dataesr/scientific-paragraphs-categorization huggingface dataset:
To resolve this issue, biomonitoR comes with two approaches. The first option aggregates the abundance of taxa with the same traits (zerodist_rm). The second option (add_bias_to_traits) differentiates taxa with identical trait profiles by adding a small amount (random bias) to the traits of each taxon (see R code and examples in SM3). Overall, both taxonomic-based indices and more novel measures still pending to be fully incorporated in national or international biomonitoring schemes are available in the package. biomonitoR currently allows the calculation of more than 30 indices (including diversity, biomonitoring, and functional measures) as well as a wide range of complementary taxonomic measures (e. g. , richness or abundance of a specific taxon, pair and combination of taxa, see Table  2  and examples of applications in SM2).
Data visualization biomonitoR relies on the plotly package for providing flexible and high-quality graphs. Three types of plots are currently available to visualize the data. The first is a Sankey diagram that links indicator taxa  (De Cáceres & Legendre, 2009)  from a desired to an upper taxonomic level through groups provided by the user or identified by a cluster analysis (e. g. , Fig. 2 ). The second plots the prevalence of a taxonomic level (e. g. , family) within an upper taxonomic level (e. g. , order, see SM4a-c). The third is an interactive barplot reporting the proportion of taxa within a desired taxonomic level, with sites ordered according to the results of a cluster analysis (see examples and codes in SM4d).
Package installation and basic usage example The biomonitoR package (version 0. 9. 3) is available on GitHub (https://github. com/ alexology/biomonitoR). The package can be installed as follows: a These indices can be calculated using the composite family approach that aggregates families having similar ecological requirements. Moreover, biomonitoR provides, by default, four sets of sensitivity scores for ASPT and BMWP (two developed UK, one in Italy and one in Spain) and two sets of sensitivity scores for LIFE (developed in UK). An R Shiny implementation  (Chang et al. , 2021) , as interactive app of the package, is available from the R console running the command line: library(shiny) runGitHub("biomonitoR_app", "TommasoCanc", ref = "main", subdir = "biomonitoR_app") as well as in the webpage (https://tcanceco.
Usage example # Load the package library(biomonitoR) # Load one of the built-in macroinvertebrate datasets (mi_prin) data(mi_prin) # Details about the dataset ?mi_prin # Import data in the biomonitoR format data_asb <-as_biomonitor(mi_prin, group = "mi") # Inspect the overall community structure by plotting the object plot(data_asb) # Aggregate taxa at different taxonomic levels data_agg <-aggregate_taxa(data_asb) # Calculate a taxonomic index # e. g. , Shannon index at taxa and family level shannon(data_agg, tax_lev = "Taxa") shannon(data_agg, tax_lev = "Family") # Calculate a biomonitoring index # e. g. , ASPT index with function aspt (see ?aspt for details) aspt(data_agg) Detailed scripts for running further analyses (e. g. , data manipulation, biomonitoring indices calculations, function customization, plots) are provided in SM2-3-4.
Limitations and perspectives Despite its flexibility, biomonitoR may presents some punctual limitations. The reference dataset needs to be implemented according to the taxonomic levels that are currently available in the package. This can be an issue in the occasional cases where species-groups, life stage information or operational taxonomic units are needed. With minor adjustments (e. g. , considering species groups as species) their use is still possible with the current implementation. Moreover, according to Tables 1-2, one of the main biomonitoR assets (i. e. , multiple index calculation) is biased towards the biomonitoring of running waters. This is due to the unparalleled pressures affecting freshwater biodiversity worldwide and the long history of biological monitoring in rivers, that date back to the beginning of the 20th century  (Bonada et al.
, 2006). Nonetheless, a detailed biomonitoring toolbox, not constrained at local scale, is still missing (see Table  1 ) and biomonitoR aspires to fill this gap. It should be noted that, despite the primary focus at European level, the package can potentially achieve a more international perspective after further customizations. Most of the river biomonitoring metrics tested in Africa, South-Central America, Mexico, Australia, and Asia are, in fact, directly related to the Biological Monitoring Working Party scoring systems (BMWP/ASPT,  Aschalew & Moog, 2015; Eriksen et al. , 2021; Feio et al. , 2021)  originally developed in the UK and already available in biomonitoR (Table  2 ). Regardless of the primary freshwater focus, the package can instantly manage biological data from other domains (e.
CONCLUSIONS Rigorous and updated biomonitoring procedures and tools are crucial for biodiversity conservation research (e. g. , early warning, long-term assessment, and observatories) and to properly inform science-based policies. Here we presented biomonitoR, a new R tool that creates the basis for a common environment for dataset management that allows users to be in an advantageous position in research and applied fields related to environmental science. The set of functions, available in a single open access package, support task automation and greatly facilitate repeatability and reproducibility among studies. Therefore, biomonitoR may also ensure greater consistency among biomonitoring programs which could lead to more far-reaching analyses at spatial and temporal scales. The package represents a flexible toolbox for (i) checking taxonomic, sensitivity and traits information against a reference taxonomic database; (ii) managing heterogeneous taxonomic resolution and computing indices at multiple taxonomic levels; (iii) calculating metrics and biological indices for both general and stressor-specific ecological assessment; (iv) interpreting results through interactive data visualization; (v) working with a userfriendly web application directly available in R.
ACKNOWLEDGEMENTS This package is based upon work from  COST Action CA15113 (SMIRES, Science and Management of Intermittent Rivers and Ephemeral Streams , http://www. smires. eu). Authors would like to thank the  Alpine Stream Research Centre (ALPSTREAM, Ostana, Italy) ,  Roberto Mattioli  for his help in preparing Fig. 1 and  Anna-Maria Sourelli  for English grammar check.
Data Availability The following information was supplied regarding data availability: The code and data are available on GitHub at https://github. com/alexology/biomonitoR. It is also available at Zenodo: Laini, A. , Guareschi, S. , Bolpagni, R. , Burgazzi, G. , Bruno, D. , Gutiérrez-Cánovas, C. , Miranda, R. , Mondy, C. , Várbíró, G. , & Cancellario, T. (2022). biomonitoR: an R package for managing ecological data and calculating biomonitoring indices. (v0. 9. 3). Zenodo https://doi. org/10. 5281/zenodo. 7074237. The examples are available in the Supplemental Files.
decision-making processes Figure 1 Example of workflow in environmental and biomonitoring science (from field work, data manipulation to results dissemination • Aim: Scientific story telling • Reproducible outputs • Challenges: • Engaging results Handling heterogeneous dissemination information/datasets b io monito R • Effective • Field work data ✓ Reference databases • Long-term datasets ✓ Data carpentry and visualization ✓ Multi-index calculations ✓ Updated metrics ). biomonitoR represents the toolbox that supports biomonitoring tasks and fluently connects the different phases. Full-size DOI: 10. 7717/peerj. 14183/fig-1different taxonomic levels according to the user's data resolution. The obtained object can be used to calculate all the indices implemented in biomonitoR at the desired taxonomic resolution.
Regions in Europe? (ii) Do the high-resolution results of precipitation in the mountainous regions (e. g. , the Alps, the Scandes and the Scottish Highlands) produce Holocene climate patterns that compare more favorably to proxy data when compared with the low-resolution results? (iii) What is the advantage of using cheap numerically dynamical downscaling for paleoclimate research? iLOVECLIM (hereafter version 1. 0)  (Roche et al. , 2014)  is a three-dimensional model, with a simplified representation of the atmosphere relative to GCMs. This simplification and its lower spatial resolution make iLOVECLIM much faster than coupled GCMs  (Goosse et al. , 2010; Kitover et al. , 2015). It is a fork of the LOVECLIM 1. 2 model code  (Goosse et al. , 2010) , and it has the main climate system components in common.
Acknowledgement The research is financed through the  European Union 's  Horizon 2020 research and innovation programme  within the  TERRANOVA  project, No  813904. The paper reflects the views only of the authors, and the  European Union  cannot be held responsible for any use which may be made of the information contained therein. Thanks to  Marie Jose Gaillard  who provided us with her input in setting up this work. Another thanks to  Heikki Seppa  and  Basil Davis , who assisted us with proxy-based reconstructions.
Code Availability The iLOVECLIM source code is accessible at http://www. elic. ucl. ac. be/modx/elic/index. php?id=289 (UCL, 2021). The developments on the iLOVECLIM source code are hosted at http: //forge. ipsl. jussieu. fr/ludus (IPSL, 2021), due to copyright restrictions they cannot be publicly accessed. Request for access can be made by contacting D. M. Roche (didier. roche@lsce. ipsl. fr). For this study, we used the model at revision 1147.
(2012)  based on plasma properties obtained from grating spectra taken with Chandra and XMM-Newton. The latest hydrodynamic simulations were presented by  Orlando et al. (2020) , who compared different progenitor scenarios and predicted the evolution and dynamical and radiative properties of the remnant of SN 1987A. Recent observations have also been analysed in a search for indications of non-thermal emission in the Chandra + NuSTAR spectrum. Greco et al. (2021)  have analysed Chandra and NuS-TAR data and concluded that the data might indicate the presence of a pulsar wind nebula. However, the possible non-thermal emission might also be emission from particles accelerated in the shocks. The non-thermal emission component, however, was not confirmed by  Alp et al. (2021)  in the analysis of XMM-Newton and NuSTAR data.
Observational data We have monitored SN 1987A regularly since 2007 with XMM-Newton about once a year. The most recent observations were performed in November 2020. An additional observation to allow cross-calibration studies between XMM-Newton and eROSITA was performed in September 2019, six weeks before the first-light observation of eROSITA. Results from the monitoring and older archival XMM-Newton observations of SN 1987A are presented in  Haberl et al. (2006) ;  Heng et al. (2008) ;  Sturm et al. (2010 ), and Maggi et al. (2012 , 2016). Maggi et al. followed the evolution of SN 1987A until December 2012. The details of all XMM-Newton observations are summarized in Table  1. Only observations with EPIC-pn in full-frame mode were used for this work. We did not use Obsid 0831810101 from 2019 November 27 because the observation was performed with EPIC-pn in large-window mode, which might introduce systematic flux differences.
Data reduction and spectral analysis 3. 1. XMM-Newton XMM-Newton /EPIC (see  Strüder et al. 2001; Turner et al. 2001 , for pn and MOS, respectively) data were processed using the latest XMM-Newton data analysis software SAS, version 18. 0. 0 1. The observations were inspected for high background flaring activity by extracting the high-energy light curves (7. 0 keV < E < 15 keV for EPIC-pn) with a bin size of 100 s. Event extraction was performed with the SAS task evselect using the standard filtering flags (#XMMEA_EP) for EPIC-pn. We extracted the spectra from the EPIC-pn data of all observations since 2007 (Table  1 ) in a consistent way (common to all observations) with the same extraction regions around source (circle with 30 radius) and background (nearby circle with same radius) and using single-pixel events (PATTERN = 0), which have the best energy resolution.
eROSITA The data were reduced with a pipeline based on the eROSITA Standard Analysis Software System (eSASS,  Brunner et al. 2022). We used data of the processing configuration c001, which determines good time intervals, corrupted events and frames, and dead times, masks bad pixels, and applies pattern recognition and energy calibration. Finally, star-tracker and gyro data were used to assign celestial coordinates to each reconstructed X-ray photon. Circular regions with a radius of 75 and 100 were used for the source and background extraction, respectively. We rebinned the spectra to a minimum of 20 counts per bin in order to allow the use of the χ 2 -statistic. We extracted eROSITA spectra from SN 1987A from the available TMs from the calibrated event files using srctool with standard parameters suitable for point-source extraction.
The background region was selected to be consistent with that chosen for the spectral analysis of the XMM-Newton EPIC-pn spectra obtained from our monitoring program since 2007 (see Table  1 ). For EPIC-pn,   2  for details. The spectra have been rebinned for visual clarity. we selected single-pixel events (PAT = 1), which provide the best energy resolution. However, given the smaller pixel size of the eROSITA CCDs, the fraction of single-pixel events is reduced, and we therefore also created spectra using all valid pixelpatterns (PAT = 15) to use maximum statistics. For the spectral analysis, we used only TM1, 2, 3, 4, and 6, which are equipped with cameras with on-chip optical blocking filters. We also investigated the possible temporal evolution of the post-shock temperatures of the cooler and hot plasma components, their normalisations and the upper limit of the ionisation timescales of the warm component (left free in the fit, as described in Sect.
Elemental abundances The elemental abundances of the different plasma components were studied by setting the abundances of the warm and hot plasma component free with respect to the cooler component. The simultaneous fit from EPIC-pn favours a higher abundance for the warm and hot components (especially the hot component and for the elements O, Ne, Mg, and Si). A time evolution of the elemental abundance was also explored by dividing the data into two groups: before the X-ray emission was dominated by the shocked plasma from the dense equatorial ring  (2007-2015), and after (2016-2020). The data show evidence of a lower abundance of N at the later epoch, together with a marginal increase in the abundance of Ne and Mg. No other clear trend can be seen. However, we note that the elemental abundances determined in epoch 2016-2020 are time-averaged over the three plasma components and the contribution of the hot plasma (which is indicated to have the highest abundances) became dominant in the observation of the latest epoch 12 329 days, as seen in Fig.
4. Bray et al. (2020)  obtained elemental abundances of Ne = 0. 4, Mg = 0. 34, Si = 0. 4, S = 0. 34, and Fe = 0. 16 from Chandra HETG/LEG. The latest XMM-Newton study by  Sun et al. (2021)  yields slightly higher abundances and also indicates that the elemental abundances have decreased in the latest epochs. While the analysis of  Bray et al. (2020)  was limited to softer energies below 3. 0 keV,  Sun et al. (2021)  analysed the RGS and EPIC data together, but assumed a model consisting of two thermal emission components. Therefore these studies miss the emission from the hottest component, which is likely caused by the reverse shock propagating into the ejecta. This is the component for which we therefore expect higher element abundances. Future analysis including the grating spectra will be important to confirm these results.
Acknowledgements. We thank the referee for a very thorough and critical reviewing of the manuscript which helped in considerably improving the clarity of the paper. This work is based on data from eROSITA , the primary instrument aboard SRG, a joint  Russian-German  science mission supported by the  Russian Space Agency (Roskosmos) , in the interests of the  Russian Academy of Sciences  represented by its  Space Research Institute (IKI) , and the  Deutsches Zentrum für Luft-und Raumfahrt (DLR). The SRG spacecraft was built by  Lavochkin Association (NPOL)  and its subcontractors, and is operated by NPOL with support from the  Max Planck Institute for Extraterrestrial Physics (MPE). The development and construction of the eROSITA X-ray instrument was led by  MPE , with contributions from the  Dr.
Genome Analysis After using the Nanopore technology combined with the ARTIC protocol, fastq files were processed using the ARTIC field bioinformatics pipeline (ARTIC-nCoV-bioinformatics-SOP-v1. 1. 0, Nick Loman, Will Rowe, Andrew Rambaut, the ZiBRA Project and the AR-TIC project, University of Birmingham, UK, https://artic. network/ncov-2019/ncov2 019-bioinformatics-sop. html; https://github. com/artic-network/fieldbioinformatics; accessed on 18 May 2022), as previously described  [14, 27]. Next-generation sequencing reads were base-called using Guppy (4. 0. 14) and were aligned to the genome of the Wuhan-Hu-1isolate, GenBank accession No. MN908947. 3, using minimap2 (v2. 17-r941) (https://github. com/lh3/minimap2; accessed on 18 May 2022, Dana-Farber Cancer Institute of Boston, USA)  [44].
The ARTIC tool align_trim was used to softmask primers from read alignment and cap sequencing depth at a maximum of 400-fold coverage. Consensus-level variant candidates were identified using a threshold of 70% and the Medaka (v. 0. 11. 5) workflow, developed by ARTIC (Nick Loman, Will Rowe, Andrew Rambaut, the ZiBRA Project and the ARTIC project, University of Birmingham, UK, https: //github. com/artic-network/artic-ncov2019; accessed on 18 May 2022). From the unique sequence obtained with the ARTIC-Nanopore technology, a sorted bam file was loaded on the CLC Genomics workbench v7 software and a tsv file was then exported. NovaSeq reads were basecalled using the Dragen Bcl Convert pipeline (v3. 9. 3; https://emea. support. illumina. com/sequencing/sequencing_software/bcl-convert. html; accessed on 18 May 2022 (Illumina Inc.
, San Diego, CA, USA). Mapping was carried out on the Wuhan-Hu-1 isolate genome with the bwa-mem2 tool (v2. 2. 1; https://github. com/bwa-mem2/bwa-mem2; accessed on 18 May 2022, Heng Li, Harvard, USA) and was cleaned with Samtools (v1. 13; https://www. htslib. org/; accessed on 18 May 2022)  [45]. Variant calling was performed with freebayes (v1. 3. 5; https://github. com/freebayes/freebayes; accessed on 18 May 2022, Erik Garrison, Gabor Marth, University of Tennessee and Utah)  [46]  and consensus genomes were built with Bcftools (v1. 13; https://samtools. github. io/bcftools/bcftools. html; accessed on 18 May 2022, 2012-2021 Genome Research Ltd. ). Freebayes results were filtered with a threshold of 70% for the majority nucleotide. A tsv file was generated using an in-house Python script.
The clade was designated at the consensus level with the Nextclade online tool (https://clades. nextstrain. org/; accessed on 18 May 2022, 2020 Nextstrain developers, Swiss Institute of Bioinformatics)  [32, 47]  and an in-house Python script allowed for the detection of variants and the hybrids of variants. At the sub-consensus level, variant frequencies that were compared to the Wuhan-Hu-1 isolate genome were calculated from tsv files. Nucleotide diversity at genomic positions was calculated using the Microsoft Excel software (https://www. microsoft. com/en-us/microsoft-365/excel; accessed on 18 May 2022, Microsoft Corporation, Redmond, WA, USA) with an in-house built file. It corresponded to the proportion of sequence reads that do not harbor the consensus (majority) nucleotide. Genome sequences obtained in the present study were submitted to the GISAID sequence database (https://www.
Generation of Additional Sequence Reads Sequencing of reverse-transcription-PCR-targeted regions: extraction of the RNA samples was carried out using the EZ1 Virus kit with the EZ1 Advanced XL instrument (Qiagen) following the manufacturer's recommendations. PCR amplification of the 3 regions was amplified in a 25 µL total volume using the SuperScript III One-Step RT-PCR Kit (Invitrogen, Carlsbad, CA, USA), using primer concentrations of 200 nM per reaction. PCR were performed with following conditions: 50   [45]  combined with an in-house awk script. Reads were then filtered according to variant-specific nucleotide patterns using SAMtools combined with an in-house awk script. Groups of reads with same patterns of mutations were then visualized using the IGV software (https://software. broadinstitute.
Phylogenetic Analysis Based on Whole and Partial Genome Sequences Phylogenetic analyses were performed separately for the twelve genome sequences and the twelve spike gene sequences obtained from the nasopharyngeal samples or the culture supernatants. Sequences were aligned using MAFFT v. 7 (Kazutaka Katoh and Daron M Standley, Osaka University, Japan)  [49]  with their 20 most similar hits identified with the BLAST tool (National Library of Medicine, Rockville Pike, MD, USA)  [50]  among SARS-CoV-2 genomes from our database, which contains sequences obtained from clinical samples collected between February 2020 and February 2022  [14, 27]. Phylogeny reconstruction was performed using the IQ-TREE software with the GTR Model and 1000 ultrafast bootstrap repetitions (http://www. iqtree. org, Free Software Foundation, Boston, MA, USA) [51], and trees were visualized with iTOL (Interactive Tree of Life) (https://itol.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/v14061266/s1. Additional Methods, Results, Figure  S1   S1 : List of GISAID identifiers for sequences used in the present study, and Table  S2 : Sampling time and location for sequences used in the present study, Table  S3 : Identifiers of raw data deposited in the European Bioinformatics Institute (EMBL-EBI) sequence database (https://www. ebi. ac. uk/; accessed on 18 May 2022), and References, are provided in the Supplementary Material.
[34]r sequences from our SARS-CoV-2 sequence database are indicated by a blue font when classified as of the B. 1. 160variant,and by a yellow font when classified as of the Alpha variant. Sequences are labeled with their GISAID (https://www. gisaid. org/;accessed on 18 May 2022)[34]identifiers. Trees are rooted with the genome of the Wuhan-Hu-1 isolate GenBank accession no. NC_045512. 2. 2. 3. Genome of the initial virus.
• C for 25 min, 95 • C for 2 min, then 40 cycles including 15 s at 95 • C, 45 s at 60 • C, and 2 min at 68 • C. Sequences of PCR primers are provided in the Supplementary Material. Amplicons were sequenced with Nanopore technology on a GridION instrument (Oxford Nanopore Technologies Ltd. , Oxford, UK), following the manufacturer's instructions. Fastq files were processed as described above. Continuous reads overlapping signature mutations of distinct variants were filtered using SAMtools (v1. 13; https://github. com/samtools/2008-2022 Genome Research Ltd. , accessed on 18 May 2022).
Acknowledgments: We are thankful to  Ludivine Brechard ,  Claudia Andrieu ,  Raphael Tola ,  Jeremy Delerce  for their technical help; to  Sophie Amrane  and  Justine Raclot  for their help with the clinical management; and to the  medical biology laboratory Alphabio, hôpital Européen, Marseille, France , for providing some results of SARS-CoV-2 qPCR testing.