dataesr/scientific-paragraphs-categorization huggingface dataset:
These cultures were then subcultured 1/10,000 into fresh LB media and incubated in a final volume of 100 µl at 37°C with shaking in 96-well round-bottom microtiter plates using a SpectraMax M3 plate reader. Optical density at 600 nm was measured every ten minutes over 24 hours in biological triplicate. The mean of the triplicate growth curve data is shown. DNA damage analysis using TUNEL. Cells were grown from frozen stocks in 3 ml of liquid LB media supplemented with 50 µg/ml kanamycin overnight at 37°C shaking at 220 rpm. These cultures were then subcultured 1/10,000 into 25 ml fresh LB in a 250 ml baffled flask and grown at 37°C shaking at 220 rpm. Cells were harvested at OD~0. 1, or after 24 hours of incubation, for exponential phase and stationary phase analyses, respectively. After the required incubation period, 1 ml of cells were transferred to a 14 ml conical vial and treated with 100 ng/ml ciprofloxacin for one hour (exponential phase) or 1 µg/ml ciprofloxacin for 24 hours (stationary phase).
Acknowledgements We would like to thank  Deborah Hung  from the  Broad Institute  for providing the bacterial pathogens used in this study. This work was supported by grants to J. J. C. from the  Defence Threat Reduction Agency  (grant number  HDTRA1-15-1-0051 ), and the  Broad Institute of MIT and Harvard ; by a grant to J. M. S. and J. J. C. from the  Bill and Melinda Gates Foundation  (grant number  OPP1182991 ); by grants to E. D. B. from the  Canadian Institutes of Health Research  ( FRN 143215 ), the  Canadian Foundation for Innovation , and by the  Canada Research Chairs Program  (Tier 1); by a fellowship to J. M. S. from the  Banting Postdoctoral Fellowships Program ; by a fellowship from the  National Science Foundation Graduate Research Fellowship Program  to I. W. A. , and by a generous gift from Anita and  Josh Bekenstein.
Non-enriched MiSeq data To estimate enrichment rate, we used single-sample non-enriched library data sets originating from various Illumina MiSeq sequencing runs for O. sativa, O. glaberrima, C. americanus, and P. aequatorialis. We used MiSeq data here because its higher yield (compared to MinION) allows a better estimation, at a lower cost, of the percentage of plastid reads of a non-enriched library. For D. exilis, P. acaulis, and R. textilis, we merged 10, two, and 16 samples, respectively, of non-enriched libraries to provide adequate read counts. Forward sequencing read outputs from each MiSeq run (i. e. , R1 files) were first demultiplexed using the demultadapt script (https://github. com/Maillol/demultadapt) to sort reads according to a given barcode list. Adapters at the beginning of each read from the R2 and demultiplexed R1 files were removed using cutadapt-1.
2. 1 software  (Martin, 2011)  with the default parameters. Reads were then filtered by length (size >35 bp) and mean quality values (Q > 30) before being paired using compare_fastq_paired_v5. pl (https://github. com/SouthGreenPlatform/arcad-hts/blob/master/ scripts/arcad_hts_3_synchronized_paired_fastq. pl and https://github. com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_ hts_2_Filter_Fastq_On_Mean_Quality. pl). A final trimming step using the fastx-trimmer command from the FASTX-Toolkit (http:// hannonlab. cshl. edu/fastx_toolkit/) was undertaken onto the R2 paired files to remove the last six bases of each read to ensure removal of any possible barcode present on short reads.
Bioinformatics All command lines are available in Appendix S4. Using the MinION Fast5 output format, base-calling and demultiplexing were undertaken using Albacore v2. 5. 11 (https://github. com/Albacore/albacore). This generated a FASTQ file from which reads were filtered out. The average quality score was lower than 7. For each barcode, a quality control using the MinionQC R script (https://github. com/roblanf/ minion_qc) was performed to check for read mean length and quality scores. Reads were then trimmed using Porechop (https://github. com/rrwick/Porechop) in order to remove the sequencing adapters and barcodes. The only non-default setting is that splitting reads containing adapters in the middle was disabled, in order to avoid issues during the polishing step using Nanopolish (see below).
For each library, the percentage of plastome-associated reads was estimated by mapping reads to a reference plastid genome using the Burrows-Wheeler alignment tool (BWA-MEM, https://github. com/lh3/bwa) with the "-B 1" option for non-enriched short-read data and the "-x ont2d" option for long-read data  (Li and Durbin, 2009). We then calculated the X-fold enrichment to evaluate capture efficiency (the ratio of plastome reads obtained with capture relative to plastome reads obtained without capture). Coverage and depth values were calculated using Bedtools  (Quinlan and Hall, 2010)  genomecov (https://github. com/arq5x/bedtools2). Mismatch percentage values between mapped reads and references were recovered using Tablet version 1. 17. 08. 17  (Milne et al. , 2010).
The draft assemblies were then polished using Nanopolish version 0. 9. 1 (https://github. com/jts/nanopolish), using minimap2 on the "map-ont" preset for the overlapping step. Finally, the assemblies were mapped on the reference sequence of each species using the dnadiff tool of MUMmer version 4. 0beta2  (Kurtz et al. , 2004) , which directly provides alignment coordinates and global statistics such as the mean identity percentage of alignments. Although read length is an important consideration, the uniformity of reference coverage by the reads can also affect plastome assembly. This is especially problematic for low-molecular-weight DNA extractions resulting in shorter read lengths on average (as happened in our study comparing extractions from silica gel-dried leaves vs. from fresh tissue).
Plastome enrichment protocol validation on Oryza sativa All raw reads for the seven species are available from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (BioProject number PRJNA526996). After read filtering (Q > 7), the median length of the 12,227 mapped plastome reads was 4264 bp (Table  1 , Fig. 2B ). We recovered the whole plastome with an average coverage depth of 364× for the enriched MinION library, with a standard deviation increasing from 0. 25 to 0. 37 between enriched and non-enriched libraries (Fig. 2B, C ; Appendix 1). The average mismatch was 11. 80% (Appendix 1), and 70. 8% of the reads mapped to the reference plastome (Fig. 2D , Appendix 1), representing an approximately fivefold increase in plastome reads when compared to the non-enriched MiSeq sequenced library (13.
ACKNOWLEDGMENTS This work was publicly funded through  ANR  (the  French National Research agency)  under the " Investissement d' Avenir" program  (reference  ANR-10-LABX-001-01   Labex Agro)  coordinated by the  Agropolis Fondation. The authors thank  Oliver Lucas  from  Oxford Nanopore Technologies and Christian-Julian Villabona-Arenas  for help and advice. The exportation and use of the Phytelephas aequatorialis sample was authorized by the  Ministerio del Ambiente of Ecuador  (permit  MAE-DNB-CM_2018-0082 ); the exportation and use of the Podococcus barteri and P. acaulis samples was authorized by  CENAREST of Gabon  (permits  AR0020/16 ,  AR0036/15 ). We thank  Dr. Ya Yang , two anonymous reviewers, and  Beth Parada  (managing editor, Applications in Plant Sciences) for comments that improved the manuscript.
Holoboreaphilus nordenskioldi is Holarctic, but only extends westwards as far as the Kanin Peninsula in Arctic Russia and is restricted to the tundra  [86]. For the Nearctic, Morgan et al. [87]  suggested an association with heavily disturbed ground over permafrost. Using habitat traits from the classification in the BugsCEP database  [88] , we infer that the more extensive fauna from sample 2 indicates a varied landscape with more or less equal indications of water, meadowland, wetland/marshes and heathland/moorland. Both standing and running water are indicated, the former more robustly than the latter (figure  12 ). There are few indications of the specific nature of the flora from the beetles. The weevils Otiorhynchus arcticus, O. nodosus, O. rugifrons and Tropiphorus obtusus are essentially polyphagous on low vegetation, the larvae feeding on the roots.
insularis as endemic to the British Isles, but this cannot be separated from A. sabuleti on the fossil material. While A. sabuleti (s. l. ) is a not infrequent Quaternary fossil  [88] , Aphodius borealis has only two other British fossil records, from MIS 3 deposits at Tattershall Castle, Lincolnshire  [80] , and from a Roman site in East Yorkshire (P. C. B. , unpublished data, 2015, Barmby-on-the-Marsh, available in the BugsCEP database  [88] ). Overall, the picture is of a pond on a braided floodplain on sparsely vegetated sandy tundra, visited occasionally by larger vertebrates. Taxon occurrence data (i. e. assuming one individual per taxon) suggest a mixed landscape for sample 2 with more or less equal indications of water, meadowland, wetlands/marshes and heathland/ moorland landscape components.
Chronology The Empetrum endocarps (crowberry) sample from the organic lens within the channel (unit 2) provided a radiocarbon age of 37 057 + 457 14 C years BP (UBA-33853). This was calibrated to 40 314-39 552 cal. years BP (2 sigma), using the INTCAL13 dataset  [121]. The remaining ages both on the channel and other units are based on the OSL results (figure  15  and table 4 ). Sample Fin 1 (Shfd02010) from unit 2, and above the radiocarbon sample, gave an age of 35. From these results, it is clear that unit 1 is older than 35. 7 + 2. 0 ka and unit 2 dates to between 39. 9 + 0. 4 and 35. 7 + 2. 0 ka BP. This corresponds to MIS 3  [122]  and to between Greenland Stadial 9 (GS-9) through to Greenland Stadial 7 (GS-7) encompassing the interstadial of GI-8  [6]. Unit 3 lies between 35. 7 + 2.
Beetle-based climate reconstruction A palaeo-temperature reconstruction was undertaken (Finningley paleo in figure  16 ; electronic supplementary material) using the mutual climatic range (MCR) method  [126]  as implemented in the BugsCEP software  [89]. The MCR method reconstructs the thermal environment in which the largest proportion of species in a sample could survive, using their modern and historical ranges for reference. It allows for the calculation of TMax-mean temperature of the warmest month, TMin-mean temperature of the coldest month and TRange-difference between TMax and TMin and essentially an indication of continentality. A higher TRange suggests a more continental climate, with larger differences between summer and winter temperatures. Although previous publications have derived means and errors, or otherwise indicated mid-points for reconstructed ranges, these have been shown to be mathematically incorrect  [89, 127]  and are thus not used here.
Coleoptera over-winter as a range of different stages (egg, larval instars and/or adult, pupa), many Arctic taxa surviving a wide variety of winter conditions in diapause  [86] , and there are a number of winter active species reliant on snow cover  [128]. However, wherever snow thickness is a few tens of centimetres or more, as is common where shrubs trap snow, Coleoptera (or anything else) over-wintering or active at or near the land surface (i. e. beneath the snow) may not provide a good indication of air temperature. Winter temperature reconstructions from any organism with restricted winter activity are thus inherently less constrained than summer temperatures, as is reflected in the reconstructions in figure  16 , and calibration data for more extreme (hot or cold) temperatures in the BugsCEP software are limited  [89].
Among the species most acutely responsible for the A -B -D -E -F -G -H -S1 -S1 -S1 -S1 -S2 -S3 -S12 -S34 -S4 -S20 -A1 -A2 -F2 -S1 -Sample 3 -Sample 2 -B3 -B2 -E4 -S4 -S5 -S6/6 -S6/7 -S6/8 -E5 -E7 -E9 -US -MH -MN -S45 -S2 -S3 -S4 -S5 -S6 -G141 -1981-2010 30. 9 -34. Samples with less than 3 MCR species have been omitted, but samples with poor maximum species overlaps (less than 80%) have been included to provide more samples for comparison. The lower bar chart shows the number of species used in each reconstruction. Sample names are as stored in the BugsCEP database  [88]. Site ages, in calibrated ka BP, are given for either the range of dated insect samples or the overall site where these are not directly dated (see electronic supplementary material). The figure therefore does not represent a contiguous sequence.
These values are similar to the possibly interstadial and warmer samples described for Baston Fen, Earith and Four Ashes, and several degrees warmer than Finningley. In summary, the MCR reconstructions from Finningley are thought to record cold stadial conditions, but with milder summers and winters than those inferred from many other English beetle faunas dated to MIS 3. The overall MIS 3 beetle dataset from England indicates substantial variation in summer and winter climate during this interval (approx. 59 -28 ka), consistent with independent evidence for substantial stadial -interstadial climate variability obtained from the Greenland ice-core record  [6, 133]. In view of the millennial timescales of such variability and the uncertainties of dating terrestrial sites at or near the limit of radiocarbon, it is clear the correlations even between terrestrial MIS 3 sites in England are challenging, and terrestial -ice-core correlations remain speculative.
Acknowledgements. Access to the site was kindly provided by the successive quarry managers of LaFarge/Tarmac. MSc students from  Sheffield University  helped clear sections. Laura Trinogga  at the  Doncaster Museum  and  Dmitri Logunov  at the Manchester Museum are thanked for access to insect collections. Geoff Gaunt and members of the  QRA  fieldtrip 2001 provided stimulating discussions which helped shape this manuscript. Robert Angus  kindly confirmed the identification of Helophorus praenanus and  Tim Prosser  provided the modern climate information. The authors wish to acknowledge the help given by  Peter Neu (Germany) ,  Peter Wiberg-Larsen (Denmark) ,  Aki Rinni (Finland) ,  Peter Barnard (UK)  and  Ian Wallace (UK)  for Trichopteran reference specimens, distribution data and taxonomic expertise and  Colin Howes  for drawing our attention to the vertebrate remains.
Data accessibility. The datasets supporting this article have been uploaded as part of the electronic supplementary material. The new fossil insect data upon which this paper is based are provided as electronic supplementary material as well as being deposited in the freely available BugsCEP database (www. bugscep. com) and SEAD database (www. sead. se) on publication. DOI's and upload to the Neotoma repository (www. neotomadb. org) are available from 2020 for these resources. The compilation of MIS 3 dates is provided as electronic supplementary material, including as much metadata for each date as was obtained through our literature search. The BugsCEP database and software includes all of the MIS 3 beetle data that were used to undertake the climate reconstruction of comparative sites. BugsCEP's built in mutual climatic range (MCR) routines were used to calculate all beetlebased climate reconstructions presented.
Statistical Analysis of Differential Gene Expression In order to detect problems and biases during mRNA isolation and library preparation, we used the ERCC_Analysis Plugin (version 5. 8. 0. 1) provided in the Torrent Suite software (version 5. 8. 0). Only forward strand reads were selected for mapping and the minimum transcript count was set to 50. The raw reads from each sequencing library were quality checked using FastQC (version 0. 11. 7; Babraham Institute) with a focus on the read length distribution and adapter contamination. In order to quantify the expression of known bovine transcripts in these datasets we used Salmon (version 0. 9. 1) that uses a lightweight alignment method (quasi-mapping) for rapid transcript abundance estimation  [19]. Briefly, we obtained the transcript reference of cattle (GCF000003055.
6 Bos Taurus UMD 3. 1. 1) from NCBI and selected only transcripts that were featured as mRNAs, non-coding RNAs and miscellaneous RNAs (for details see Supplementary Table  S2 ). A Salmon index was created using the option for a perfect hash, rather than a dense hash. Each sequencing library was then used as input for the major Salmon function "quant" using appropriate options for the library type (stranded single-end protocol with reads coming from the forward strand) and the mean read length. The number of bootstraps was set to 100 replicates. Subsequently a "tx2gene" table was prepared using the accessions of each RNA transcript from the aforementioned reference in the first column and the corresponding gene symbol in the second column (see also Supplementary Table  S3 ). Using this table, the transcript abundancy datasets from Salmon "quant" were imported into R workspace (version 3.
4. 1; [20] ) using the "tximport" package (version 1. 6. 0; [21] ) as implemented in the Bioconductor library. For handling and manipulating R scripts, the software RStudio (version 1. 0. 153) was used. Technical replicates of samples (multiple sequencing of the same library from a single sample) were combined and the datasets were pre-filtered using only genes were more than four samples had raw gene counts greater than or equal to 100. In order to check the dataset for influence of treatment (infection, time and animal) and repeatability (replicates of same treatment) a principal components analysis (PCA) as well as a heatmap clustering was conducted using the regularized log transformed (rlog) read counts. DESeq2 (version 1. 18. 1; [22] ) was then used to identify differentially expressed genes between the treatments based on the negative binomial distribution.
The 20 most intense peaks per scan cycle were selected for CID fragmentation in the LTQ. Ions with unknown charge state, as well as singly charged ions were excluded from fragmentation. Dynamic exclusion of precursor ions for 30 s was enabled. Internal calibration (lock mass 445. 120025) was enabled as well. For protein identification, resulting spectra were searched against a database containing sequences of Bos taurus including reverse sequences and common laboratory contaminants  (44,376 entries). Database searches using Sorcerer-SEQUEST (version v. 27, rev. 11; Sage-N Research, Inc. , Milpitas, CA, USA) and Scaffold (version v. 4. 8. 4; Proteome Software, Portland, OR, USA) were done as described earlier  [27]. In order to identify differentially expressed proteins we imported the raw NSAF values into R workspace and created an ExpressionSet using the Biobase package (version 2.
38. 0;  [28] ). We then used the "Power Law Global Error Model" (PLGEM) as implemented in the "plgem" package (version 1. 50. 0; [29] ) and first fitted it to the dataset using default settings and "FMDV infection" (samples from 24 h post infection (hpi) and 28 dpi) as fitting condition. After computation of observed and resampled signal-to-noise ratios, p-values for each detected protein were calculated.
Data Availability The raw sequencing data along with deduced Salmon read count tables and substantial metadata are available at ArrayExpress (http://www. ebi. ac. uk/arrayexpress) under the accession number E-MTAB-7605. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www. proteomexchange. org) via the PRIDE  [30]  partner repository with the dataset identifier PXD012242.
RNA Sequencing and Exploratory Data Analysis The polyadenylated RNA fraction from 21 primary bovine SP cell culture samples was sequenced. From these, 10 and 11 samples originated from a female and male bovine, respectively. Cells for analysis were harvested immediately before inoculation (0 hpi), 24 hpi and 28 dpi (Figure  1A ). For each time point (0 hpi, 24 hpi, 28 dpi) and treatment (FMDV, control), a minimum of four biological replicates (two from each animal) was sequenced. The number of reads for each sample ranged from 21. 8 to 28. 0 million, with an average of 24. 7 million. In total, 518. 4 million reads were included in the analysis (Supplementary Table  S1 ). These reads were assigned to a cattle transcript reference and the raw gene count data was transformed with respect to library size and transcript length using an appropriate model (Supplementary Figure  S2 ).
A full list of differentially expressed transcripts can be found in the supplementary material (Supplementary Table  S5 ). The significantly differently expressed genes during acute and persistent infection were further matched to specific metabolic and signaling pathways using the Reactome database  [31]  (Figure  3 ). During both infection stages, most of the regulated genes are associated with interferon signaling, in particular interferon α, β and γ signaling (Figure  3A  and Table  2 ). The induction of these interferons appears to be mediated by the DDX58/IFIH1 pathway. Accordingly, a number of interferonstimulated genes (ISG), such as the ubiquitin-like family member ISG15, that induce strong antiviral mechanisms of the innate immune system are highly activated. During acute infection, transcripts involved in the interleukin-1 (IL1) and -10 signaling pathways as well as the programmed cell death (apoptosis) pathway were enriched.
However, it is of note that the IL1 and IL10 genes were not The significantly differently expressed genes during acute and persistent infection were further matched to specific metabolic and signaling pathways using the Reactome database  [31]  (Figure  3 ). During both infection stages, most of the regulated genes are associated with interferon signaling, in particular interferon α, β and γ signaling (Figure  3A  and Table  1 ). The induction of these interferons appears to be mediated by the DDX58/IFIH1 pathway. Accordingly, a number of interferon-stimulated genes (ISG), such as the ubiquitin-like family member ISG15, that induce strong antiviral mechanisms of the innate immune system are highly activated. During acute infection, transcripts involved in the interleukin-1 (IL1) and -10 signaling pathways as well as the programmed cell death (apoptosis) pathway were enriched.
In addition to NCAM1, other proteins that are associated with extracellular membrane (ECM) pathways, such as type I and III collagens (COL1A1 and COL3A1) were also downregulated during persistence. Black lines connect pathways that are enriched in both datasets and the size of the circles represents the ratio of genes found for a certain pathway to the overall gene number. (B) The log2 fold change of genes that contribute to selected enriched Reactome pathways are highlighted (24 hpi-blue dots) and 28 dpi-orange dots). The grey dotted lines indicate a |log2 fold change| > 1. The numbers on the right correspond to the number of genes associated with each pathway.
Acknowledgments: We thank  Cells for Life Platform , partly funded by the  Infrastructure Committee  at  SLU, Sweden , for providing facilities and equipment. We acknowledge  Patrick Zitzow  and  Jenny Lorke  of the  Institute of Diagnostic Virology at the Friedrich-Loeffler-Institut  for their excellent technical assistance. We are grateful to  Dörte Becher  and  Claudia Hirschfeld  from the  Institute of Microbiology at the University of Greifswald  for helping with the mass spectrometry analyses.