dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary of dissolved organic phosphorus (DOP) observations CoastDOM v1 includes a total of 13534 DOP measurements, collected between 1990 and 2021 (Table  1 ). Overall, DOP concentrations ranged from < 0. 10 to 84. 27 µmol P L - 1 (average (SD): 0. 34 (1. 11) µmol P L -1 ; median: 0. 18 µmol P L -1 ; Table  2 ). The majority (74%) of DOP concentrations were below 0. 30 µmol P L -1 (Fig. 1 ). Analysis of the DOP dataset revealed that 75% of the concentrations were above 0. 11 µmol P L -1 , while 25% were above 0. 30 µmol P L -1 (Table  1 ). The lowest DOP concentrations were measured off the Kimberley Coast in Australia, while the highest concentrations were found in the Vasse-Wonnerup Estuary in the South west region of Australia. Similarly to DOC and DON, most of the DOP measurements have been conducted from the 1990s onwards, with a predominant focus in the Northern Hemisphere, particularly in Europe and the United States (Fig.
Potential use of the dataset The use of the CoastDOM v1 dataset should be accompanied by the citation of this paper and the inclusion of the correct doi-reference. CoastDOM v1 will be available in full open access on the PANGEA homepage after acceptance of the manuscript, where it will be available as a *. csv file. The dataset includes a brief description of the metadata and methods employed, with emphasis on measurement techniques and data units. We chose the terminology most familiar to the ocean science community. It is important to note that all data included in CoastDOM v1, as well as this manuscript, are considered public domain; as such, a subset of this global dataset may also be present in previous data compilations (e. g. ,  (Hansell et al. , 2021). The list of citations and links referenced in CoastDOM v1 also provide users with information as to how these data has been previously used in publications or databases.
In CoastDOM v1, we have compiled available coastal DOM data in a single repository, making it freely available to the research community. This compilation has established a consistent global dataset, serving as a valuable information source to investigate a variety of environmental questions and to explore spatial and temporal trends. We suggest a set of recommendations for the future expansion of this global dataset. Firstly, our analysis highlights a spatial bias, with a concentration of sampling efforts and/or data availability predominantly concentrated in the Northern Hemisphere. The data gap in coastal DOM measurements in the Southern Hemisphere needs to be addressed to provide a more representative global understanding of the role of DOM in coastal water biogeochemistry. Additionally, increased sampling efforts especially in the African and South American continents are warranted due to the vulnerability of many coastal areas to climate change and intensifying human activities, which will undoubtedly impact DOM biogeochemistry.
Further it is also worth noting that there is comparatively few data from coastal waters affected by river discharge into the tropics, e. g. , Amazon, Indian and Indonesian rivers that together dominate freshwater inputs to the coastal ocean. Secondly, there is a need for more comprehensive temporal and spatial datasets to capture the variability of DOM levels in highly dynamic and productive coastal systems. Focused efforts should be made to resolve these temporal and spatial changes. Thirdly, it is also important to collect and report ancillary data, such as temperature, salinity, nutrient measurements, and particulate components, to provide context and better understand the underlying processes driving the observed DOM levels. Lastly, we strongly recommend that the DOM research community conducts regular inter-calibration exercises to establish standardised and interoperable methods, particularly for DON and DOP measurements.
Qi Chen is acknowledged for his very skilful help with Figure  3. Funding During the drafting of the manuscript C. L. received funding from the Independent Research Fund Denmark Grant No. 1127-00033B. The monitoring data obtained from Bermuda received funding from the Bermuda Government Department of Environment and Natural Resources. A subset of the data obtained from UK coastal estuaries received funding from the Natural Environment Research Council (Grant NE/N018087/1). Data retrieved from the Palmer LTER data were collected with support from the Office of Polar Programs, US National Science Foundation. Data obtained from the Great Barrier Reef Marine Monitoring Program for Inshore Water Quality, which is a partnership between the Great Barrier Reef Marine Park Authority, the Australian Institute of Marine Science, James Cook University, and the Cape York Water Partnership.
The contribution by P. K. was supported by DiSeDOM project contract no. UMO-2019/33/B/ST10/01232 funded by the NCN -National Science Centre, Poland. N. W. and A. M. -P. participated in this synthesis effort with funding provided by the U. S. Department of Energy funded COMPASS-FME project; the provided data was collected with funding from the PREMIS Initiative, conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory. The data obtained from the Levantine Sea (Med Sea) received funding from the Scientific and Technological Research Council of Türkiye (TÜBİTAK, 1001 program, Grant 115Y629).
Data availability The dataset is available for the review process at Figshare https://figshare. com/s/512289eb43c4f8e8eaef). The dataset is furthermore submitted to the PANGEA database and is currently waiting to be assigned a Doi number  (Lønborg et al. , 2023). The file will be available as a *. csv merged file and will be available in full open access in the PANGEA database after acceptance of the manuscript.
0 License. -Total dissolved phosphorus (TDP) concentration (µmol L -1 ) -Method used to measure TDP concentration -TDP -QA flag: Quality flag for TDP measurement -Responsible person -Originator institution -Contact of data originator It should be noted that in all entries, at least DOC, DON or DOP should have been measured. In addition, we also included other relevant data, when available, in the CoastDOM v1 dataset: -Depth at the station where the sample was collected (Bottom depth, m). -Total suspended solids (TSS) concentration (mg L -1 ) -Chlorophyll-a (Chl a) concentration (µg L -1 ) -Chl a -QA flag: Quality flag for chlorophyll-a measurement -Sum of nitrate and nitrite (NO3 -+NO2 -) concentration (µmol L -1 ) -NO3 -+ NO2 --QA flag: Quality flag for NO3 -+ NO2 -measurement -Ammonium (NH4 + ) concentration (µmol L -1 ) -NH4 + -QA flag: Quality flag for NH4 + measurement -Soluble reactive phosphorus (HPO4 2-) concentration (µmol L -1 ) -HPO4 2--QA flag: Quality flag for HPO4 2-measurement -Particulate organic carbon (POC) concentration (µmol L -1 ) -Method used to measure POC concentration -POC -QA flag: Quality flag for POC measurement -Particulate nitrogen (PN) concentration (µmol L -1 ) -Method used to measure PN concentration -PN -QA flag: Quality flag for PN measurement -Particulate phosphorus (PP) concentration (µmol L -1 ) https://doi.
Acknowledgements Funding by the  Innovation Pool Project of the Helmholtz Society  and the  Federal Ministry of Education and Research  is gratefully acknowledged. We are thankful to many colleagues from different universities and research centres who have contributed to the design of instruments:  R. Bewley ,  J. Fenske ,  M. Feygenson ,  C. Franz ,  H. Frielinghaus ,  M. Ganeva ,  A. Glavic ,  S. Jaksch ,  N. Kardjilov ,  G. Kemmerling ,  H. Kleines ,  Z. Ma ,  S. Mattauch ,  S. Pasini ,  K. Schmalzl ,  T. E. Schrader ,  W. Schweika ,  M. Strobl ,  E. Vezhlev ,  N. Violini ,  R. Zorn.
PLOS PATHOGENS C. difficile requires the S-layer in vivo seq and whole genomic sequencing data have been deposited within Gene Expression Omnibus (GEO) repository and can be accessed using the reference ID GSE205747. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE205747. All other relevant data are available within the manuscript and its Supporting information files. to date  [20, 25]. Strikingly, the lattice is very compact compared to other studied S-layers, which have pores of between 30-100 Å compared with only ~10 Å in diameter in C. difficile  [24]. This tight packing correlates well with the hypothesis that S-layer acts as a molecular sieve  [18] , as deletion of the most exposed regions of SLP L results in a strain with increased sensitive to lysozyme, in comparison to the parent strain, R20291  [24].
Sequence modifications can affect SlpA structure and assembly To understand the effects of the detected variations in amino acid sequence on SlpA structure and S-layer assembly, crystallisation of SlpA R20291 , SlpA varA and SlpA varB was carried out. Although crystals were obtained for all strains, only SlpA varB crystals were of sufficient quality for X-ray diffraction data collection and structural determination by molecular replacement, using previous SlpA structures as models. As we have so far been unable to obtain an experimental structure of the complete SlpA R20291 , our previously described variant-SlpA RΔD2 -(PDB ID: 7ACZ), which lacks the most exposed region of SLP L  [24] , was used in our comparisons as a model of the S-layer protein in R20291. SLP H and the interacting domains were easily traceable in the electron density but D1 was only partially built, whilst density for domain D2 was very poor and this region could not be traced in the final SlpA varB model (Fig 3a, PDB ID: 8BBY and S1 Table ).
These insertions could not be traced in our previous SlpA RΔD2 model  [24]  but were traceable in SlpA varB and result in several loops protruding above the SLP H plane, towards the environment, partially occluding the CWB2 motifs (Fig 3a  , right ). Together with the movement of the interacting domains and D1 towards the SLP H tiles, this creates a more compressed arrangement (~66 Å thickness compared to ~76 Å in SlpA CD630 , PDB ID: 7ACY, Fig  3b, bottom ). In the previously described crystallographic models of SlpA CD630 (PDB ID: 7ACY), SlpA R7404 (PDB ID: 7ACX) and SlpA RΔD2 (PDB ID: 7ACZ), α2 L was responsible for closing a (shown in light blue) in SlpA varA , (dark blue); a five-nucleotide insertion (light crimson) in SlpA FM2. 5 sequence results in modification of 13 amino acids (shown in light crimson) in SlpA varB (crimson).
As no crystal data was obtainable, we initially used AlphaFold 2  [36]  to predict models for SlpA R2021 and SlpA varA. However, in the absence of a specific template, the predicted models show considerable variations of the relative orientation of SLP L and SLP H or some of the proteins sub-domains. The observed orientations are not compatible with our knowledge that SLP H faces the cell wall and SLP L the environment  [24, 37]. This misorientation was also observed in AlphaFold 2 predictions of our experimental structures of SlpA CD630 and the SlpA-varB and therefore we did not proceed with AlphaFold 2 predicted models. Instead, we used the SWISS-MODEL server, either without a template input, or using SlpA RΔD2  [24]  or the SlpA varB structure determined here. Depending on which template was used (automatically selected SlpA R7404 , PDB ID: 7ACX; SlpA RΔD2 or SlpA varB ), different predicted structures of SlpA varA were obtained, varying mostly in the orientation of D1 and interacting domains relative to SLP H (S4b Fig).
Histology and immunohistochemistry Tissue samples were harvested from the caecum and colon of antibiotically susceptible animals infected with either R20291 or FM2. 5 at post-mortem, 48 hpi. These tissues were gently washed in sterile PBS and immediately fixed in 10% formalin. Embedded tissue sections were cut and stained with Hematoxylin and Eosin  [35]. Blind histological scoring of tissue was performed on three independent sections of caecal and colonic tissue from each mouse. Each section was scored out of a total of 15, with a score of 0 indicating no change, 1 slight change, 2 moderate change and 3 significant change, for the following categories: epithelial damage, neutrophil migration, haemorrhagic congestion, tissue oedema and crypt hyperplasia. Data presented represents the mean scores calculated for four mice for caecal, and three mice for colon assessment for each treatment.
Whole genome sequence analysis Genomic DNA recovered from R20291, FM2. 5, FM2. 5 varA , FM2. 5 varB were sequenced by MicrobesNG (Birmingham UK) using Illumina based technologies. The raw data was then aligned to the reference genome (NC_013316. 1) using Bowtie2. Polymorphisms were called using FreeBayes under default settings and VCF file was filtered to include only mutations identified with a read depth of > 50 and in at least 50% of reads. To build the tree (S2 Fig) , the filtered mutations across all strains were merged and converted into a bed file. Regions were expanded by 100 bp on each side and these were considered the mutable regions. The phylogenetic tree was constructed using PhyloTree (https://github. com/samandmac/PhyloTree), which uses BLAST and PHYML to generate a tree in Newick format, and subsequently an R script which allows visualisation of the tree using the ggtree package.
Crystals were obtained in 0. 03 M magnesium chloride hexahydrate; 0. 03 M calcium chloride dihydrate, 0. 12 M ethyleneglycol, 0. 05 M Tris (base); 0. 05 M bicine pH 8. 5, 20% v/v glycerol; 10% w/v PEG 4,000. Data were collected on the I24 (λ = 0. 71 Å) beamline at the Diamond Light Source Synchrotron (Didcot, UK; mx24948-136) at 100 K. Two datasets were collected from one crystal and initially processed by the automatic multi-crystal data-analysis software pipeline xia2. multiplex  [51]  within the Information System for Protein Crystallography Beamline (ISPyB), re-processed using Automatic Image Processing with Xia-2 (DIALS  [52]  and Aimless 3d  [53] ) and scaled with Aimless within ccp4. cloud of CCP4  [54]  software suit. The initial model of the core SLP H was obtained by molecular replacement in Phaser  [55] , using an SlpA varB model of CWB2 domains, derived from the SlpA RΔD2 model (PDB ID: 7ACZ) and calculated using SWISS-MODEL  [56].
Protein structure prediction Homology models for SlpA R20291 and SlpA varA were generated by providing SWISS-MODEL webserver with the SlpA varB (PDB ID: 8BBY) or SlpA RΔD2 (PDB ID: 7ACZ) as user templates, as well as without a template. Structural alignments between predicted models and templates were performed using COOT  [58]. As SlpA RΔD2 lacks the D2 domain, predicted models based on this experimental model have a disordered D2 domain. Therefore, overall comparison of the three predicted models was based on models calculated in the default mode, which uses SlpA R7404 (PDB ID: 7ACX) as a template, while analysis of the modification-containing region in the D1 domain was done using SlpA RΔD2 or SlpA varB derived models.
Total RNA was then extracted using the standard PureLink RNA mini kit protocol, according to the manufacturer's specifications. Genomic DNA was removed using a TURBO DNase kit (Ambion) and samples were tested for efficient removal of DNA by conventional PCR. Samples for RNA-Seq were prepared in triplicate on two separate occasions (6 samples for each) for all four bacterial strains. Illumina library preparation of mRNA samples for RNA-Seq was prepared using a TruSeq Stranded mRNA library prep kit (Illumina) according to the manufacturer's instructions. Sequencing was performed on the Illumina NextSeq 500 platform (75 bp length; single-end). Library generation, optimisation of amplification and sequencing were performed at the University of Glasgow Polyomics facility. Quality control of sequencing data was performed using FastQC (Babraham Bioinformatics) to assess the minimum Phred threshold of 20 and potential data contamination.
RNA analysis and identification of differentially expressed genes Raw RNA-Seq datasets were subject to the following pipeline. Firstly, fastQ files were assessed using FastP  [62]  and then were aligned to the C. difficile R20291 (accession number NC_013316) reference genome using STAR  [63]  (v2. 6) with-quantMode GeneCounts-outFil-terMultimapNmax 1 and-outFilterMatchNmin 35. We used a Star index with a-sjdbOverhang of the maximum read length -1. Next, read count files were merged and genes with mean of < 1 read per sample were excluded. Finally, the expression and differential expression values were generated using DESeq2  [64]  (v1. 24). For differential comparisons, we used an A versus B model with no additional covariates. All other parameters were left to default. The processed data was then visualised using Searchlight  [65] , specifying one differential expression workflow for each comparison, an absolute log 2 -fold cut-off of 1 and adjusted p of 0.
Acknowledgments The authors would like to thank  Dr Arnaud Basle , facility manager of the  Newcastle Structural Biology Lab , and beamline staff at  I24, Diamond Light Source  (BAG mx24948) for support on structural data collection and  Dr Sam McAllister  for help with phylogenetic tree analysis. The contents of this work are solely the responsibilities of the authors and do not reflect the official views of any of the funders, who had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.
(f) Histopathological sections representing caecal (i, ii and iii) sections following challenge with PBS (i); R20291 (ii); or FM2. 5 (iii). Scale bars represent 100 μm. Statistical tests were conducted using GraphPad Prism software v. 12. Statistical significance is indicated: ns-not significant; *p < 0. 05; **p < 0. 01; and ***p < 0. 001.
(f) Histopathological sections representing caecal (i, ii and iii) sections following challenge with PBS (i); R20291 (ii); or FM2. 5 (iii). Scale bars represent 100 μm. Statistical tests were conducted using GraphPad Prism software v. 12. Statistical significance is indicated: ns-not significant; *p < 0. 05; **p < 0. 01; and ***p < 0. 001. https://doi. org/10. 1371/journal. ppat. 1011015. g001.
Kim et al. , 2020; M. Kim et al. , 2018; Kwon et al. , 2019; Park et al. , 2021). The Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) instruments onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) satellites were launched in October 2011 and November 2017. Li et al. (2015)  and González  Abad et al. (2016)  retrieved HCHO total columns from OMPS-NM observations onboard Suomi NPP with principal component analysis and hyperspectral direct fitting methods, respectively. Recently, we have developed updated, multiyear, and publicly availably OMPS-NM HCHO products using hyperspectral direct fitting  (González Abad, 2022a , 2022b; Nowlan et al. , 2022). These OMPS-NM HCHO products can play an important role in augmenting and extending long-term HCHO data sets from OMI, since all three products share algorithms.
Unfortunately, OMI has been affected by the row anomaly since 2008, reducing the frequency at which it achieves total coverage of the globe  (Torres et al. , 2018). OMPS-NM products can make up for a loss of pixels in OMI, allowing us to construct afternoon HCHO climatological data sets from 2004 until now with daily global coverage. Furthermore, NOAA plans to launch OMPS-NM instruments in successive operational satellites, which can provide a consistent set of HCHO observations starting in 2012 and extending to the 2030s. It is also worth considering that when TROPOMI is decommissioned NOAA OMPS-NM instruments will be the main source of HCHO observations in the afternoon overpass. concentrations, negative biases of -15% ± 6% appear for OMPS-NPP, but OMPS-N20 products have a smaller bias of 0.
FTIR HCHO Observations We use harmonized NDACC FTIR HCHO observations. To minimize possible biases between the various sites, NDACC FTIR enforces the harmonized retrieval settings including a consistent spectroscopic database and   (Vigouroux et al. , 2018). NDACC FTIR groups use two retrieval codes: SFIT4 and PROFFIT, and the two codes produce very consistent retrieval products according to a previous comparison study by  Hase et al. (2004). The details of the harmonized retrievals for FTIR are given in  Vigouroux et al. (2018). Table  2  summarizes NDACC FTIR locations, retrieval codes, institutes of the principal investigators, observation periods, and geographical region. Three Arctic sites (Eureka, Ny-Ålesund, and Thule) are excluded in our analysis, as compared to  Vigouroux et al. (2020).
Construction of Comparable Data Sets To compare satellite and ground-based observations, we need to collect high-quality data and then build comparable pairs, which are spatially and temporally collocated. First, we collect OMPS-NPP/N20 pixels with the quality control conditions mentioned in Section 2. 1. Then, satellite and ground-based observations need to be spatially and temporally collocated. For OMPS-NPP, the median of the fitting uncertainties is ∼3. 5 × 10 15 molecules cm -2 over the reference sector in June 2018, and several pixels need to be averaged to reduce noise and match the precision of the ground-based observations. Vigouroux et al. (2020)  showed that the median uncertainty related to random components for FTIR observations was 2. 3 × 10 14 molecules cm -2 , which is ∼10 times lower than that of OMPS-NPP.
Acknowledgments This study was supported by  NASA 's  Making Earth System Data Records for Use in Research Environments  ( 80NSSC18M0091 ),  NASA  The  Science of Terra ,  Aqua and Suomi NPP  ( 80NSSC18K0691 ), and  NOAA's Climate Program Office' s Atmospheric Chemistry, Carbon Cycle, and Climate Program  ( NA18OAR4310108 ). Computations in this paper were conducted at the  Smithsonian High Performance Cluster (SI/HPC) ,  Smithsonian Institution  (https://doi. org/10. 25572/SIHPC). This study was supported by the research grant of the  University of Suwon , 2022. Research at the  University of Bremen  has been supported by the  BMBF (German Ministry of Research and Education)  in ( 01LG1904A ). We thank the  AWI Potsdam  and the  Impres GmbH, Bremen , for logistical and technical support in Palau.
We thank the  Coral Research foundation (CRRF)  and the  PCC Palau  for support in conducting the measurements. We also thank the  Senate of Bremen  for partial support. We are grateful to  T. Blumenstock ,  F. Hase , and  M. Schneider  as the PI of  Kiruna, Karlsruhe , and Izaña stations, respectively. The NDACC stations at Bremen,  Izaña, Karlsruhe, Kiruna, Paramaribo , and Zugspitze have been supported by the  German Bundesministerium für Wirtschaft und Energie (BMWi)  via  DLR  under Grants  50EE1711A , B, and D. The FTIR monitoring program at Jungfraujoch was primarily supported by the  F. R. S. -FNRS (Brussels, Belgium)  and the  GAW-CH program  of  MeteoSwiss (Zürich , Switzerland). FTIR operations of Rikubetsu and Tsukuba sites are supported in part by the  GOSAT  series project. The NDACC site at as parts of the joint research program of the  Institute for Space-Earth Environmental Research (ISEE), Nagoya University.
Data Availability Statement The OMPS HCHO products from Suomi NPP  (González Abad, 2022b)  and NOAA-20 (González Abad, 2022a) are available at the NASA GES DISC. NDACC FTIR raw data are available at the website (NDACC, 2023) for most sites. Please see the NDACC data use agreement before the data are used (https://ndacc. larc. nasa. gov/data/ use-agreement). Comparable pairs between OMPS and FTIR observations used in this study are available at the Harvard Dataverse (Kwon, 2022).