dataesr/scientific-paragraphs-categorization huggingface dataset:
2% having CS hits. 16S sequences from query dataset 1, a study of land use change across the United Kingdom  (Malik et al. , 2018) ,  also sequenced with the same 341f/806r primer set, had the highest percentage of hits against the CS representative sequences (67. 26%). Wider assessment of our own unpublished datasets using the exact same methodologies yield percentages of hits of 62 and 56% for soils from United Kingdom calcareous grasslands and tropical rainforests, respectively. A separate survey of Welsh soils  (George et al. , 2019)  was also queried against the CS database (query dataset 2, Table  1 ), which used the commonly used Earth Microbiome primer set exclusively targeting the V4 region (as opposed to V3 and V4 targeted region used for the CS dataset). This dataset had a percentage of hits of 58.
49% providing evidence that datasets amplified with other primer sets can be matched to the CS database with only marginal loss of coverage. We next wanted to explore possible reasons for obtaining less than 100% coverage from query soil datasets, given the good coverage of the CS reference sequence database evident from the rarefaction curve (Figure  1 ). We predicted this discrepancy was caused by rare OTUs being unique to specific studies and tested this by classifying OTU's from query dataset 1 (Table  1 ) into 1,000 discrete abundance based quantiles (1 being the most abundant quantile and 1,000 being the least). Plotting the proportion of query OTUs which matched to the CS database by query OTU abundance class, confirmed that less abundant query OTUs had less matches to the CS database (Figure  2 ).
Modeling OTU Responses to Soil pH Since the majority of the 39,952 reference OTUs obtained across all CS samples likely derive from rare taxa with intrinsically little value for predictive modeling (low within-sample abundance, and occurrence across samples), we opted to only model taxa-pH relationships for those taxa which occurred in at least 30 samples within the CS dataset. These taxa were selected from a cleaned dataset of 1,006 samples which had at least 5,000 reads per sample. These samples covered the following aggregate FIGURE 2 | The CS database provides good coverage of dominant taxa within a query dataset. Query OTU reference sequences (dataset 1, Table  1 ) were grouped into 1,000 bins by decreasing rank (e. g. , the 1000th bin contains the least abundant OTUs); and the proportion of each bin matching the CS dataset calculated and displayed on the y-axis.
The proportion of matches to the CS database (> 97% similarity) declines as query taxa become rarer, despite the comprehensive nature of the CS database. vegetation classes: crops and weeds, fertile grassland, heath and bog, infertile grassland, lowland wooded, moorland grass mosaics, tall grass and herb, and upland wooded. Further examination of the species accumulation by sample curves for the resulting 13,781 OTUs, revealed saturation implying that this dataset had complete coverage of common OTUs, defined by being present in at least 30 samples across Britain. Hierarchical logistic regression (HOF) models were then applied to determine individual bacterial taxa responses to pH. HOF models enable individual taxon responses to be modeled along environmental gradients using five response shapes,that increase in number of parameters and shape complexity.
A HOF modeling approach was taken as HOFs discrete response shapes allow for good interpretability in ecological contexts. GAM's were considered as an alternative method, but were not used due to the challenges associated with choosing suitable smoothing parameters to avoid overfitting  (Jansen et al. , 2013). HOF models were generated using the R package eHOF using a poisson error distribution. Model choice was determined using AIC and bootstrapping methods implemented with the eHOF package  (Jansen and Oksanen, 2013) , whereby the model with the lowest AIC was initially chosen and its robustness then tested by rerunning models on 100 bootstrapped datasets (created by resampling with replacement). If the most frequently chosen model in the bootstrap runs was different to the initial model choice, the most common bootstrap choice was selected.
, 2011) , which was further confirmed by a similar regression tree analyses of this sequence dataset (not shown). This pH value also represents a critical threshold in microbial functioning  (Jones et al. , 2019). Similarly, a second threshold was designated at pH 7, with OTUs exhibiting an optima above this being classed as neutral, and those between 5. 2 and 7 classed as "mid. " Plateau model shapes (model III), were sometimes more difficult to classify, since two optima are provided which span the plateau, and in some cases these crossed the pH 5. 2 and 7 thresholds. Whilst OTUs exhibiting this response were in the minority, we opted to assign a separate designation representing this range, for instance "acid to mid" for an OTU with two optima above and below pH 5. 2. The proportion of taxa classified to each pH response group are shown in Table  3.
Incorporating CS Data and pH Responses Into a Sequence Identification Tool A web application was developed using the Shiny package 2  which enables users to BLAST a 16S query sequence against the countryside survey representative sequences, subsequently allowing visualization of key environmental information including HOF model outputs, relevant to individual matched sequences. The Graphic User Interface was implemented in R (3. 4. 1) using the Shiny package (see text footnote 3) alongside ShinyJS to execute JavaScript functions from R 3. BLASTn commands are executed from R using the users query sequence, e-value of 0. 01, and the reference sequence database of CS representative sequences. eHOF model objects were converted to binary using the Rbase serialize function and stored in a PostgreSQL (9.
3. 17) database 4  alongside model and other environmental metadata (Supplementary Figure  1 ). BLAST results are displayed as an interactive table of hits, each hit linking to a plot of the pH model fit (based upon raw read number), a LOESS fit (based on relative abundance), a box plot of habitat associations and a simple interpolated map showing relative abundance distribution across Britain (Supplementary Figure  2 ). Additionally we provide a text box which can be populated with user submitted trait related information on matched OTUs. The application is available at shiny-apps. ceh. ac. uk/ID-TaxER/ and to facilitate batch processing of query sequences the sequence database, taxonomy and trait matrix are released via github (github. com/brijon/ID-TaxER-flat-files) for integration into bioinformatics pipelines.
Utility in Predicting pH Preferences and Community Structure Using a Query Dataset To demonstrate both the utility of the reference sequence database, and the HOF modeling approach to identify environmental responses of soil bacterial taxa, we used a query dataset of > 400 samples collected across Britain (dataset 1, Table  1 ). Since this survey focused on productive habitats (grassland and arable land uses), with only a few acidic samples, it was not appropriate to generate independent HOF models. Instead we classified the samples according to the same pH cutoff levels identified above (pH 5. 2 and 7) and then determined pH responsive taxa using indicator species analyses  (Dufrene and Legendre, 1997). As can be seen in Figure  5A , the pH groupings were clearly evident in the sample based ordination.
Representative sequences from this dataset were then blasted against the CS database, and optimum pH and pH classification metrics retrieved from the top hit for subsequent comparison. In total 477 indicators for the three pH groupings were retrieved, of which 454 had a match greater than 97% similarity to the CS database. Of the 155 acidic indicator taxa identified in the query dataset, 129 (83%) were reliably classified as acidic OTUs based on matches to the CS database (Figure  5B ), with 20 OTUs "incorrectly" classified as having a mid-pH optima. However, the predicted optima of these OTUs was mainly below pH 6 and most lie very close to pH 5. 2. Similarly for the 226 query taxa identified as indicating neutral soils, 203 (90%) had a neutral pH classification in the CS database, with 15 being incorrectly classed as mid, though the optima for these was between pH 6.
5 and 7. Sixty-seven indicators of the query mid pH soils were obtained of which 64 (96%) had a mid pH classification based on match FIGURE 5 | Validating the pH models using a query dataset. Taxa strongly responsive to soil pH were identified from Query dataset 1 (Table  1 ), and then matched to the CS database to evaluate utility of the approach. (A) NMDS ordination plot of the query dataset, with pH groupings denoted by color (red = pH < 5. 2; green = pH > 5. 2 < 7; and blue = pH > 7). (B) Indicator species analyses on the query dataset revealed 477 OTUs strongly associated with the three pH classes ("Observed pH class"). The y-axis values and point color denote the predicted pH optimum, and predicted pH class following matching to CS database. (C) The relative abundances of the 100 most abundant taxa in the query dataset were predicted using the CS HOF models of matched taxa, and subjected to NMDS ordination.
The plot shows that the predicted abundances of these taxa reliably predicted the observed data first axis NMDS scores. to the CS database. Overall this analyses shows that information on soil pH preferences from independent datasets can be reliably obtained using our approach. We then sought to test whether we could reliably predict community structure using the CS HOF model outputs to predict query OTU abundances. We identified the most abundant OTUs in the query dataset, and blasted against the CS database. CS HOF models were then used to predict the abundances of the 100 matched dominant OTUs within the 424 query samples. This predicted community matrix was then subject to NMDS ordination with the first axis scores plotted against the actual observed ordination scores generated from 24,260 OTUs.
The results in Figure  5C  show that the observed and predicted first axis ordination scores were highly related (r 2 = 0. 88) demonstrating that it is possible to predict broad scale community change from individual OTU relative abundance pH models. These findings add to a growing body of literature on the predictability of soil bacterial communities  (Fierer et al. , 2013; Griffiths et al. , 2016; Bickel et al. , 2019) ; but furthermore demonstrate the utility of our overall approach in deriving meaningful ecological information from matches to a 16S rRNA sequence database incorporating ecological responses.
CONCLUSION This work demonstrates how large scale soil molecular survey data can be used to build robust predictive models of bacterial abundance responses across environmental gradients. The models were applied to the single soil variable of pH which is known globally to be the strongest predictor of soil bacterial community structure in surveys spanning wide environmental gradients. We have produced an informatics tool incorporating extensive sequence data from a wide range of soils, linked to taxonomic and ecological response information. This currently includes data on the modeled pH optima, and the predictive utility in this regard was demonstrated using an independent dataset. Other ecological information is also made available via an online portal including habitat association, spatial distribution, and metrics relating to abundance and occurrence.
We are currently working on incorporating other information on the sensitivities of discrete OTUs to land use change; and there is the wider potential for users to update the trait matrix with other observations (more information provided at https://github. com/brijon/ID-TaxER-flat-files). Such information could include sensitivities to perturbations such as climate change, as well as rRNA derived links to wider genome data to inform on function. We anticipate this simple database and tool will be of use to the soil molecular community, but also hope it prompts further global efforts to better capture relevant ecological information on newly discovered microbial taxa. We acknowledge some limitations of the current tool, and identify some possibilities to develop further: firstly being a 16S rRNA amplicon dataset, the database inventory will be affected by known biases relating to PCR primers and amplification conditions, i.
e. , taxa that are known to be poorly detected by the primers used will be under represented in the database  (Thijs et al. , 2017). Secondly as the database features the V3-V4 region of the 16S rRNA amplified with 341f/806r primers and therefore obviously, user datasets built on a different region of the 16S rRNA gene will not produce any matches. Additionally the length of sequences means only limited taxonomic resolution is currently provided, and ecological inferences based on BLAST matches must consider the strength of match, and variance within the matched region with respect to taxonomic discrimination  (Fox et al. , 1992). Emerging long read sequencing technologies applied to survey nucleic acid archives in the future may improve these current constraints  (Singer et al. , 2016). With respect to the pH models, many other factors can of course influence bacterial abundances  (Thomson et al.
, 2010; Fierer, 2017) , and we note the large degree of variance in relative abundance for a taxon even within its apparent pH niche optima (Figure  3 ). Such variance could may be caused by nutrient availability, stress etc. and more complex models, albeit constrained by pH, need to be formulated to advance predictive accuracy. More generally, we assert that observed taxon relative abundance only inform on relative taxon success at a given soil pH, and does not identify any explicit underpinning ecological mechanism (e. g. , pH stress tolerance vs. competitive fitness)  (Austin, 1999). However, linking emerging genomic data to detailed environmentally relevant sequence databases such as detailed here, will likely improve future understanding in relation to elucidating specific functional response traits and determining mechanisms underpinning bacterial community assembly along soil gradients.
Finally, and importantly, the CS database is spatially constrained to a temperate island in Northern Europe, and would benefit from a more global extent to capture other soil biomes such as drylands. Improvements here could be made from integrating data from global sequencing initiatives, or leveraging data from sequence repositories provided consistent environmental metadata can also be retrieved in order to reliably predict response trait characteristics. Further use of data from global sequence repositories, would likely shed light on more taxa and potentially reveal more on those taxa deemed rare in for example national focused studies.
MATERIALS AND METHODS Samples were collected as part of the UK Centre for Ecology and Hydrology Countryside survey (CS) between June and July 2007 covering sites throughout Great Britain. Samples were chosen through a stratified random sample of 1 km squares using a 15 km grid, implementing the institute of Terrestrial Ecology (ITE) land classification to ensure incorporation of different land classes, with up to 5 randomly sampled cores (15 cm long × 4 cm diameter) taken within each square. Metadata for each soil sample were collated including soil organic matter, soil organic carbon, bulk density, pH, indicator of phosphorus availability using methodologies detailed elsewhere  (Griffiths et al. , 2011; Reynolds et al. , 2013). DNA was extracted from 0. 3 g of soil using the MoBIO PowerSoil-htp 96 Well DNA Isolation kit (Carlsbad, CA) according to manufacturer protocols.
DATA AVAILABILITY STATEMENT ID-TaxER tool is available at https://shiny-apps. ceh. ac. uk/ID-TaxER/. Countryside survey OTU representative sequences (16S), taxonomic and HOF model assignments are accessible at: https://github. com/brijon/ID-TaxER-flat-files. All Countryside survey 16S Sequences are deposited in the European Nucleotide Archive under accession PRJEB45286 and wider metadata for the samples is available at https://catalogue. ceh. ac. uk/ documents/79669141-cde5-49f0-b24d-f3c6a1a52db8. The bioinformatics pipeline used to pre-process 16S sequences can be found at https://github. com/brijon/sgtoolkit. Exemplar R code for generating all figures in manuscript is provided 5 hannonlab. cshl. edu 6 jgi. doe. gov/data-and-tools/bbtools/ at https://github. com/brijon/Beyond_taxonomic_identification.
Lately,  Ungermann et al. (2017)  demonstrated that the mean ice thickness in the Arctic increases with the number of categories used in the ITD and found that the better-resolved solution does not lead to the better model-data fit. Since simulations were tuned by minimizing cost functions, they did not investigate the physical processes explaining the model behaviors. In this targeted study, we aim at assessing the response of a state-of-the-art coupled ocean-sea ice model, Nucleus for European Modelling of the Ocean (NEMO) version 3. 6 and Louvain-la-Neuve sea Ice Model (LIM) version 3 (NEMO3. 6-LIM3), to changes in the ITD discretization. From this model assessment, we wish to provide recommendations to the NEMO community (and more largely to users of large-scale sea ice models) regarding the number of thickness categories and the position of their boundaries, based on a physical understanding of the mechanisms at play.
To this end, we analyze ice volume tendency terms diagnosed in the model in order to separate thermodynamic contributions from dynamic ones but also surface from bottom ones. We focus our analysis on the response of the model climatology rather than on the long-term trends and interannual variability (which will be analyzed in a companion study). This paper is organized as follows. The ocean-sea ice model (NEMO3. 6-LIM3), its configuration and the series of sensitivity experiments are described in Sect. 2. The results are presented and discussed in Sect. 3. Conclusions and recommendations are finally drawn in Sect. 4. Note that all results and figures presented in this article can be reproduced bit-wise thanks to the archiving of the data and scripts on publicly available repositories (see "Code and data availability" at the end of the paper).
Continental freshwater inputs consist of river runoff rates from the climatological dataset of  Dai and Trenberth (2002)  north of 60 • S, of prescribed meltwater fluxes from ice shelves along the coastline of Antarctica  (Depoorter et al. , 2013)  and of climatological freshwater fluxes from iceberg melt at the surface of the Southern Ocean  (Merino et al. , 2016). (Schweiger et al. , 2011; Zhang and Rothrock, 2003). The stars show the monthly data and the curves are cubic interpolations between the data points. The sole difference compared to  Barthélemy et al. (2017) 's setup is the shorter integration time  (1979-2015 instead of 1958-2015)  due to the computational cost of the multiple sensitivity experiments. Simulations are only weakly impacted by the length of integrations. A comparison of the reference simulation labeled S1.
Acknowledgements. Computational resources have been provided by the supercomputing facilities of the  Université catholique de Louvain (CISM/UCL ) and the  Consortium des Equipements de Calcul Intensif en Fédération Wallonie Bruxelles (CECI)  funded by the  F. R. S. -FNRS  under convention 2. 5020. 11. François Massonnet is a F. R. S. -FNRS Research Associate. The research leading to these results has received funding from the  European Commission 's  Horizon 2020 APPLICATE  ( GA 727862 ) and  PRIMAVERA (GA 641727 ) projects. Financial support. This research has been supported by the  European Commission (APPLICATE  (grant no. 727862 ) and  PRIMAV-ERA  (grant no. 641727 )). Review statement. This paper was edited by  Qiang Wang  and reviewed by two anonymous referees.
Make Spectra Available in Tabulated Form Spectra should be made available in tabulated form at 1-10 nm spacing (depending on the instrument) between 380 and 780 nm, where possible. The table should be included as data files in the Supplementary Material. Where a journal does not offer the capability of making Supplementary Material available, files can be made available on FigShare (https://figshare. org/), the Open Science Framework (https://osf. io/) or GitHub (https://github. com/). Some institutions also offer repositories for research data. To future-proof documentation of stimulus conditions and thereby lengthen the "lifetime" of a piece of scientific work, it is strongly recommended that the files are made available as uncompressed ASCII files (in comma-separated or otherwise delimited file format), to avoid the risk of digital obsolescence of proprietary file formats.
Although there is extensive fluctuation in GC content along the ICE containing the bacteroidetocin B locus, the lowest GC region is the ~19-kb kb region containing the bacteroidetocin B biosynthesis locus (average GC 31. 1%). This finding suggests that this bacteriocin region may have originally been acquired from a low G+C bacterium such as a Gram-positive bacterium. Prevalence of bacteroidetocin A and B in human gut metagenomes. Although the bacteroidetocin A and B genetic regions are not present in many of the sequenced Bacteroides genomes, our finding of Bacteroides strains with these toxins in the gut ecosystems of four of our 15 human subjects suggests they may be more prevalent than indicated by the analysis of sequenced genomes. To better understand the distribution of bacteroidetocin A-and B-producing strains in the human population, we analyzed the "3 consortium gene catalog" (3GCG) human gut metagenomic dataset  27  that includes 1070 human subjects from four different countries.
Data are shown as the mean of biological triplicates with the SEM plotted as error bars, as calculated by Prism version 8. 0. 1 for 64-bit Windows (GraphPad Software, San Diego, CA). For calculations of bacteroidetocin effect on bacterial viability, the experiments were performed as above except the starting cultures (T = 0) were plated for enumeration and wells containing bacteria exposed to toxin (biological triplicates) were plated after four hours and compared to the cfu of bacteria in a well treated only with the water control. Analysis of bacteroidetocin A and B from wild-type bacteria. For analysis of native bacteroidetocin A and B, BvCL01T12C17, BtCL15T12C11, and BtCL15T12C11Δ1844 were grown anaerobically as a lawn on BHIS agar plates for two days. Bacteria and associated toxin were swabbed from the plates and resuspended in PBS.
Peptide sequences (and hence protein identity) were determined by matching the acquired fragmentation pattern with protein databases using the software program Sequest (Thermo Fisher Scientific, Waltham, MA)  51. All databases included a reversed version of all the sequences and the data was filtered to between a one and two percent peptide false discovery rate. Mouse colonization studies and western blot analysis. Mouse studies were approved by the Institutional Animal Care and Use Committee (IACUC), Brigham & Women's Hospital and comply with all relevant ethical regulations for animal testing and research. Six female and six male Swiss Webster germ free mice 8 weeks old were obtained from and maintained in the Harvard Digestive Diseases Center gnotobiotic core facility at Brigham and Women's Hospital, Boston, MA.