvements have streamlined the process for extending the toolbox and will allow users to easily take advantage of newly developed gas transfer velocity parameterisations and/or new sources of input data. These new tools and the toolbox are fully compatible with the internationally established data structures being used by the SOCAT and the MEMENTO communities. The inclusion of the handling of CH 4 and N 2 O sea-air gas fluxes is intended to directly support those communities studying these gases.
Significant international research focus and effort is now being directed to collating data on these gases towards monitoring and understanding their spatial distribution and variability. The FluxEngine toolbox will continue to be updated as new approaches become available. Further development will be guided by the needs of the international research and monitoring communities, so we welcome feedback from users on all aspects of the toolbox. User-selectable gas transfer velocity parameterisations The toolbox includes 14 published wind speed parameterisations and one mean square slope parameterisation, along with a generic userconfigurable wind speed parameterisation (see Sect. 7 of the FluxEngine manual). Custom user parameterisations can be added using a Python template (Sect. 9. 2 of the FluxEngine manual).
Wrobel and Piskozub (2016)  showed that the (model) uncertainty due to the choice of quadratic gas transfer parameterisation led to a 9 % difference in the gas transfer velocity for the North Atlantic and subpolar waters. This uncertainty increased to 65 % when all parameterisations were considered. Reanalyse f CO 2 data to a reference temperature and depth The user can reanalyse paired in situ f CO 2 and SST measurements to a consistent depth using a reference temperature dataset as described by  Goddijn-Murphy et al. (2015). This removes unknown biases due to paired data originating from multiple depths. The depth of in situ data can vary during a single research cruise (e. g. due to sea state or ballasting) and measurements from varying depths become a more significant problem when using large collated datasets collected using different measurement systems.
Calculate integrated net gas fluxes A tool for calculating global or regional integrated net gas fluxes (e. g. CO 2 sink) from netCDF air-sea gas flux data is provided. This enables user-defined land, ice, and region of interest masks to be used and there are multiple options for handling the impact of sea ice. Shutler et al. (2016)  showed that the calculated net CO 2 fluxes can vary by 14 % for the European shelf sea simply due to differing shelf sea masks, highlighting the need for traceable methods and masks for net flux calculations. Input data uncertainty analysis Users can add random noise (with user-defined variance and bias) to any input dataset. This enables the users to investigate the impact of input data uncertainties on their air-sea gas flux calculation (e. g. through using an ensemble approach).
(2019)  Surface Ocean CO 2 ATlas (SOCAT;  Bakker et al. , 2016)  sea-to-air CO 2 flux reference dataset for the year 2010. Both of these datasets are contained within  Holding et al. (2018)  and the verification process compares the output from a test run of the toolbox with the reference data published in  Holding et al. (2018). The installation is deemed successful if all results are identical to the reference dataset within a precision of five decimal places. An additional utility (run_full_verification. py) enables the user to perform a more detailed verification of different user-defined options that exploit the  Woolf et al. (2019)  reference dataset. This executes a suite of 12 different configurations and scenarios, the justification for which is described within  Woolf et al. (2019).
Owing to the large volume of data required to execute and verify all of these scenarios, the verification data are not packaged with the standard FluxEngine download but are all freely available and contained within  Holding et al. (2018). To provide an indication of the execution time, a benchmarking analysis was performed using an Intel Core i5 2. 7 GHz laptop processor with 8GB RAM running MacOS El Capitan. The automatic installation took ∼ 3 min to complete and the basic verification script using the  Woolf et al. (2019)  reference dataset (involving a global 1 year analysis of the gas fluxes for 2010, monthly temporal resolution, and 1 • × 1 • spatial resolution) took approximately 6 min to complete. As the flux calculation is sequential for each grid cell, the execution time scales approximately linearly with the number of grid points and number of time steps.
Hence, doubling the temporal resolution will approximately double the execution time, whilst doubling the resolution of both spatial dimensions will lead to a factor of 4 increase in execution time. The Surface Ocean CO 2 ATlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean-Lower Atmosphere Study (SOLAS), and the Integrated Marine Biosphere Research (IMBeR) programme, to deliver a uniformly quality-controlled surface ocean CO 2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. CCMP Version-2. 0 vector wind analyses are produced by Remote Sensing Systems (http://www. remss. com, last access: 6 December 2019).
Code availability. FluxEngine is an open-source software and is available with a creative commons license via https://github. com/ oceanflux-ghg/FluxEngine (last access: 6 December 2019, GitHub, 2019a). FluxEngine is in constant development and historic versions are available via GitHub. To access the specific version used to conduct the case studies in Sect. 3, please access the v3. 0 release via https://github. com/oceanflux-ghg/FluxEngine/releases/tag/v3. 0 (last access: 6 December 2019, GitHub, 2019b).
Financial support. support This research has been supported by the European Space Agency (grant nos. 4000104762/11/I-AM, 4000112091/14/I-LG, 4000124734/18/NL/CT/gp, and 4000124521/18/NL/CT/gp), the Natural Environment Research Council (grant no. NE/K002473/1), the Horizon 2020 Frame-work Programme (RINGO (grant no. 730944)), and the Seventh Framework Programme (INTEGRAL (grant no. 282887)).
Scale bar 2 μm. c Quantification of time-lapse experiments. The total number of attacker cells and total number of target cells in at least ten microcolonies per experiment was counted at t = 0 h and 3 h and used to calculate fold increase in attacker and target cell numbers during the co-culture. Bars show mean +/-SEM, with individual data points superimposed (n = 3 independent experiments). Source data are provided as a Source Data file. voltage-clamp conditions in non-symmetrical conditions (210 mM KCl in the trans chamber and 510 mM KCl in the cis chamber). Incorporation of MBP-Ssp6 generated a current, thus revealing that Ssp6 could indeed form ion-conducting channels. We observed that the Ssp6-mediated pore could exist in different open states and that multiple channels gated in a coordinated manner, (Supplementary Fig.
In order to determine how widely Ssp6-like effectors occur in other organisms, we used HMMER homology searching to interrogate a database of complete, published bacterial genome sequences. Homologues of Ssp6 were found to be widespread across the family Enterobacteriaceae. We identified 95 homologues in 38 different species, with up to three Ssp6-like proteins encoded within the genomes of individual strains. For selected examples, the phylogenetic relationship of the identified Ssp6-like proteins and the genetic context of their encoding genes is depicted in Fig. 6 , with the corresponding amino acid sequence alignment in Supplementary Fig. 8a. The full set of identified homologues is shown in Supplementary Figs. 8b  and 9 , and detailed in Supplementary Data 1. In each case, a small open reading frame can be identified immediately downstream of the ssp6-like gene which is predicted to encode the corresponding immunity protein (Fig.
For detection of Ssp6-HA, cellular samples were prepared by resuspending cells recovered from 200 μL of culture in 100 μL of 2× SDS sample buffer, whilst secreted protein samples were prepared by precipitation from 15 mL of culture supernatant using 1:1 chloroform:methanol, washing in methanol and resuspension in a final volume of 40 μL of 2× SDS sample buffer  52. Ssp6-HA was detected using anti-HA primary antibody (MRC PPU Reagents, University of Dundee; 1:6000) and HRP-conjugated anti-mouse secondary (Bio-Rad, #170-6516; 1:10,000). Uncropped blot images can be found in Supplementary Fig. 11  and the Source Data file. Co-culture assays for T6SS-mediated anti-bacterial activity. Using our standard co-culture assay  52, 54  , cell suspensions of attacker and target strains of S. marcescens Db10 were normalised to OD 600 0.
Independent fields of view were located and their XYZ positions were stored in order to capture images of the same coordinates every 30 min for 3 h. Images were manually corrected for drift and, where necessary, adjusted for any loss of fluorescence during the timecourse. Post-acquisition, images were deconvolved using softWoRx and stored and processed using OMERO software (http://openmicroscopy. org)  57. Quantification of attacker and target cells was done using OMERO. mtools. All attacker strains carried cytoplasmic mCherry while target strains carried cytoplasmic GFP (Supplementary Table  1 ). For time-lapse experiments, microcolonies were chosen for analysis on the basis that they contained attacker and target cells which were in direct contact to allow T6SS-mediated attacks. The number of attacker and target cells in the microcolonies were counted at time point 0 h and 3 h.
05 Software (John Dempster, University of Strathclyde, Glasgow, UK). Current fluctuations were measured over ≤30 s and recordings were then filtered using a low pass digital filter at 800 Hz (-3 dB) implemented in WinEDR 3. 05. For experiments in which a nonsymmetrical KCl gradient was used, the KCl solution in the cis chamber contained 510 mM KCl, 10 mM HEPES pH 7. 2, while the KCl solution in the trans chamber contained 210 mM KCl, 10 mM HEPES pH 7. 2. For bi-ionic relative permeability studies, the KCl solution contained 210 mM KCl, 10 mM HEPES pH 7. 2; the NaCl solution contained 210 mM NaCl, 10 mM HEPES pH 7. 2; and the CaCl 2 solution contained 210 mM CaCl 2 , 10 mM HEPES pH 7. 2. Noise analysis was performed by subdividing current recordings into segments in time, with each segment containing N samples.
For each holding potential, current fluctuations were measured over 20-30 s. For each segment, the mean current was plotted against time and computed using the following formula: I mean ¼ N ΣI ðiÞ i ¼ 1 N Where I(i) is the amplitude of the ith current and N is the number of the samples in the analysed segment. The mean data obtained from multiple replicates were subsequently plotted as a function of voltage. Predicted reversal potentials were calculated using the Nernst equation. The relative permeability ratio when comparing relative permeability of monovalent cations was calculated using the Goldman-Hodgkin-Katz equation  59, 60  : Px=Py ¼ Y þ ½ = X þ ½ Áexp ÀE rev F=RT ð Þ Where R is the ideal gas constant (8. 314 J mol -1 ), T is the temperature expressed in kelvin, F is the Faraday constant (9.
6485 × 10 4 C mol -1 ) and E rev is the reversal potential, which was taken to be the holding potential at which transmembrane current fluctuations were at a minimum. The relative monovalent to divalent cations permeability ratio was calculated using the Fatt-Ginsborg equation  61  Px 2þ =Py þ ¼ Y þ ½ =4 X 2þ Â Ã Á exp E rev F=RT ð ÞÁexp E rev F=RT þ 1 ð Þ Junction potentials were calculated using Clampex software version 10. 2 (Molecular Devices) and subtracted from the reversal potential obtained for each experiment. Identification of Ssp6-like effector family. Genome sequences were downloaded from the RefSeq database  62  (ftp://ftp. ncbi. nlm. nih. gov/genomes/ASSEMBLY_RE-PORTS/assembly_summary_refseq. txt, as of 4th Feb 2019). Genome sequences that were designated "Complete Genomes" and had previously been published were selected for analysis.
Genomes were annotated using prokka (v1. 13. 3)  63  and CDS protein sequences extracted using a custom script (gff_to_faa. py). Genomes were selected for further analysis based on possession of both at least one gene encoding an Ssp6-like protein, and at least one T6SS-encoding gene cluster. To achieve these criteria, two steps were performed. First, hmmsearch from the HMMER suite (v 3. 1b2)  64  was used to identify genomes containing genes encoding Ssp6-like proteins, based on a small, manually-curated alignment of nonredundant Ssp6 homologues which had originally been identified using BLASTp; the cutoff value was a HMMER bit score of 20 over the overall sequence/profile comparison. Second, T6SS-encoding gene clusters were identified as a locus containing at least 9 T6SS component genes (components identified using HMMER, cutoff value as above) in a contiguous set with no more than 8 unrelated genes between each known T6SS gene, performed using a custom script (hamburger.
py). HMM models were taken from PFAM, or created from protein sequences stored in the Secret6 database (using hmmbuild) for accessory and core components of the T6SS, respectively. All models and the alignments they are based on (if applicable) are stored in https://github. com/djw533/ssp6-paper/tree/master/models. All custom scripts can be found at https://github. com/djw533/ssp6-paper/tree/master/ scripts. Ssp6-encoding loci of ~20 K nucleotides (10 Kb upstream and downstream of the Ssp6 HMMER hit) were extracted using the script hamburger. py from genome sequences that satisfied the requirements stated above. Extracted loci were then subsequently analysed for possession of T6SS related genes using hamburger. py a second time, as above. Ssp6-like protein sequences were aligned using MUSCLE (v3.
8. 31)  65  and trees drawn using IQTREE (v1. 6. 5)  66 with 1000 ultrafast bootstraps. Trees were visualised using the R package ggtree (v1. 15. 6)  67  , and associated genomic context depicted using ggplot2 (v3. 1. 1)  68 and gggenes (v0. 3. 2). An R script (https://github. com/djw533/ssp6-paper/blob/master/scripts/ Plotting_ssp6_figures. R) was used to plot figures from the above results (stored at https://github. com/djw533/ssp6-paper/tree/master/results). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by the  Wellcome Trust  ( 104556/Z/14/Z ,  Senior Fellowship in Basic Biomedical Science  to S. J. C. ;  097818/Z/11/B  and  109118/Z/15/Z , PhD studentships to  University of Dundee ), the  MRC  ( MR/K000111X/1 ,  New Investigator Research Grant  to S. J. C. ) and the  Royal Society of Edinburgh (Biomedical Personal Research Fellowship  to S. J. P. ). We thank  Roland Freudl  for the gift of anti-OmpA antibody;  Adam Ostrowski  for construction of strains  AO07  and  AO08 ;  Gal Horesh ,  Amy Dorward  and  Gavin Robertson  for expert assistance; the  Flow Cytometry and Cell Sorting Facility at the University of Dundee ; and the  Dundee Imaging Facility  (supported by  Wellcome Trust  [ 097945/B/11/Z ] and  MRC  [ MR/K015869/1 ]) awards).
Comparison of PXR and PPARα-Dependent Transcriptome This prompted us to investigate the intersection between PXR and PPARα activation to test the hypothesis that PXR activation influenced PPARα activity. We took advantage of our previously published microarray dataset  [14] , in which C57Bl6/J male mice carrying an hepatocyte-specific deletion of Pparα (Pparα hep-/-) were fasted for 24 h to induce PPARα activity and compared to their wild-type littermates (Pparα hep+/+ ). We have indeed previously shown that, during fasting, PPARα senses increased levels of free fatty acids released from adipocytes, and in response, controls the expression of hundreds of genes involved in fatty acid uptake, transport, and catabolism in hepatocytes  [14, 15]. Figure  3a  indeed illustrates that a large number of the fasting-induced hepatic genes are PPARα sensitive, with 538 genes significantly up-regulated in a PPARα-dependent manner.
Gene Expression Total RNA was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Gene expression profiles were obtained at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France) using Sureprint G3 Mouse GE v2 microarrays (8 × 60 K; design 074,809; Agilent Technologies, Santa Clara, CA, USA) following the manufacturer's instructions Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [41]  and are accessible through GEO Series accession numbers GSE123804. For real-time quantitative polymerase chain reaction (qPCR), 2 µg RNA samples were reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Table  S3  presents the SYBR Green assay primers. Amplifications were performed using an ABI Prism 7300 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA).
Statistical Analysis Microarray data were processed using R (http://www. r-project. org, accessed at 22 September 2017) and Bioconductor packages (www. bioconductor. org, accessed at 22 September 2017, v 3. 0). Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath), and normalized using CrossNorm method  [43]. Normalized data were first analyzed using Matlab (v2014. 8). The principal component analysis was performed using an in-house function. The linear model was fitted using the limma lmFit function  [44]. Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using the Benjamini-Hochberg procedure for false discovery rate (FDR). Probes with FDR ≤ 0.
Acknowledgments: We thank all members of the  EZOP  staff for their careful help with this project. We thank the staff from the  Genotoul: Anexplo ,  Get-TriX , and  Metatoul-Lipidomic  facilities. Pxr -/-mice are a generous gift from  Pr Steven Kliewer  ( University of Texas Southwestern Medical School, Dallas, TX, USA ). Colony founders were kindly provided by  Pr Urs A Meyer (Biozentrum, University of Basel, Basel, Switzerland ).