dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample volume effects on beta-diversity and community composition were seen on different taxonomic groups from other studies, including bacteria, eukaryote and metazoans in both terrestrial and aquatic ecosystems  12, 17, 44, 46. While the effects of sample volume on beta-diversity were significant in our metazoan and non-metazoan eukaryotes datasets (Fig. 2 , Table  1 ), there were important differences between these two taxonomic groups. Our results suggest that the analysis of a much larger percentage of the core is needed for metazoans (78%) than for non-metazoan eukaryotes (43%) in order to achieve a representative picture of the real beta-diversity, indicating a higher degree of patchiness for the metazoan community. Such small spatial scale patchiness has also been reported for small benthic metazoans like meiofauna  12, 14  as these animals tend to cluster in microsites rich in organic matter content  14, 49.
Community composition. We found a high diversity of eukaryotes in brackish sediments of the Baltic Sea. In terms of relative OTU abundance (Fig. 6  and Supplement Fig. S6 ) the Copepoda was the most represented group, despite nematode being generally the most abundant taxon in metazoan datasets for brackish and marine sediments  7, 12, 54. It is possible that the higher prevalence of copepod OTUs here found is related to: (a) the degenerate nature of the eukaryotic primers sets used in our study  10, 55  , (b) high biomass of copepod tissue that was potentially differentially amplified  56  and (c) the extraction strategy used in our study designed to capture total (both intra-and extracellular) DNA. As such, and taking into account the size of our amplicons (~400 bp), our sample likely reflects DNA from contemporary and recently inhabiting organisms, in addition to DNA from planktonic organisms that has settled to the benthos.
Copepods species such as Acartia bifilosa and Arcatia tonsa found in our dataset deploy sediment resting stages  57  , thereby suggesting marine snow as the likely source of copepod eDNA. Similarly, Guardiola et al. 37  recovered more Arthropoda diversity compared to nematodes in 18 S nSSU HTS analyses from samples obtained from sea canyons where eDNA was extracted directly from sediment. Nevertheless, our study detected 194 unique nematode OTUs suggesting that we did manage to effectively sample metazoan diversity (Supplement Fig. S10 ). Concluding remarks. Collectively our work indicates that small spatial variability, even within a sampling unit, is a factor that needs to be taken into account in studies investigating eukaryotic and metazoan diversity in aquatic sediments. To achieve a representative assessment of the beta-diversity for both these communities it is necessary to sample a large percentage of the sampling unit (78% or 22 g).
The purified amplicons were then sequenced in both directions on an Illumina MiSeq platform at National Genomics Infrastructure (NGI-Stockholm, Sweden) as a single pool comprised of the 25 different samples with 25 unique index primer combinations (i. e. , five sample volumes -0. 2 g, 4 g, 6 g, 8 g, and 10 g -from 5 different sampling core replicates). Bioinformatics. Amplicon reads were demultiplexed by NGI and raw demultiplexed reads were used in the initial data processing and quality-filtering was carried out in QIIME 1. 8  63. Raw Illumina reads were first subjected to read pairing (merging Read 1 and Read 2) using the join_paired_ends. py in QIIME, followed by a demultiplexing step using the multiple_split_libraries_fastq. py script which split samples according to dual-index barcodes and also performed quality-filtering of raw reads.
Remaining forward and reverse PCR primer sequences were subsequently removed from Illumina reads using Trimmomatic version 0. 32  64. Processed reads were concatenated into a single file, and then subjected to open-reference OTU picking with Uclust using the pick_open_reference_otus. py script in QIIME (with 10% subsampling, no prefiltering, and reverse strand match enabled). Singleton and doubleton OTUs were discarded from the resulting OTU tables (retaining only OTUs with >2 reads). Two independent OTU picking workflows were carried out, using pairwise cutoffs at 96% and 99% sequence identify (hereby referred to as 96% and 99% OTUs). Taxonomy was assigned to representative OTU sequences using the RDP Classifier  65  in QIIME (assign_taxonomy. py), using the SILVA 119 release as a reference database  66.
To test for effects of sample volume on beta-diversity we conducted a PERMANOVA multivariate analysis of variance with the adonis function of the vegan package  70. The function PERMDISP was used to test for differences in beta-diversity among sample volumes in relation to the 100% treatment. Additionally, we used metrics to partition beta-diversity to quantify the relative importance of turnover and nestedness in the different sample volumes  72, 73. These authors propose that beta-diversity measured as Sørensen dissimilarity index (βsør) can be separated into dissimilarity as a consequence of turnover, i. e species replacement between sites or samples (βsim), and dissimilarity attributable to nestedness, species loss from sample to sample (βnes). We used the R package betapart 74 for this analysis.
Acknowledgements We would like to thank three anonymous reviewers for comments and suggestion that have helped to improve the manuscript,  Caroline Raymond  for assistance with sediment collection and the  MEFGL  staff for help with the laboratory work. FN's participation in this project was supported by the  Swedish Research Council  (grant nr  623-2010-6616 ), the  Swedish Research Council Formas  ( Mobility Grant   2013-1322 ) and the  Lars Hierta Minne Foundation. Sequencing was performed at the  National Genomics Institute, Sweden , with the support of the  SciLifeLab National Project in Genomics.
provided a proof of concept in 2010 8 , and this was refined by the first group to use such an approach in earnest 9. Ding et al. 10 published the first fully-fledged method for estimating length rather than just telomere content, with the accompanying tool 'TelSeq'. Their study was also the first time a computational method had been validated against an established experimental method. TelSeq assumes a fixed number of chromosomes when estimating telomere length and so makes no allowance for aneuploidy. Nevertheless, as the strongest available tool there are several examples of TelSeq being used to analyse cancer datasets  11, 12. Ṅotably a recent pan-cancer analysis made use of the TelSeq tool 6. While generally sound,.
Observing reads on the boundary between telomere and subtelomere provides a quantification of telomere numbers through which we normalize the telomere lengths. Where other samples always assume that more telomere reads mean longer telomere, Telomerecat is able to account for the fact that there may actually be more individual telomeres. Moreover, differences in patterns of sequencing error have the potential to lead to inconsistency between samples even if using the same method. To this end, Telomerecat includes a novel method for correcting sequencing error in telomere sequencing reads. This model automatically adapts to differing error across sequencing preparations. Telomerecat is an open source tool, the code is available from https://github. com/jhrf/telomerecat. Full installation and usage documentation is available at https://telomerecat.
Application to a longitudinal MSC data set. We applied Telomerecat to a set of WGS samples from a mesenchymal stem cell (MSC) experiment described previously  22. Mesenchymal stem cells are multipotent stromal cells commonly located in bone marrow  23. The experiment constituted six WGS samples: an in vivo MSC sample from a healthy 31 year old male, three passaged MSC samples (P1,P8 and P13) and two induced pluripotent stem cell (iPSC) samples. MSCs are unusual amongst mature human stem cells as they do not express any measurable amount of telomerase  24. Accordingly, telomere length attrition has been described in MSC passage experiments  25, 26. Conversely, iPSC cells have been shown to exhibit heightened telomerase expression  27. We hypothesised that telomere length would shorten across the passaged MSC samples and lengthen within the iPSC samples.
The results of applying Telomerecat and TelSeq to the aforementioned MSC WGS data are shown in Fig. 2. Telomerecat identifies telomere shortening across the passaged samples, as expected. Telomerecat estimates that between P1 and P13 the average telomere length was shortened by 2. 5 KB, at a rate of approximately 0. 2KB per passage. Furthermore, we see that Telomerecat identifies long telomere length in the the two iPSC samples. We also note that TelSeq fails to identify the expected telomere dynamics. Possible explanations for this discrepancy are discussed in detail in the Supplementary Information Section 2. Application to a cancer dataset. After establishing that Telomerecat performs well in diploid samples, we demonstrated that it can also be applied to cancer samples. We applied Telomerecat to a data set comprised of samples from four donors suffering from Hepatocellular carcinoma (HCC)  28.
We have also demonstrated that Telomerecat can be run quickly (five times faster than TelSeq for our example). Telomerecat is able to process samples quickly as it is built on a parallel BAM processing framework -parabam  38  -and thus uses multiple processing cores at all stages of the analysis. Telomerecat promotes reproducible research by generating subsets of reads from which telomere length estimates can be generated. We hope that these smaller file will be more easily stored and transferred allowing researchers to regenerate estimates without the need to process the cumbersome original BAM files. Finally, we have demonstrated the application to a cancer WGS dataset: Telomerecat's raison d' être. We see that Telomerecat identifies differing telomere phenotypes across four passage experiments.
Acknowledgements We thank  Lawrence Bower  for running bioinformatic pipelines, the  Cambridge Cancer Research Fund  and  Hayley Whitaker  for access to computing resources, and  Zhao Ding  for information regarding TelSeq. We also thank  Chris Penkett  for running bioinformatic pipelines and  Hana Lango Allen  and  Ernest Turro  for providing feedback on the repeated measurements study. This study makes use of data generated by the  NIHR BioResource -Rare Diseases , based at  Cambridge University Hospitals, Cambridge Biomedical Campus, Cambridge ,  CB2 0QQ ,  UK. A full list of the investigators who contributed to the generation of the data is available from http:// bioresource. nihr. ac. uk/rare-diseases/rare-diseases. Funding for  NIHR BioResource -Rare Disease  was provided for by the  National Institute for Health Research.
We acknowledge  Zhixin Qiu  and colleges at  Shanghai Institute of Biochemistry and Cell Biology  for granting access to the HCC cell line data. We acknowledge  TwinsUK  for providing WGS and mTRF telomere estimates. TwinsUK WGS data was generated by the  UK10K  Project. TwinsUK is funded by the  Wellcome Trust, Medical Research Council, European Union , the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guys and St Thomas NHS Foundation Trust  in partnership with Kings College London. JHRF, AGL and MLS were supported in this work by a  Cancer Research   UK Programme Grant  to  Simon Tavaré  ( C14303/A17197 ). Additionally, MLS was supported in this work by the  European Community 's  Seventh Framework Programme  under grant agreement No.
Data Availability 1. The Twins UK10K sequencing data are available from the EGA repository (accession ID: EGAD00001000194) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available upon reasonable request to datasharing@sanger. ac. uk and with permission of Twins UK10K. 2. The MSC sequencing datasets analysed during the current study are available in the NCBI SRA repository under accession ID SRP032359, https://www. ncbi. nlm. nih. gov/sra/?term=SRP032359. 3. The HCC sequencing sequencing data are available from the EGA repository (accession ID: EGAD00001001995) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.
Data are however available upon reasonable request to qiuzhixin@sibcb. ac. cn. 4. The repeated measurement sequencing data are available from the EGA repository (accession ID: EGAD00001003809) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available upon reasonable request to Stirrups (nihr_dac@medschl. cam. ac. uk) and with permission of NIHR BioResource -Rare Diseases. 5. The mice sequencing datasets analysed during the current study are available from the mouse genome project website repository, http://www. sanger. ac. uk/science/data/mouse-genomes-project.
Polar ice core water isotope records are commonly used to infer past changes in Antarctic temperature, motivating an improved understanding and quantification of the temporal relationship between δ 18 O and temperature. This can be achieved using simulations performed by atmospheric general circulation models equipped with water stable isotopes. Here, we evaluate the skills of the highresolution water-isotope-enabled atmospheric general circulation model ECHAM5-wiso (the European Centre Hamburg Model) nudged to European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis using simulations covering the period 1960-2013 over the Antarctic continent. We compare model outputs with field data, first with a focus on regional climate variables and second on water stable isotopes, using our updated dataset of water stable isotope measurements from precipitation, snow, and firn-ice core samples.
, 2011). We explore a simulation performed for the period 1960-2013 in which the atmospheric model is nudged to the European Reanalyses (ERA) ERA-40 and ERA-Interim  (Uppala et al. , 2005) , ensuring that the dayto-day simulated variations are coherent with the observed day-to-day variations in synoptic weather and atmospheric circulation (see  Butzin et al. , 2014 , for more explanation). This framework is crucial to performing comparisons between simulations and observations for temporal variations. Second, we compile a database of precipitation, snow, and firn-ice isotopic composition using data from precipitation sampling and ice core records and considering δ 18 O and deuterium excess (hereafter, d). These methods are described in Sect. 2. We then compare the model outputs with the available datasets (Sect.
, 2011). Finally, we extracted the reconstruction of temperature for Byrd station by  Bromwich et al. (2013)  based on AWS data and infilled with observational reanalysis data. No record meets our criteria for the Weddell Sea coast region (Fig. 1 ). SMB data have been extracted from the quality-controlled GLACIOCLIM-SAMBA (GC) database  (Favier et al. , 2013). We have selected data spanning the twentieth century, corresponding to 3242 punctual values, which have then been clustered within the corresponding ECHAM5-wiso grid cells for the calculation of gridded annual average values. As described by  Favier et al. (2013) , the spatial coverage of SMB field data is particularly poor in the Antarctic Peninsula, in West Antarctica, and along the margins of the ice sheet. As a result, SMB is not correctly sampled at elevations between 200 and 1000 m a.
A database of Each of the 1205 locations have given an individual index number. Data have been processed to calculate timeaveraged values (available at 1089 locations for δ 18 O values, 879 locations for δD, and 770 locations for d). The ice core records with sub-annual resolution were averaged at annual resolution over the period 1979-2013, resulting in 88 ice core records for δ 18 O and only 5 for d. Most precipitation records are not continuous and do not cover a full year, preventing the calculation of annual mean values. We have also used sub-annual records from 22 highly resolved ice cores (including 18 records giving access to δ 18 O and 5 records giving access to d) and precipitation sampling from eight stations to characterise the seasonal amplitude. For ice core records, we have only calculated the yearly amplitude from available measurements, as chronologies cannot be established at monthly scales.
Methods for model-data comparisons In the model, we have extracted specific daily variables for comparison with available data and then averaged them. We have extracted daily 2 m temperature outputs (hereafter 2 m-T) for comparison with surface air instrumental records, daily (precipitation minus evaporation) outputs (hereafter P-E) for comparison with SMB data, and daily precipitation isotopic composition outputs for comparison with measurements of isotopic composition data in the precipitation. For ice core data, we averaged daily precipitation isotopic composition weighted by the daily amount of precipitation. For each specific site, we selected the model grid cell including the coordinates of the site. When comparing model outputs with the database of surface data (time-averaged SMB and isotopic composition), available data have been averaged within each model grid cell.
Time selection was dependent on the variables. The 2 m-T outputs have been compared with temperature records for the period 1960-2013 based on annual averages and selecting the same years as in the data (see  Sect. 3. 1. 1). The comparison with other datasets (SMB, snow, and water stable isotopes from firn-ice cores) is restricted to the period 1979-2013 due to concerns about the skills of the reanalyses used for the nudging prior to 1979 in Antarctica (see next section). Daily (P-E) outputs were all extracted over the whole period 1979-2013 and averaged (see  Sect. 3. 1. 2). For comparison with the surface isotopic database (Sect. 3. 2. 1), daily precipitation isotopic composition was averaged by weighting by the daily amount of precipitation over the whole period 1979-2013. For the inter-annual variability (same section) or annual values (e.
g. for d outputs, see Sect. 4), daily precipitation isotopic composition was averaged by weighting by the daily amount of precipitation for each year of the period 1979-2013. For sub-annual isotopic composition, we used precipitation isotopic compositions (amplitude and mean seasonal cycle) and highly resolved ice cores (amplitude only). Precipitation isotopic composition data consist of a very small number of measurements, sometimes taken before 1979 (e. g. observations from DDU consist of 19 measurements during 1973), and thus model precipitation isotopic composition outputs were extracted at the very exact sampling date. Then, monthly averages were performed and mean seasonal cycles were calculated. The resulting mean seasonal cycles of precipitation isotopic composition were obtained the same way in both precipitation data and the model.
Model skills In this section, we assess ECHAM5-wiso skills with the perspective of using the model outputs for the interpretation of water stable isotope data. In polar regions, isotopic distillation is driven by fractionation occurring during condensation, which is itself controlled by condensation temperature  (Dansgaard, 1964). We thus first compare ECHAM5-wiso outputs with regional climate records, as this comparison may explain potential isotopic biases. This includes a comparison with reanalyses in order to explore the role of nudging in model-data mismatches. We then compare ECHAM5wiso outputs with our isotopic database.
e. over 1960-1978 and 1979-2013, respectively)  and with the station temperature data (horizontal black lines, Fig. 3 ). All datasets reveal a cold bias simulated by both the reanalyses and ECHAM5-wiso at all stations but Byrd and Vostok over the two periods  (only over 1960-1978 for Neumayer and Esperanza) , but this bias is larger over the period 1960-1978 compared to the period 1979-2013. This finding supports our earlier suggestion for Dumont d'Urville  (Goursaud et al. , 2017)  that the 1978-1979 shift simulated by ECHAM5-wiso arises from the nudging to ERA-40 re-Table  2. Linear relationship between surface temperatures (in • C) from station instrumental records and ECHAM5-wiso outputs (in • C) over the period  1960-2013 and 1979-2013:  the slope (in • C • C -1 ), the correlation coefficient (noted as "r"), and the p value.
Comparison with GLACIOCLIM database accumulation For each grid cell in which at least one stake record is available, we have calculated the ratio of the P-E values (which we use as a surrogate for accumulation) simulated by ECHAM5wiso to the averaged SMB estimate for that grid region based on stake measurements (Fig. 4a ). Due to the limited number of grid cells containing SMB data points from 1979 to 2013 (100 cells) located almost only on the East Antarctic Ice Sheet, we have decided to use the dataset covering the entire twentieth century (521 cells) spread over the continent. The spatial distribution of SMB is well captured by ECHAM5-wiso, with decreasing SMB values from the coast to the interior plateau (Fig. 4a ). However, the model quantitatively shows some discrepancies when compared with the GC database.
For instance, the lowest value from the GC database is -164 mm w. e. yr -1 , measured at the Bahia del Diablo glacier, a small glacier covering important elevation ranges in a narrow spatial scale between the front and the summit. It was the only one within the corresponding model grid cell, so the resulting GC value within this grid cell could not be representative of the model scale, and the simulated P-E value is not representative of this small glacier-wide value. When considering the whole Antarctic grounded ice sheet, the area-weighted P-E simulated by the model amounts to 164. 4 mm w. e. yr -1. This value falls within the highest values of the 11 simulations displayed by  Monaghan et al. (2006) , varying from 84 to 188 mm w. e. yr -1. However, the high range of values between the different simulations illustrates the uncertainties related to the SMB model, mainly due to model resolution, which is crucial to reproducing the impact of topography on precipitations and to non-resolved physical processes (e.
Comparison with water stable isotope data Limited by the availability of the data, we could only study model skills with respect to spatio-temporal patterns, including seasonal and inter-annual variations, and the simulated relationships between δ 18 O and temperature. We have also extended the model-data comparison to the second-order parameter, d. Table  3. Comparison between measurements from precipitation samples and ECHAM5-wiso simulated precipitation isotopic composition for grid cells closest to the sampling locations over the same period as the data (at daily or monthly scale when the name of the station is associated with an asterisk). We report the mean value ± the standard deviation for δ 18 O (in ‰) and for temperature ( The model-data difference of the time-averaged values is positive for 88 % of all grid cells, suggesting a systematic underestimation of isotopic depletion by ECHAM5-wiso (Fig.
e. over the plateau. This finding is consistent with results from ice core records reported previously and consistent with the isotopic systematic biases. From this limited precipitation dataset, there is no systematic relationship between model biases for temperature (mean value or standard deviation) or for δ 18 O in contrast with the outcomes of the model-data comparison using the whole dataset, including surface snow. At Dome C, ECHAM5-wiso underestimates the standard deviation of temperature, but strongly overestimates the standard deviation of δ 18 O. As a conclusion, while δ 18 O time-averaged model-data biases are consistent with temperature biases using the whole dataset, no systematic relationships emerge between model biases for temperature and δ 18 O measured in precipitation.