dataesr/scientific-paragraphs-categorization huggingface dataset:
GENOMIC STUDY OF MARSEILLEVIRUSES General Genomic Characteristics The genomes of marseilleviruses are double stranded DNA with a size ranging from 348 to 404 kilobases (kb) and with a G + C content between 42. 9 and 44. 8%. The number of predicted genes varies between 386 for kurlavirus and 491 for Brazilian marseillevirus. A high proportion of these genes corresponds to ORFans (genes without significantly similar sequences in databases) or hypothetical proteins with no function assigned. This is a common feature of giant viruses  (Raoult and Boyer, 2010). Several paralogous families of proteins are described in marseillevirus genomes. The most abundant are MORN (Membrane Occupation and Recognition Nexus) repeat-containing proteins, serine and/or threonine kinases (a host-signaling protein family), restriction endonucleases, F-box containing proteins, ankyrin repeat containing proteins, and HNH endonucleases.
Phylogeny The family Marseilleviridae is classified in five lineages with, as the founding members, marseillevirus (Marseillevirus marseillevirus) in lineage A, and lausannevirus, tunisvirus, Brazilian marseillevirus, and golden marseillevirus in lineages B, C, D, and E, respectively  (Boyer et al. , 2009; Thomas et al. , 2011; Aherfi et al. , 2014; Dornas et al. , 2016; dos Santos et al. , 2016). A phylogenetic tree with most of the marseilleviruses available in the NCBI database was constructed (Figure  5 ). The MCP gene (the most widely-deposited one) was selected for tree construction with 49 marseilleviruses. The structure of the tree follows the previously-established classification, but the marseillevirus-like genomes generated from metagenomic data form two outgroups relative to the known lineages, one with marseillevirus LCMAC201 and marseillevirus LCMAC202, the FIGURE 5 | Phylogenetic tree based on the major capsid protein (MCP).
In addition, the clusters comprising the core genome included proteins annotated as putative glycosyltransferases, mannosyltransferase, metallopeptidase, RNA methyltransferase, and ATPase (Supplementary Table  2 ). The reciprocal best hits analysis performed with the ProteinOrtho software (v6. 0. 10) with amino acid identity and sequence coverage thresholds of 30 and 70%, respectively  (Lechner et al. , 2011)  reflected the same increasing dynamic of pangenome size, and a total of 70 clusters of proteins included at least one sequence of each of the 29 viruses (0. 6% of the full set). The analysis of the pangenome reinforced the family Marseilleviridae classification by lineages. The pangenome size was relatively constant in each lineage (Figure  3 ). The addition of each lineage one by one coherently increased the pangenome size.
Cells, Virus and Reagents PRRSV-permissive Marc-145 cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM; Life Technologies Corp. , Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS) and 100 U/mL of penicillin plus 100 µg/mL streptomycin. Porcine alveolar macrophages (3D4/21, ATCC CRL-2843) were cultured in RPMI (Hyclone Laboratories, Logan, UT, USA) containing 10% FBS with penicillin/streptomycin. All cells were maintained at 37 • C with 5% CO 2 in a humidified incubator. The PRRSV strain used in this study was highly pathogenic (HP) PRRSV XJ17-5 (GenBank ID: MK759853. 1), which was isolated from Xinjiang Province, China, being the representative strain of HP-PRRSV2 predominant in Chinese swine herds  [28]. The virus was propagated and titrated in Marc-145 cells grown in DMEM supplemented with 2% FBS.
CRISPR gRNA Design and Preparation of STING -/-Marc-145 Cells The CRISPR gRNAs targeting monkey STING (GenBank accession: NC_041759. 1) were designed using the web tool from Benchling (www. benchling. com, accessed on 9 September 2021). Two gRNA 1 and gRNA 2 were chosen according to the predicted scores, and the encoded DNA sequences are shown in Table  2. The annealed gRNA encoding DNA pairs were ligated with the BbsI digested vector pSpCas9(BB)-2A-GFP (pX458, Addgene, Watertown, NY, USA). Subsequently, gRNA expressing pX458 was transfected into Marc-145 cells using Lipofectamine 3000 reagent and the GFP-positive cells were sorted out from transfected cells using a FACS Aria SORP cell sorter (Becton Dickinson). The individual Marc-145 cell clones obtained by limited dilution method from the sorted GFP expressing cells were screened by PCR using the designed primers shown in Table  2.
Porcine STING RNA Interference by siRNA All siRNAs targeting porcine STING (mRNA accession number: NM_001142838. 1) were designed and synthesized by Invitrogen (ThermoFisher Scientific) and the siRNA sequences are listed in Table  3. CD163-PAMs were plated into 24-well plates and the cells with 70-80% confluency were transfected with 30-50 nM of siRNA using Lipofectamine 2000 according to the manufacturer's instructions. To determine the efficiency of the knockdown, total RNA was extracted from the cells at 24 h post transfection, and endogenous mRNA levels of STING were quantified by RT-qPCR. In parallel, cells were lysed, and proteins were measured by Western blotting for STING protein expression with polyclonal anti-STING rabbit antibody (1:2000) (Proteintech, Rosemont, IL, USA).
Flow Cytometry Marc-145 or CD163-PAMs were transfected or stimulated with agonists, and then infected with PRRSV XJ17-5-dsRed. At different time points post infection, cells were collected and analyzed by FACSVerse flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) for GFP and dsRed expressions. All samples were gated based on forward scatter (FSC) and side scatter (SSC) to gate out cellular debris or dead cells. The levels of GFP and dsRed were detected using the wavelength pairs 488/525 nm and 510/600 nm, respectively. The final analysis and graphical output were performed using FlowJo software (Tree Star, Inc. , Ashland, OR, USA).
Acknowledgments: The work was partly supported by the  National Natural Science Foundation of China  ( 31872450 ;  31802172 ), and A Project Funded by the  Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). N. C is supported by  Natural Science Foundation for Excellent Young Scholars of Jiangsu Province and High Talent Supporting Program of Yangzhou University. Y. X is supported by  Research Innovation Project of Jiangsu Province Graduate Students.
We divide our discussion into the main characteristics of mast seeding: interannual variation, synchrony, temporal autocorrelation, and mast frequency. Data indicate that masting patterns, are changing, but the direction of that change varies, likely reflecting the diversity of proximate factors underlying masting across taxa. Experiments to understand the proximate mechanisms underlying masting, in combination with the analysis of long-term datasets, will enable us to understand this observed variability in the response of masting. This will allow us to predict future shifts in masting patterns, and consequently ecosystem impacts of climate change via its impacts on masting.
At regional scales, analysing of large-scale masting observational datasets using a geography of synchrony approach may illuminate the drivers of synchrony and its variability over time  [69]. Where large-scale datasets based on observations of masting are not available, the use of cone-scars or dendrochronological methods may provide an opportunity to retrospectively assess changes in masting synchrony across scales  [36, 70]. Another challenge is to unpack the consequences of changing synchrony for recruitment and wider community dynamics. Declining synchrony has been demonstrated to decrease individual plant fitness as measured by viable seed production  [40] , but the next step is to link this with tree regeneration and population growth  [71, 72]. Trophic consequences of changes in synchrony are potentially substantial but remain unexplored.
An analysis of a global network of 1086 time-series for 363 species that found no global change in mast frequency over the last century  [43] , although this lack of a global signal may result from variation in the direction of change in frequency among species and populations. It should be noted that detecting changes in masting frequency using short datasets is challenging. Multi-decadal mast records are rare, but analysis of European beech masting frequency based on regional aggregations of records  [54]  or tree-ring-based reconstruction  [70]  indicate substantial multi-decadal variability in mast frequency that is not clearly linked to long-term anthropogenic climate change. Mast years represent pulses of reproduction and resources, thus understanding the climate change impact on mast frequency is crucial for predicting and managing ecosystem responses to climate change  [89, 90].
Pearse et al. [43]  demonstrated an overall increase in interannual variation of reproduction across a global dataset representing 363 species, but a notable result was the large and unexplained variance in changes to interannual variation over recent decades. Thus, a priority for the next generation of studies based on increasingly extensive large-scale masting datasets will be to explain this variation, and identify species traits and regions that may structure this variation. Metrics used to characterize masting are linked to individual fitness and population viability via the benefits gained through economies of scale, and to wider ecosystem dynamics via the characterisation of resource pulses  [36, 41]. However, a full understanding of how masting responds to climate change is complicated as masting metrics are not independent.
For example, a shift to more frequent mast years will reduce the interannual variability as measured by coefficient of variation and will change the strength of autocorrelations at different time-lags. Limited evidence so far indicates that spatial and temporal changes in masting patterns may not be correlated  [36, 44]. The next challenge is to understand if common responses exist and under what circumstances. A major challenge is the attribution of observed masting changes to climate change. So far, studies are correlational rather than experimental, with causation to climate change inferred. For example, Pearse et al  [43]  found no relationship between observed changes in CVp and local rates of climate warming across a dataset of 79 species, but this analysis was not able to control for the likely variation in response among species and habitats  [98].
Analysis of the within-species masting response to local rates of climate change may prove a useful step forward, particularly where existing species-specific datasets cover gradients in the local rate of climate change. Nevertheless, masting responses will also depend on concomitant environmental changes including nitrogen deposition and CO2 fertilization, both of which may enhance forest productivity and relax nutrient limitation of masting  [99]. The effect of large-scale climate oscillations on decadal-trends in masting further complicate attribution of changes in masting variability and spatial synchrony to anthropogenic climate change  [52, 54]. Untangling these interacting factors remains challenging. A small but growing number of studies have used experimental approaches in an attempt to isolate the effects of climate change on masting.
To understand the response of interannual variability, synchrony, temporal autocorrelation and mast frequency to climate change, we must use a dual approach that combines the analysis of long-term monitoring datasets and targeted experimental studies. Multi-decade masting datasets are increasingly available. They now include high species replication and time-series collected from sites distributed over large climate gradients, including across regions that have experienced varying rates of recent climate change  [43, 106]. Testing for changes in masting patterns in such datasets, combined with improved methods of climate change attribution, will enable characterising masting responses to recent climate change. Such studies will enable a general understanding of likely responses of masting to climate change, including testing alternative hypotheses for masting sensitivity to climate change  [21, 27].
Crop models are usually calibrated and evaluated with datasets that do not focus on successive stresses, but on long-lasting stresses (e. g. , mineral limitation, increased temperature over one specific phenological stage). Therefore, the emerging question concerns the extent of biases in the responses of crops that are challenged to a long-term stressing period vs. to repeated stressing events as observed when plants acquire stress memory. Indeed, when two stressing events occur, the modelled plant response will result in two levels of decreased performance which do not represent the plant behaviour when the first stressing event triggers beneficial stress memory (Figure  3 ). Another example of model disruption deals with the characteristics of seeds from stressed mother plants. Studies on several crops indicated that germination kinetics were modified when seeds came from heat-or drought-stressed mother plants  [265, 266] , thus leading to a lower value of the base temperature for germination and to faster seed germination kinetics under higher temperature (Brunel-Muguet, unpublished).
This is in line with prior observations which indicated that landraces (or 'peasant seeds') are more resilient to extreme climate events than varieties produced under semi-controlled or homogeneous and stable conditions  [267]. For crop models where the base temperature for germination is a key plant parameter (e. g. , SIMPLE)  [268, 269] , unchanged value will result in prediction biases. This example highlights the evidence for trans-generational memory of heat stress that can modify plant parameter values in a crop model. Figure  3. Illustration of the above-raised modelling questions when simulating stress response (measured through plant performance) in a two-stressing-event sequence when a priming effect from the first stressing event is observed. Graphs (a) (green dotted line) and (b) (green dotted line) display negative effect of a single stressing event (early (a) and late (b)) on plant performance along with associated molecular events (levels of RNA transcripts and chromatin structure changes).
Note: The adult specimens were weighed at death. Both neonates were weighed after thawing and evisceration. species and the developmental stages using a Student's t-test with the logarithm of the values, using the stats. ttest_ind function of the SciPy Python package (see File S1 for code). If the value for a muscle was missing in any of the two specimens that were compared with the t-test, the muscle was removed in the other specimen compared as well, in order to compare identical sets of muscles. This was the case for eight muscles out of 63 when comparing between both adults, 20 when comparing between both neonates, 11 when comparing both C. simum individuals, and 20 when comparing both R. unicornis specimens.
ACKNOWLEDGEMENTS We wish to acknowledge very warmly  Suzannah Williams  and  Hinnah Rehman , undergraduates at the  Royal Veterinary College  who helped greatly with the dissections of the White and Indian rhinos, respectively, and with data collection. We thank  Elizabeth Ferrer ,  Sharon Warner  and all those that helped in the  Structure and Motion Laboratory (RVC)  for assistance in data collection during this project, along with post-mortem room support from  Richard Prior. ZSL Whipsnade Zoo ,  Woburn Safari Park ,  Munich Hellabrunn Zoo  and their staff are wholeheartedly thanked for generously donating the specimens used. Finally, we warmly thank  Korakot Nganvongpanit  ( Chiang Mai University, Thailand ) for editorial work, as well as  Jamie MacLaren  ( University of Liège, Belgium ) and two anonymous reviewers for their helpful comments and constructive critiques.
Simultaneous presence of native and alien rDNA was not detected in the sequenced BAC clones although a chimeric sequence read containing parts of both native and alien rDNA (Fig. S6  and Dataset S1) was found within the Nanopore whole genome shotgun (WGS) dataset (Tables  S3, S4 ). The fragmentation of native rDNA observed in this sequence is possibly result of pseudogenization. Nevertheless, given the rarity of this sequence type, one cannot rule out its origin as a sequencing artefact. Presumed pseudogenization of alien rDNA at a larger scale was caught in action in the longest Nanopore read containing Potentilla-specific rDNA (Fig. S7  and Dataset S2). Here, disruption of rDNA subunits has a more or less symmetrical pattern within a palindromic structure that renders little likelihood for the artefact origin.
Primers ITS5 and ITS4  (58)  were used for routine amplification of rDNA ITS. For differential amplification of Potentilla-specific ITS2 in Erythronium, new primers were designed (Table  S6 ). The single instance of cloning of E. denscanis ITS region was performed as described previously  (31). Potentilla reptans ITS, Erythronium ITS clones and Potentilla ITS2-specific PCR products of Erythronium were Sanger sequenced at Macrogen Europe B. V. (Amsterdam, The Netherlands). Phylogenetic Analyses. Potentilla-type ITS and partial ETS sequences retrieved from BAC clones and Illumina datasets of Erythronium were added into a recent backbone phylogeny of Potentilla  (59). This phylogeny was supplemented with additional Potentilla taxa having publicly available ITS sequences that were potentially closely related to the alien ITS of Erythronium.
A maximum likelihood (ML) phylogeny was computed with the IQ-TREE (60) web server (http://iqtree. cibiv. univie. ac. at/). The strategy for public Potentilla ITS sequence gathering as well as details of their aligning and parameters of the applied ML phylogeny are provided in SI Materials and Methods. GenBank accession numbers for newly generated and previously published Potentilla and Erythronium sequences included into the study are listed in Tables S1, S8 and S9, respectively. Southern Blot Analysis. In addition to constructing a BAC library from flow-sorted nuclei (see further), Southern blotting technique was used to verify integration of dicot rDNA into the genome of E. dens-canis. Based on the BAC clone sequences of E. dens-canis, it was predicted the ApoI restriction enzyme to produce an ~878 bp long fragment selectively from the dicot-type rDNA.
Hybridized probes were visualized through fluorescently-labelled antibodies against biotin-dUTP and digoxigenin-dUTP as in  (63). Chromosomes were counterstained with 4´,6-diamidino-2-phenylindole (DAPI, 2 µg/ml) in Vectashield antifade. Fluorescence signals were analyzed and photographed using a Zeiss Axioimager epifluorescence microscope and a CoolCube camera (MetaSystems, Altlussheim, Germany). Individual images were merged and processed using the Photoshop CS software (Adobe Systems, San Jose, CA, USA). Construction and Screening of a BAC Library from E. dens-canis. HMW DNA was prepared according to a protocol modified from that in ref  (64). The method takes advantage of purification and sorting of nuclei using flow cytometry and embedding them into agarose plugs. Briefly, 30 g of fresh leaf tissue was fixed in 2% formaldehyde for 20 minutes and homogenized.
Complementary DNA and gDNA (for control) were then used as templates in the upcoming regular PCR using primers for dicot and monocot ITS2 (Table  S6 ). A SQ-PCR approach was employed using Maxima SYBR Green qPCR Master Mix (Thermo Scientific, Waltham, MA, USA) and primers for dicot and monocot ITS2. The experiment was extended with gDNAs of one E. caucasicum sample and four additional E. dens-canis samples from distant localities of the species' range (Table  S1 ). Each sampleprimer pair combination was run in triplicate. Raw fluorescence data provided by a Rotor-Gene 6000 instrument (Corbett Research, Mortlake, Australia) were analyzed in the LinRegPCR v. 11. 0 software. Relative quantification was achieved with the RFD = E -delta Cq formula. Aliquots of the completed SQ-PCR reactions were ultimately verified by agarose-gel electrophoresis and ethidium bromide-UV detection.
Nanopore Long-Read Sequencing and Data Analysis. Oxford Nanopore Technologies' (ONT) long-read sequencing service was ordered from Deep Seq (University of Nottingham, Nottingham, UK). Bulb-extracted gDNA of ~25 kbp in size was used as input for the preparation of the standard SQK-LSK109 library as per ONT's protocol (ONT, Oxford, UK). The library was run over one PromethION Flow Cell. Base calling was completed in PromethION's sequencing software MinKnow by using the default quality threshold of 7. Resulting 12. 6 million reads (~68. 2 Gigabases (Gb) of data) had N50 read length of 9. 9 kbp. A total of 64998 contigs with minimum, maximum and average lengths of 1 kbp, 1. 38 Mbp and 5. 69 kbp, respectively were assembled using CANU v 1. 8 (67) from the raw Nanopore data. Polishing of the CANU assembly was attempted using Pilon (68) (https://github.
com/broadinstitute/pilon) with default parameters, using a preprocessed paired-end Illumina dataset (see below). Both the raw reads and the contigs were screened for dicot/monocot ITS1/ITS2 fragments (Table  S3 ) in Geneious Prime. In terms of the number and the length of Nanopore reads containing Potentilla-specific rDNA sequences, the assembly did not turn out to be superior to the original read dataset (Table  S4 ). Raw Nanopore sequences that were analyzed and presented in the current study were included in the supplementary dataset (Datasets S1 and S2).
Illumina Whole Genome Shotgun (WGS) Sequencing and Read Analysis. A TruSeq shotgun library (of 350 bp insert-size) was prepared from gDNA of E. dens-canis and paired-end (150 bp) sequenced via a HiSeqX platform at Macrogen B. V. Fifty gigabases of raw reads were preprocessed to remove low-quality reads and controls and to trim adapter sequences by using a combination of tools provided in the BBMap project  (69, 70). A de novo assembly of Illumina reads was performed in SPAdes  (71). Potentilla-specific ITS and partial-ETS sequences were extracted from contigs following a screen in Geneious Prime with dicot ITS1/ITS2 sequences. In case of E. caucasicum 5 Gb of WGS data was obtained. A shotgun gDNA library of 350 bp insert size was prepared and paired-end (150 bp) sequenced at Novogene (Cambridge, UK) using the Novaseq6000 platform.
Acknowledgments The authors would like to thank  Adela Jurković  ( University of Zagreb ) for providing guidance to LB in regard with the Southern blotting technique. Chemiluminescence detection was achieved with the assistance of  László-Csaba Bencze  and  Alina Filip  ( Babeș-Bolyai University ) whose help is also acknowledged. The authors thank  Kunigunda Macalik ,  Jānis Rukšāns  and the  Rare Bulb Nursery (Latvia ),  Andrey Dedov ,  Sergey Banketov ,  Julianna Lingvay ,  Paul-Marian Szatmari ,  Gábor Sramkó ,  Boštjan Surina ,  Polina A. Volkova ,  Nikolay V. Stepanov ,  Alexander L. Ivanov  for their help in the acquisition of plant material. This work was supported by a grant of the  Romanian Ministry of Research and Innovation ,  CNCS -UEFISCDI , project number  PN-III-P1-1. 1-PD-2016-0919 , within  PNCDI III.
PAB was supported by the research grant  20-12496X  ( Grant Agency of the Czech Republic). The authors would like to acknowledge support of the  GENTYANE platform of Clermont-Ferrand INRAE Center  (http://gentyane. clermont. inra. fr/) for providing assistance in NGS sequencing and of the Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul, doi: 10. 15454/1. 5572369328961167E12, http://bioinfo. genotoul. fr) for providing computing resources.
2014)  and we report here the data (Suppl. material 1) that were gathered between 2012 and 2019. Our crowdsourcing survey is based on a smartphone application named Agiir allowing volunteers to report cases of BMSB observations. The data are stored in a database and further carefully checked by professional entomologists. We here provide all reports, either valid BMSB observations or erroneous sightings in order to provide the entire dataset generated in the course of the survey.