dataesr/scientific-paragraphs-categorization huggingface dataset:
In parallel, the double-stranded cDNA obtained after the end repair step was used to prepare the MinION library with the PCR Barcoding kit (SQK-PBK004) (Oxford Nanopore Technologies. Oxford, Oxfordshire, UK) according to the manufacturer's instructions. The library was multiplexed with five other samples and loaded on a R9. 4 flow cell (FLO-MIN106) and sequenced on the MinION Mk1B device in 37 h. The raw sequencing data were collected using ONT MinKNOW software (version 19. 05. 0).
Bioinformatics analysis. The quality of the raw reads from the Illumina sequencing was first assessed, filtered, and then trimmed using CLC Genomics workbench v10. 0. 1 (Qiagen, Hilden, Germany). All trimmed reads were then de novo assembled using SPAdes v3. 10 to obtain two contigs corresponding to the S and L segments of an arenavirus genome. All reads were mapped back on the two contigs previously obtained using Geneious Prime (Biomatters, Auckland, New Zealand). All the reads from the Illumina sequencing that have been mapped were grouped together in a constituted dataset referred to as "AnRB-3214-Illumina". Three subsets, derived from the AnRB-3214-Illumina dataset, were constituted by collecting random reads from the raw fastq files in such a way that the first dataset represented 10% of the initial reads, the second 1%, and the last 0.
1%. In parallel, the raw data obtained from MinION sequencing were acquired in real-time in files containing 4000 sequences in FAST5 format. These files were base-called and demultiplexed using Guppy (version 3. 4. 1). As carried out for the Illumina raw reads, the MinION raw reads were mapped on the obtained sequences of the two segments using Minimap2  30. All the reads that have been mapped were grouped in a dataset referred to as "AnRB-3214-MinION". From this dataset, three sub-datasets were constituted in the same way as previously. Viral diversity was determined from the two above-described datasets (AnRB-3214-MinION and AnRB-3214-Illumina) and their three sub-datasets using three different taxonomic classification tools: Kaiju 31 , Centrifuge  32  , and Kraken2  33. These tools use reference databases in which the sequence of our AnRB-3214 strain was absent.
In parallel with the use of these three taxonomic classification tools, two other traditional approaches were used for the identification of viral reads. The first approach consisted in a homology search from reference sequences using the BLASTN tool whereas the second approach was to map the reads on other reference sequences using Minimap2. To use these approaches, the genomes of viruses closest to our AnRB3214 strain were screened for in GenBank and identified as Mobala virus (DQ328876, AY342390) and Gairo virus (KJ855308, KJ855307). Then, we screened the AnRB-3214-MinION and AnRB-3214-Illumina datasets against these Mobala and Gairo virus (formerly identified as Ippy) originate from the Ombella-M'poko prefecture. The maps were drawn in QGIS v3. 14. 16 (https:// www. qgis. org/ fr/ site/) using geopackages provided by GADM v3.
The node supports were estimated using 1000 bootstrap replicates. Concurrently, a molecular clock was tested on concatenated S and L segments using Bayesian inference. S + L concatenates of genomic sequences were aligned using MAFFT v7  36  and manually edited with AliView  37. Starting from this alignment, ML and Bayesian analyses were conducted. A global ML evolutionary tree was reconstructed using IQ-TREE  38  according to a GTR + F + I + Γ4 substitution model, recommended by the "ModelFinder" model fitter included IQ-TREE. Branch robustness was assessed using the ultrafast bootstrap approximation with 1000 bootstrap replicates  39. The resulting ML tree was visualized and edited in FigTree (https:// github. com/ ramba ut/ figtr ee/ relea ses/ tag/ v1. 4. 4). TempEst v1. 5 was used for investigating the temporal signal of our molecular phylogeny  40.
Results Bioinformatics analysis. De novo assembly of the raw reads from Illumina sequencing allowed us to obtain two sequences with lengths of 3390 nt and 7390 nt for the S and L fragments, respectively. Then, mapping back all the raw reads identified a total of 9,275,654 reads corresponding to the arenavirus. The mean depth was of 1902 × and 155 × for the S and L fragments, respectively. Mapping also made it possible to identify a total of 77,133 reads from MinION sequencing. After mapping MinION data on these two sequences, the mean coverage was 18 × and 3x. The taxonomic assignment using three different classification tools was performed only on the dataset that contained only reads corresponding to our virus. Taxonomic assignments showed that the percentage of reads from the AnRB-3214-Illumina dataset assigned as belonging to the Arenaviridae family ranged from 0.
02 to 0. 4% whereas for reads belonging to the AnRB-3214-MinION dataset, taxonomic assignment percentages were 0. 01% and 0. 88% for Centrifuge  32  and Kaiju 31 , respectively (Table  1 ). Moreover, the percentage of reads from the computer-diluted Illumina datasets assigned as belonging to the Arenaviridae family only varied from 0. 050 to 0. 070% and from 0. 020 to 0. 024% for Kraken2  33  and Centrifuge, respectively. The variation was also low with Kaiju (0. 45 to 0. 46%) except for the 0. 1% subset for which no reads were assigned. Excluding the 0. 1% subset in which no MinION reads could be classified, the percentages in the other datasets ranged from 0. 03 to 1. 04% for computer-diluted datasets from MinION sequencing (Table  1 ). In parallel to these three classification tools, two other tools were used to identify viral reads, a mapping tool (Minimap2  30  ) and a homology search tool (BLASTN) using the sequence of the closest variant available in the GenBank (Mobala virus).
From the AnRB-3214-Illumina dataset, a total of 26,726 reads (0. 29%) were identified with Minimap2, but only 2364 (0. 025%) were identified with BLASTN. Finally, although only 0. 45% of the reads were classified with Kaiju, almost 100% of both the S and L segments were covered. The second best coverage was obtained with Minimap2 with 95% (573 positions uncovered), but using either BLASTN or the other two classification tools (Kraken2 and Centrifuge), the coverage percentages were lower, varying between 17% (BLASTN) and 45% (Kraken2) and were concentrated on only certain regions of the different fragments (Fig. 2 ). Nucleotide sequences and the deduced amino acid sequence of AnRB3214 strain were compared with those of other OW arenaviruses species. The S and L segments of the AnRB3214 strain are phylogenetically very distant from the other described Ippy virus strain DakAnB188d  42, 43  , with respectively 64% and 56% nucleic acid similarity, evidence that they belong to two distinct virus species.
This feature was already observed at the nucleic acid level with the NP gene length of 1545 nt with the other arenavirus NP genes ranging from 1674 to 1779 nt. Phylogenetic trees were built for each segment and coding region using references available in GenBank. All the tree topologies confirmed the conclusions drawn from the pairwise comparison of the sequences. For example, strain AnRB3214 belongs to the cluster comprising the Mopeia, Morogoro, Luna, Gairo and Mobala viruses (Fig. 3 ). The AnRB3214 strain is genetically very divergent from the Ippy virus DakAnB188d strain, because they are found in two distant groups in the phylogenetic trees. Moreover, strain AnRB3214 shares a most recent common ancestor (MRCA) with Mobala virus. Molecular clock analysis shows that the two viruses diverged from one another around the year 1600 AD with a substitution rate of 4.
Discussion In this study, the AnRB3214 strain was sequenced on two sequencing platforms, Illumina and MinION, and we used a diverse set of taxonomic classification and mapping tools to retrieve arenavirus-specific reads from the obtained datasets. We obtained 77,133 and 9,275,654 reads specific to arenaviruses from the MinION and Illumina datasets, respectively. It is no surprise that we obtained less reads from MinION sequencing as this technology produces longer reads compared to Illumina. Additionally, the high amount of reads obtained during our experiment is representative of the type of sample that was used (here, cell culture supernatant) compared with direct sequencing of biological samples in which the viruses are present at low abundance with high levels of the host nucleic acid background  26, 46.
When using reference viral genome to recover arenaviruses reads in our AnRB3213 dataset, the best read identifications were obtained with Kaiju and Minimap2. Few, if any, studies have compared tools for the classification of reads, either from long reads (MinION) or short reads (Illumina) for the analysis of divergent viruses. Minimap was initially developed for long reads, but has been updated to work with smaller paired-end reads, through a genuine management of gaps  30. Furthermore, Minimap2 has been shown to be a good tool for the alignment of nanopore reads, with a higher speed than GraphMap although with comparable accuracy  47. Contrary to Kraken2 and Centrifuge which primarily classify nucleotides, Kaiju uses amino acid sequences instead of genomic sequences for metagenomic classification.
This feature allows Kaiju   Figure 3. Phylogenetic trees built on the large (A) and small (B) segments. The phylogenetic trees were built using the whole genome segment under the maximum-likelihood method and the GTR-I-Γ4 substitution model with the 1000 bootstrap replicates. The trees show two groups of arenaviruses, one from the Old World (OW) and the other from the New World (NW). AnRB3214 (in blue) forms a cluster with Mobala, which is supported by a bootstrap value of 100. The phylogenetic trees were built in MEGA X v10. 1. 1 (https:// www. megas oftwa re. net/). to overcome the limitations introduced by the redundancy of the genetic code and therefore make it suitable for investigating divergent organisms  31. For more insight on our dataset and sequencing approaches, randomly generated subsets corresponding to 10%, 1%, and 0.
Acknowledgements We acknowledge  Dr Carolyn Engel-Gautier  for editing the English in the manuscript. This study was supported by the  World Organisation for Animal health (OIE)  through the  European Union  (capacity building and surveillance for Ebola virus disease; EBO-SURSY, project reference:  FOOD/2016/379-660 ). This project was also supported by the  Chinese Academy of Sciences , a  Shanghai Municipal Science and Technology Major Project.
Recently,  Leyronas et al. (2018)  provide evidence that the arrival in a given area of airborne inoculum of Sclerotinia sclerotiorum from remote origins can be predicted using connectivity networks of air mass movements in the troposphere. In their approach, the directional connectivity between a particular pair of source and sink sites is estimated using archived meteorological data provided by the Global Data Assimilation System (GDAS) of NOAA and the software HYSPLIT that models air-mass trajectories. They also provide evidence that directional connectivity is more informative than the simple geographic distance. This study -as well as other similar studies based on long-distance dispersal of the wheat stem rust pathogen  (Meyer et al. 2017 ) -opens new avenues to understand the atmospheric highways of airborne pathogen dispersal and, from an applied perspective, new opportunities to set up surveillance networks  (Carvajal-Yepes et al.
Plateaus and rebounds of various epidemiological indicators are widely reported in Covid-19 pandemics studies but have not been explained so far. Here, we address this problem and explain the appearance of these patterns. We start with an empirical study of an original dataset obtained from highly precise measurements of SARS-CoV-2 concentration in wastewater over nine months in several treatment plants around the Thau lagoon in France. Among various features, we observe that the concentration displays plateaus at different dates in various locations but at the same level. In order to understand these facts, we introduce a new mathematical model that takes into account the heterogeneity and the natural variability of individual behaviours. Our model shows that the distribution of risky behaviours appears as the key ingredient for understanding the observed temporal patterns of epidemics.
Acknowledgements The authors are grateful to the municipalities surrounding the Thau lagoon and to the company " Ingénierie et Analyses en Génétique Environnementale " (I. A. G. E. ) for giving them access to the measurements they carried out and which are quoted in this publication. The authors are also thankful to  Jianhong Wu ,  Marc Barthélemy ,  Odo Diekmann  and  Jose Scheinkman  for useful discussions about this work.
Here, we follow the reasoning of Campbell et al. [18]  and use the term RRS  [19]  to refer to all of these methods, which are attractive because they make more frugal use of sequencing volume than whole genome sequencing. In RRS, one or several restriction endonuclease enzymes are used to first fragment the target genome into smaller portions, thus reducing sequencing costs. Millions of reads from high-throughput sequencing platforms are then aligned against either a reference genome or, alternatively, a de novo reference catalog of loci  [20, 21]. Subsequently, genetic variants, most commonly single nucleotide polymorphisms (SNPs) are determined. In addition, approaches have been developed to use RRS data to create microhaplotypes  [22, 23] , or identify microsatellites  [24]  or copy number variants (CNV).
g. aimed at using a NovaSeq platform can be obtained by adjusting the R code; see: https://github. com/notothen/radpilot). The setup for optimizing results as listed here also includes the consideration that it would be cost-efficient to use the same enzyme or enzyme combinations for several species whenever possible, because adaptors can then be reused. Therefore, when several enzymes (or combinations) seemed promising according to in silico digestion, we attempted to choose setups that were also promising in other target species. For ostracods, we assumed a genome size of 250 Mb and 500 Mb as worst-case scenario. 4 ).
The five-base recognition site of ApeKI features a degenerate base, which may have a similar effect. The methylation sensitivity of ApeKI may also provide more genomic markers in genic regions  [104]. It is unclear, however, how general this prediction holds across metazoans. Finally, some of the excess loci recovered may be artefacts from library preparation, PCR duplicates, or incorrect locus assembly  [21]. Rigorous downstream filtering and/or comparison of several, differently filtered datasets may help determine the true biological signal. Whatever the reason, the higher-thanexpected number of loci still lead to sufficient coverage, except in the bird library. The latter is likely related to low quality/quantity of input DNA. Few bird samples were available, some only as feathers, which yielded very little DNA.
08 tags per Mbp). Second, in silico estimations of how these targets can be reached and approximations of the associated costs should be obtained. The number of markers and individuals must be matched to reach a certain coverage (e. g. an average target of 30×). Subsequently, it is useful to briefly evaluate the trade-offs and benefits of RRS and other methods. If a promising combination of RRS method, enzyme, size selection, and sequencing effort is found, it is often worthwhile to conduct a pilot experiment before running the full sequencing experiment (Fig. 3 ). However, it is also advisable to stick to one approach afterwards and not change for example the sequencing platform, the size window or other properties of the setup that will otherwise reduce comparability between datasets. Finally, it is also important to thoroughly test and optimize the bioinformatic processing and data filtering to obtain a robust population genomic dataset  [21, 113].
Alternative approaches In some cases, RRS might not be the right choice for molecular ecological research (Fig. 3 ). A plethora of other genomic or genetic methods exists, which may offer more appropriate cost-benefit ratios. SNP genotyping arrays are a common and highly reproducible alternative, but usually only for species with more genomic resources (which exist for some Antarctic taxa; see e. g. [114] ). Similarly, whole genome resequencing is providing the most extensive datasets which can be used for a wide range of analyses  [11, 12, 115]. However, this is still too costly for many research projects, especially if information across many individuals and/or localities is needed. Another option is to focus on the expressed part of the genome and use a form of sequence capture enrichment (e.
Genomic resources Prior to computational analyses, genomic information was collated for all target species or, if such information was not available, from the closest related species. Published reference genomes were collected from the literature and online resources, such as GenBank and Ensembl  [131]. In addition, genome size estimates were retrieved from genomesize. com  [132]  and other published estimates based on flow cytometry (e. g. [69] ). Genome size estimates as C values were transformed to Mb for comparison (1 pg = 978 Mb)  [133].
Reactions were purified with CleanPCR (GC Biotech) according to the manufacturer's protocol. Between 1 and 5 ng of the purified digested DNA was loaded on a High Sensitivity DNA chip (Agilent Technologies) and run on an Agilent 2100 Bioanalyzer System. Results were exported from the 2100 expert software (Agilent) as XML files and read into R v4. 0. 4  [135]  using the bioanalyzeR package v0. 5. 1  [136]. Additional R packages used in this project were here v1. 0. 1  [137] , seqinR v1. 0-2  [138] , the tidyverse packages  [139] , ggsci v2. 9  [140] , and gridExtra v2. 3  [141]  (see also more details under: https://github. com/notothen/radpilot). Because it is not possible to accurately standardize the number of fragments in an empirical digest without knowledge of the true genome size, we compared the shape of the curves of produced fragments (number of loci or DNA concentration vs.
The latter estimate is based on our own experience, predominantly from fish genomes (but see also e. g. [142] ). If an estimate of the naturally occurring SNP density across the genome is known for the target species or a related species, then this should be used. We provide an R function where any estimate can be used as input for marker density calculations. In general, all our calculations and plots should be reproducible with our spreadsheet tables and R scripts available at https://doi. org/10. 5281/zenodo. 5045574 and at https://github. com/ notothen/radpilot.
Sequence analyses Sequencing data were checked using FastQC v0. 11. 5  [143]  and then demultiplexed and cleaned (options -c and -q) using the process_radtags module of Stacks v2. 4  [98, 99]. Because some of our multiplexing barcodes for the PstI library were contained in longer ApeKI barcodes, we demultiplexed the ApeKI libraries first and captured reads that were discarded in the process. These reads were subsequently used for demultiplexing of the PstI library. All demultiplexing runs were conducted without barcode rescue to avoid cross-contamination between libraries. The Stacks pipeline was also used for each target species independently to create a de novo assembly and call genotypes. Building contigs from pairedend reads is not possible with GBS data in Stacks  [21] , because the orientation of the reads is ambiguous.
Acknowledgements We are very grateful to everyone that contributed to field work and provided samples. In particular, we thank  S. Brandão  for ostracod DNA and  H. Griffiths  and  K. Linse  ( BAS ),  C. Held  ( AWI ),  S. Mills  and  D. Macpherson  ( NIWA ),  H. Baird  and  G. Johnstone  ( AAD ), and the  MNHN in Paris  for amphipod samples. We thank  M. Clark  ( BAS ) and the scientific staff involved for the generous collection of bivalve samples from Rothera station. We thank the captains, crew and science leaders who contributed to the collection of sea stars from  RRS James Clark Ross  ( JR15005 ) and  RV Polarstern  ( PS77 ,  PS81 ). We thank the scientific team of  IPEV  ( Institut Polaire Français Paul-Emile Victor) program 1124 REVOLTA (Radiation EVOLutives en Terre Adélie ; chief scientists  M.
Funding This is contribution no. 35  of the vERSO project, funded by the  Belgian Science Policy Office (BELSPO , Contract no. BR/132/A1/vERSO ) and contribution no. 8 of the  RECTO  project (BELSPO, Contract no. BR/154/A1/ RECTO ). TP thanks  BELSPO  for funding JPIO Mining Impact. Research was also supported by the  Scientific Research Network  " Eco-evolutionary dynamics in natural and anthropogenic communities " (grant  W0. 037. 10N ), and the  European Marine Biological Resource Center (EMBRC) Belgium , both funded by the  Research Foundation -Flanders (FWO). The first author was supported by an individual grant from the  former Flemish Agency for Innovation by Science and Technology , now managed through  Flanders Innovation & Entrepreneurship (VLAIO , grant no. 141328 ). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image.
In 1978, Clark and Moffatt suggested to remove interference fringes by manually fitting sine waves, by changing the frequency, amplitude, phase and offset, until the fringes were minimized  [20]. This method is tedious and inapplicable to large datasets where the parameters vary between spectra. Iwata et al. suggested in 1994 a method for removing noise from spectra by use of a singular value decomposition (SVD) on a cyclic matrix constructed from the interferogram  [21]. This method can be used to remove interference fringes, however, it requires the fringe signal to be of relatively high frequency and low amplitude, such that it resembles noise. Further, the method is inapplicable to absorbance spectra from biological samples, as it fails when several absorbance bands are present in one spectrum.
In EMSC, the amplitude and constant offset are estimated as well. The method, we call it Fringe EMSC, was revised in 2018 by Azafar et al. , with the suggestion to make the fringe removal an iterative process, as well as including sine waves of multiple frequencies as model functions in the EMSC  [16]. The EMSC based method has the advantage that it can correct many spectra in a fast and robust manner. This is one of the reasons why EMSC based methods are often preferred to iterative methods. Here, we propose a robust and automated approach for precise estimation fringe frequencies, which is needed as an input for the EMSC correction, and provide it as an open source code for the infrared community. The code is available at the BioSpec Norway GitHub page, as a part of the biospectools toolbox (https://github.