dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR analysis A Bruker Avance HD 700 MHz (Bruker Biopsin, Rheinstetten, Germany) equipped with a cooled SampleJet and a TCI CryoProbe from the same manufacturer was used to acquire the spectra from all biofluids. The data was recorded at 25 °C ± 0. 15 °C calibrated with a Methanol-D4 sample  (Findeisen et al. 2007 ). One-dimensional 1 H NMR-based experiment, Carr-Purcell-Meiboom-Gill (CPMG)  (Meiboom and Gill 1958) , was recorded with presaturation applied for 3 s during the relaxation delay at a B 1 field of 50 Hz, 64 scans, 8 dummy scans and 32 k complex pairs over a spectral width of 9091 Hz. CPMG T 2 filter was set at 39 ms (122 echoes of 316 µs).
Data processing and statistical analysis MestReNova version 11. 0. 2-18153 (Mestrelab Research S. L. , Spain) was used to pre-process spectra. Manual phase correction and automatic baseline correction were performed using the Whittaker Smoother algorithm. Calibrations were carried out using TSP (δ 0. 00 ppm). Residual water (δ 4. 70 ppm -5. 10 ppm) and noise (regions before δ 0. 5 ppm and after δ 9. 5 ppm) were removed before exporting spectra for statistical analysis. Matlab version R2015b (Mathworks, UK) was used to carry out the statistical analysis using algorithms provided by the Korrigan Toolbox version 0. 1 (Korrigan Sciences Ltd. , U. K. ). After normalisation using a median-base probabilistic quotient method  (Dieterle et al. 2006 ) sample variability was evaluated using an unsupervised Principal Component Analysis (PCA).
Supervised pairwise Orthogonal Projection to Latent Structures Discriminant Analysis (O-PLS DA) was used to identify metabolomics modulations associated with the time of death. Seven-fold crossvalidation was used to evaluate the goodness of prediction (Q 2 Y value) of the O-PLS DA models. The scores are shown as a plot of the model scores (T) on the x axis against the cross-validated scores (Tcv) on the y axis. This allows to visualise the level of overfit since overfitted models would scatter away from the diagonal. The model loadings plot was colour-coded to display metabolites that are strongly associated with the discriminant component in warm colours (colours close to red); by contrast, metabolites in cold colours (colours close to blue) are not discriminant. Chenomx NMR Suite 8. 2 from Chenomx Inc (Edmonton, Canada), The Human Metabolome Database (http://www.
Results Determination of time of death still represent a challenge for the scientific community. In this investigation, we combined NMR metabolic profiling with multivariate statistics to evaluate sequential metabolic modulations after death. Principal Component Analyses (PCA) were carried out on all datasets to evaluate sample variability. Specific metabolic modulations were more accurately evaluated using pairwise supervised O-PLS DA analyses comparing Time Point 1 (immediately after euthanasia), Time Point 2 (six hours after euthanasia) and Time Point 3 (twenty-four hours after euthanasia). Heart was one of the tissues that underwent the most metabolic modulations after death. In total, 65 metabolic modulations occurred when comparing the three-different time points. These modulations were evenly distributed between the three possible pairwise comparisons.
Acknowledgements The authors thank the  Medical Research Council (MRC)  for funding this research ( M004945/1 ). We also wish to thank all the staff from the  Biological Resource Unit (BRU)  from the  University of Reading , particularly to  Wayne Knight  and  Sophie Reid , for their technical assistance and  Ianis Charrouf  for technical support. This work was also supported by the  Francis Crick Institute  through provision of access to the  MRC Biomedical NMR Centre. The  Francis Crick Institute  receives its core funding from  Cancer Research UK  ( FC001029 ), the  UK Medical Research Council  ( FC001029 ), and the  Wellcome Trust  ( FC001029 ).
On the other hand, if connectivity profiles are homogeneously distributed across hosts, this would provide evidence to support the idea that microbiome assembly processes do not affect host-microbiome network structure. Here, we use the Sponge Microbiome Project dataset  30  , comprising over 2000 microbial community samples derived from over 150 globally distributed marine sponge species, to construct the global sponge-microbiome network. We interrogate this network to analyse host occurrence patterns of ~375,000 spongederived microbial OTUs across these hosts. To examine the structuring of the most prevalent interactions between sponges and microbes, we also construct a sponge core-microbiome network where we restrict our analyses to OTUs that are present in at least two-thirds of the samples and with a relative abundance >0.
Sponges and their microbial symbionts are renowned for their bioactive compound production  37, 38  ; and this function appears to have a significant role in structuring the microbiome. 2  Sponge phylogeny is associated with core network modularity. Sponge species belonging to the same module identified over the sponge core-microbiome network display the same colour. Stars, crosses and filled squares refer to sponge type according to whether they are high-or lowmicrobial abundance species (HMA and LMA respectively). NA = no information for host type is available. Only 144 out of the 156 host species considered in this study are shown because 10 of the original host species were not resolved to the species level and the sponge species Ircinia fasciculata and Strongylacidon conulosa did not have matches in the Open Tree of Life from which this tree was extracted (see Methods).
The axis derived from the modular classification of the sponges (dbRDA1) explains 17. 64% of the total variation, while the first axis of unconstrained ordination explains 38. 78% of the variation. Module 0 (black dots) comprises original modules 1, 2, 5 and 6 in the sponge core-microbiome network, which were not found to be significantly different from each other by pairwise PERMANOVA comparison in terms of predicted metabolic functions metabolic abilities. This is consistent with recent findings by Björk et al. 58  of prevalent amensal and commensal interactions between bacteria within the core sponge microbiome of three HMA sponges. Positive interactions however, are known to be destabilising in ecological communities, including microbiomes  59. To counteract this destabilising effect, Coyte et al.
Methods Network construction. Using the 16S rRNA gene sequencing data from the Sponge Microbiome Project (SMP) dataset  30  which comprises marine sponge samples from many locations across the globe (Supplementary Figure  6 ), we selected samples belonging to sponges that were compliant with the following criteria: (i) at least three samples where present in the dataset, (ii) these had >10 4 16S rRNA gene sequencing reads, (iii) were extracted from healthy, adult individuals, and (iv) had unequivocal taxonomic identification. In addition, OTUs that were present in seawater samples from the SMP dataset with relative abundances larger than 0. 01%, were removed from sponge samples, since they were likely derived from seawater that was part of the sponge sample. From this resulting dataset, a bipartite network was constructed based on OTU occurrence (presence/absence) within each sponge species.
e. they were present in only one sponge). While this might affect the absolute value of the modularity measure (Q), it does not affect the way in which nodes are partitioned into modules, since all nodes that are connected to only one other would always be assigned to the same module its partner belongs to. From a biological point of view, an OTU found in only one sponge species (i. e. nodes with one link) can only be part of the module to which its only host belongs. We thus consider the modular partition of the network obtained in this way to be equivalent to that of the whole network. Data rarefaction. To assess the robustness of the modularity partition obtained using the procedure described above to a different sequencing depth, we performed pairwise permutational analyses of variance (PERMANOVA) on 100 rarefied datasets of the original OTU table using the same modules detected in our network as an independent variable.
Thus, testing for 'network module' as a source of variation in microbiome composition. In this way, we can assess whether the modular partition obtained for the network (i. e. the main topological feature our analyses focus on) is robust across the rarefied datasets. We rarefied the data to 10 4 reads per sample (i. e. the size of the smallest sample in the dataset) using the rrarefy function of the vegan  63  package. This procedure was repeated independently 100 times to obtain 100 different rarefied instances (i. e. randomisations) of the data. We then analysed each of these rarefied datasets using pairwise PERMANOVA with the adonis function from vegan  63. p-values were corrected for false discovery rates due to multiple comparisons using the Benjamini-Yekutieli correction. We then obtained the average and standard deviation of the values obtained from this test across the 100 randomisations of the rarefied data.
P i ¼ 1 À X N M s¼1 k is k i 2 ð3Þ where k is is the number of links node i has to nodes in module s, and k i is the total degree of node i. These measures, as their names suggest, provide a quantified measure of the degree to which a node is well connected within its module and the extent to which it connects to other modules in the network  29. Impact of environmental variables and microbial abundance. The effect of the environment on the structure of the global network was assessed by looking at the relationships between (i) temperature, (ii) depth, and (iii) biogeographical location to the structural modularity of the network, using environmental metadata derived from the SMP dataset. Biogeography was assessed by mapping the sampling location of each sponge sample onto the Marine Ecoregions of the World  64.
These marine ecoregions represent broad-scale patterns of species and communities in the ocean such that communities within ecoregions are thought to have been subjected to similar historical conditions (environmental or otherwise) that could have influenced their assembly  64. Biogeographic data was obtained as a Geographical Information System (GIS) layer from the World Wildlife Fund (https:// www. worldwildlife. org/publications/marine-ecoregions-of-the-world-abioregionalization-of-coastal-and-shelf-areas) and overlaid on sampling points for each sponge species available as part of the SMP dataset. Finally, information on microbial abundance within each host species was used to classify ~83% (n = 129) of the species into either a high-microbial abundance (HMA) or low-microbial abundance (LMA) category as per  18, 65  (see Supplementary Data 1 for the metadata used in this study).
01% across the whole dataset and (ii) were present in at least two-thirds of the samples from the species they were associated with. These criteria were defined by Astudillo-García et al. 31  and are robust to different filtering criteria. We acknowledge however that this procedure implies a space for time substitution for the presence of microbes, and thus 'transient' microbes are actually transient across species samples and not through time. Filtering according to the core microbiome reduced the number of OTUs to 10,941 resulting in a sponge core-microbiome network containing only 51,207 links. Phylogenetic analysis and UniFrac. To assess the extent to which observed patterns in network modularity were driven by microbial or host phylogeny, phylogenetic trees were constructed for both the sponges and the microbial OTUs in the network.
OTU phylogeny was assessed using 16S rRNA gene neighbourjoining phylogenetic construction within the software MEGA7  66  for the subset of OTUs in the core network, while FastTree 67 was used to construct the OTU phylogeny for the global network. Sponge phylogeny was extracted from the Open Tree of Life 68 (OTL), using the R 69 library rotl  70. To evaluate whether OTUs or sponges that are closer in the tree of life are found in the same network modules, their relationship was analysed using the unweighted UniFrac 71 algorithm provided by the Mothur 72 software package. Unweighted UniFrac determines the extent (quantified as the phylogenetic distance) to which any of the groups (network modules in our case) into which the different OTUs in the phylogenetic tree have been aggregated have a significantly different phylogenetic composition than the other groups.
The UniFrac score is thus the difference (or more intuitively, the distance) between microbial communities in a given module to the rest; or between two modules in pairwise comparisons. Functional profiling. To test whether network modules comprise microbes with distinct functional repertoires, we investigated the functional profile of each OTU and associated that information to their community membership. For the core microbiome, we thus extracted functional profiles based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) Ontology (KO) using Tax4Fun2  73. Only OTUs that were matched with a 97% similarity to sequences present in the SILVA database were included. Using this threshold, 994 (7. 6%) of the 10,941 total core OTUs could be functionally profiled. Tax4Fun2 extracts functional profiles calculated using complete genomes available through the KEGG database to generate reference data.
Functional profiles are obtained from each genome and are then associated with taxonomic keys (i. e. a particular genus) found in the SILVA reference genome database by using the 16S rRNA gene data from each genome. Using sequences aligned against the SILVA database from the original dataset, we used Tax4Fun2 to check whether a reference profile was available for each taxonomic classification. Tax4Fun2 then calculated a predicted metagenome incorporating the abundance of each OTU. OTUs assigned to a taxonomic key having no functional reference profiles would not be included in the prediction (called Fraction of Unexplained; FTU). For the present study, we generated the reference data as follows: we obtained more than 10,000 complete genomes available in RefSeq (the NCBI reference sequence database) and inferred a functional profile for each based on KEGG KO.
The obtained profile is subsequently normalised: the sum of all functions in a sample is 1. Tax4Fun2 is freely available as an R package at: https://sourceforge. net/projects/ tax4fun2/. Statistical analyses. All statistical analyses were performed in R  70. Sample temperatures and depths were classified into groups according to the network module that the corresponding sponge species belong to. Statistical support for differences in temperature and depth distributions across modules was then assessed using Nemeyi tests for multiple comparisons of (mean) rank sums of independent samples using the PMCMR  74  library. Non-metric multidimensional scaling (NMDS) to obtain the ordination of community similarity was performed using the metaMDS function of the vegan  63  package with Bray-Curtis as the dissimilarity measure.
test function in R. Differences between functional profiles of sponges and the identified modules were explored using principal coordinate analysis (PCoA) of Bray-Curtis dissimilarity matrices of the log2-transformed relative abundances of metabolic functions inferred for each sponge species. Pairwise permutational analyses of variance (PERMANOVA) were used to test for significant differences between groups of sponges (according to their module membership). PERMANOVA was also used to test microbial community differences between host type. To quantify the differences in microbial community composition within groups (i. e. host type), we analysed the homogeneity of the groups dispersion (i. e. variance). PCoA was performed using the pcoa function of the ape library, and PERMANOVA tests were performed using the adonis function in vegan  63.
Multiple pairwise comparisons of the PERMANOVA tests were corrected based on the Benjamini-Yekutieli false discovery rate control. Analysis of the homogeneity of variance among groups was performed using the betadisper function in vegan  63. To identify microbial functions that were significantly different across modules, an indicator (i. e. multi-level pattern) analysis was performed. All modules that were not significantly different to the rest based on PERMANOVA analysis (above) were considered as a single module and the multipatt function from the indicspecies 76 library was applied. The resulting bi-serial coefficients (R) of each function/pathway within each network module were corrected for unequal sample size using the function r. g 77. Obtained p-values for the permutation tests were adjusted for multiple testing using the Benjamini-Hochberg correction  78.
Metabolic pathways identified by the indicator analysis to be significantly different (with adjusted p-values of permutation tests < 0. 05) among modules of the core network were used to illustrate the metabolic differentiation across them. Constrained distance-based redundancy analysis was performed on the log2transformed relative abundances of the subset of metabolic pathways thus identified. Module membership was used as the constraining factor. This analysis was performed using the dbrda function from vegan  63. Reporting summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements M. L. and J. M. M. are supported by the  French ANR  through  LabEx TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-002-02 ) by a  Region Midi-Pyreńeés Project  ( CNRS 121090 ), and by the  FRAGCLIM Consolidator Grant , funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement Number  726176 ). M. L. acknowledges support from the  Embassy of France in Australia  through its scientific mobility programme, which benefited this collaboration. T. T. is supported by the  Australian Research Council.
Data availability The datasets analysed during the current study have been made available previously in ref. 30. Raw sequence data have been deposited in the European Nucleotide Archive (BioProject: PRJEB18736; Accession number: ERP020690). Quality-filtered, demultiplexed fastq files, and QIIME resulting OTU tables are available at the Qiita database: http://qiita. microbio. me (Study ID: 10793). Additional information derived from the sequence data including an OTU abundance matrix, an OTU taxonomic classification table, an OTU representative sequence FASTA file, and the metadata for this dataset, which are the only three data sources used in this study, are available from the GigaScience repository, GigaDB (https://doi. org/10. 5524/100332)  30. The authors declare that all other data supporting the findings of this study are available within the article and its Supplementary Information files.
| Genetic data collection and quality control Genomic DNA was extracted from whole embryonic or larval individuals using Qiagen DNeasy kits following the manufacturer's instructions. We analyzed variability at 10 microsatellite loci to evaluate spatial genetic structure for each species. PCR components, thermal cycler profiles, and citations for loci primer sequences are described in Appendix S1. Negative controls were included in each 96-well PCR to allow for detection of reagent contamination. Microsatellite fragment analysis was conducted using an ABI 3730 automated genetic analyzer (Applied Biosystems, Inc. ) at the University of Maine DNA Sequencing Facility. Genotyping was performed using Geneious v7. 1. 9 with fragment sizes based on GeneScan 500 LIZ Size Standard (Applied Biosystems) and all allele calls confirmed visually.
| Genetic diversity and differentiation We quantified genetic diversity within each site and genetic differentiation among sites using multiple measures. Average number of alleles per site (A O ) was estimated using PopGenReport, and allelic richness (i. e. , allelic counts rarefied based on smallest sample size per species; spotted salamander: 10, wood frog: 12; AR), expected heterozygosity (H E ), and Wright's inbreeding coefficient (F IS ) were estimated using the R package hierfstat  (Goudet & Jombart, 2015). Genetic differentiation was calculated using G ST (i. e. ,  Nei, 1973; Nei & Chesser, 1983)  and G″ ST  (Meirmans & Hedrick, 2011). G ST (commonly reported as F ST ) summarizes the amount of diversity contained among populations relative to the diversity of all populations combined  (Nei, 1973) , whereas G″ ST provides a scaled maximum value of G ST based on the genetic diversity within a measured population  (Meirmans & Hedrick, 2011).
Both G ST and G″ ST were estimated using the R package mmod  (Winter, 2012). Statistical significance of pairwise population differentiation was evaluated with an exact G test implemented using the genetic differentiation option in Genepop v4. 2  (Raymond & Rousset, 1995; Rousset, 2008)  with a FDR correction for type I error rates. The spatial arrangement of effective genetic diversity was visualized using estimated effective migration surfaces (EEMS;  Petkova, Novembre, & Stephens, 2016). Effective genetic diversity reflects the expected genetic dissimilarity of two individuals sampled within each deme assuming a generally IBD-driven system and a stepping-stone dispersal pattern  (Petkova et al. , 2016). EEMS constructs a dense, regular grid across the study range and assigns sampling sites to the nearest grid intersection (node), often resulting in a set of fewer demes than the actual number of sampling sites.
Next, we estimated the slope (β) of variable intercept IBD regressions that were performed repeatedly using expanding datasets based on distance between sampling sites, which generated IBD scaling profiles for each species. For example, the first iteration of the analysis was conducted using the 20 shortest pairwise geographic distances, the next iteration with the 21 shortest, and so on, until all pairwise comparisons were included. We performed 1,000 bootstrapped replicates of each regression using the "Boot" function in the R package car  (Fox & Weisberg, 2011)  to estimate each beta coefficient and its 95% confidence intervals. The slope confidence intervals for each IBD regression were then plotted using the maximum analyzed distance as a response variable to generate the IBD scaling profiles.
| Regression and multivariate analyses We assessed the influence of urbanization on a sampling site's degree of isolation and genetic diversity. The intensity of urbanization near a site was quantified using six environmental characteristics measured in ArcGIS v10. 2 (ESRI): distance to nearest roadway, percent impervious surface within one km, length of roads within 1 km for light, secondary, and primary road types, and percent canopy cover within 1 km. Road type and classification was determined and percent canopy cover data were drawn from the 2011 National Land Cover Database  (Homer et al. , 2015). Collinearity between the urbanization-related explanatory variables was evaluated, and one variable was selected at random to be retained from each set with a correlation coefficient exceeding 0.