dataesr/scientific-paragraphs-categorization huggingface dataset:
The assembly resulted in two distinct contigs (one of 466,767 nt and a second of 6049 nt). A pulsed field gel electrophoresis (PFGE) confirmed the genome size and its circular structure (Supplementary Fig. 4A ). The smaller contig, not seen on the PFGE, potentially corresponds to an assembly artifact. Finally the assembly was subsequently polished using the Quiver tool from SMRTanalysis. The cedratvirus kamchatka gene annotation was performed using GeneMarkS 73 with the virus option. Only genes predicted to encode proteins of at least 50 amino acids were kept for functional annotation. These proteins were aligned against the NR and Swissprot databases using BlastP  74  (with an E-value cutoff of 10 -5 ) and submitted to CD search  39  , to InterProScan 75 with the Pfam, PANTHER, TIGRFAM, SMART, ProDom, ProSiteProfiles, ProSitePatterns and Hamapts databases, and to Phobius  76.
This resulted in 632 Mb (78,685 reads) and 346 Mb (55,797 reads) of sequences for pithovirus sibericum and mollivirus sibericum WGA amplified DNA. Identification of methylated motifs. Methylated motifs were identified using the modification and motif analysis module from the SMRTanalysis package using the datasets described in Supplementary Table  1  and the corresponding reference genome sequences. In addition we used the per-base resolution file of IPD ratios calculated by SMRTanalysis to compute the global IPDr of detected motifs and predicted MTases targets. Annotation of MTases, REases, and target prediction. We analyzed the MTases and REases encoded in giant viruses genomes based on published annotations when available, as well as a combination of CD search and Interproscan protein domain search tools analyses  39, 40.
In addition, we performed blast alignments against the REbase database 41 to support the annotations and to predict their targets when possible. Phylogenies. All the phylogenies were calculated from the protein multiple alignments of homologous sequences computed using Expresso 77 , Mafft 78 , Mcoffee  79  , and Clustal Omega 80. We next selected the best multiple alignment using TrimAI  81. For phylogenies based on single genes we identified homologs using BlastP against the NR database with an E-value cutoff of 10 -10. IQtree 82 was used to compute the tree using the best model as defined by ModelFinder  83. Bootstrap values were computed using the UFBoot method  84. Phylogenies of viral families (Fig. 2  and Supplementary Fig. 9 ) were based on the multiple alignments of strictly conserved single copy genes as defined by the OrthoFinder algorithm  85.
The assembled cedratvirus kamchatka genome has been deposited to the GenBank database under the following accession MN873693. MTases annotations were performed using the REbase database [http:// rebase. neb. com/rebase/rebase. html] and the cedratvirus kamchatka gene annotations using the Blast NCBI NR (GenBank CDS translations+PDB + SwissProt+PIR + PRF) database and the Uniprot-Swissprot [https://www. uniprot. org/uniprot/] database. The source data underlying Fig. 1, Supplementary Figs. 1 and 10, Supplementary Tables 3-5 are provided as source data file.
Acknowledgements We are deeply indebted to our volunteer collaborator  Alexander Morawitz  for collecting the Kamchatka soil samples. We thank the  PACA Bioinfo platform  for computing support. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  832601 ), from the  FRM prize Lucien Tartois  and from  CNRS  ( PRC1484-2018 ) to C. Abergel. The funding bodies had no role in the design of the study, analysis, and interpretation of data and in writing the manuscript.
Wound-Healing Scratch Assay MCF-7 and MDA-MB-231 cells were seeded into 6-well plate and cultured overnight in complete medium to assess cell motility by wound-healing scratch assay  [18]. Upon confluence, a wound was made by scratching across the monolayer cells on the bottom of the wells by using a p-200 pipette tip followed by treatment with extracts at IC 50 values for 24 h. Then, cells were fixed and stained with Coomassie brilliant Blue methanol solution. Photographs were taken at 4× magnification using phase-contrast microscopy and are representative of three independent experiments. The rate of wound healing was quantified from the picture using Adobe Photoshop software and standard deviations were determined by GraphPad-Prism 8. 3. 0 software (GraphPad Inc. , San Diego, CA, USA).
TUNEL Assay Fragmentation of DNA, a late event during apoptosis, was determined by using terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) assay, based on the enzymatic labelling of DNA strand breaks, using terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) assay. Labeling was conducted using TUNEL assay kit (Promega, Madison, WI, USA), as previously described  [46] , on MCF-7 and MDA-MB-231 cells treated for 72 h with DMSO or extracts at IC 50 values. Nuclear staining was performed by using 0. 2 mg/mL 4 ,6-diamidino-2-phenylindole (DAPI; Sigma-Aldrich, St. Louis, MO, USA) and samples were analyzed by using a fluorescent microscope (Olympus BX4 (Shinjuku, Tokyo, Japan) with CSV1. 14 software, using a CAMXC-30 for image acquisition).
Statistical Analysis Data are presented as the mean values ± standard deviation, taken over ≥3 independent experiments, with ≥3 replicates per experiment, unless otherwise stated. Statistical significance was measured by using the analysis of variance (ANOVA) test. A p value ≤ 0. 05 was considered statistically significant. Non-linear regression analysis (GraphPad Prism 8. 3. 0 software) was used to generate sigmoidal dose-response curves to calculate the IC 50 values.
Chemical Composition S. rosmarinus aerial parts collected in two different areas of southern Italy were herein subjected to two extraction procedures namely maceration and ultrasound-assisted extraction by using methanol as solvent. As evident in Table  1 , except for R4, samples exhibited comparable extraction yields. The phytochemical compositions of S. rosmarinus extracts were determined using an HPLC-DAD-ESI-Q-MS mass spectrometric approach. The identification of rosmarinic acid, luteolin-7-glucoside, rutin, ursolic acid, carnosol, and carnosic acid was sought by authentic standards (Figure  1B ). All compounds were identified based on UV spectra, molecular weight (m/z ion [M+H] + and [M-H] -), and bibliographic data concerning the phytochemical composition of S. rosmarinus previously published.
In Vitro Antioxidant Activity S. rosmarinus extracts were investigated in vitro for their potential antioxidant activity. Data are reported in Table  4. The analysis of results highlights that rosemary extracts from Ionian coast are more active than rosemary extracts from Tyrrhenian coast with some exception. In particular, the best radical scavenging power was observed with sample R2 with IC 50 values of 0. 94 and 8. 80 µg/mL in an ABTS and DPPH test, respectively. Interesting results were obtained also in a β-carotene bleaching test with sample R2 with IC 50 values of 7. 18 and 6. 53 µg/mL after 30 min and 60 min of incubation, respectively. Conversely, sample R3 such as extract obtained by maceration of S. rosmarinus from Cirò superiore was the most active in FRAP test with a value of 97.
Conclusions Bioactive compounds isolated from natural products play a dominant role as potential drugs with utility in many diseases  [59]. Herein we evaluated and compared the biological effects of four extracts of S. rosmarinus (R1-R4) collected in two distinct sites of Southern Italy. The extraction was performed by two different procedures, the maceration (extracts R1 and R3) and ultrasound-assisted extraction (extracts R2 and R4). For a long time, S. rosmarinus has been used in traditional medicine for attending to several diseases. Recently, literature data established that its extracts are endowed with antioxidant, anti-inflammatory, and anticancer properties  [68]. Our findings evidenced that the four extracts tested are provided with antioxidant properties, although rosemary extracts from Ionian coast are more active than those from Tyrrhenian coast.
Sequence Datasets Analyzed Nucleotide coding sequences from isolates of ZIKV [n = 376 including sequences from African (n = 20) and Asian (n = 356) genotypes], YFV (n = 51) and tobacco mosaic virus (TMV) (n = 68) that had information on year and place of isolation and representing all data available to date on Genbank (www. ncbi. nlm. nih. gov/genbank/) were downloaded and used in this study. The complete coding sequences were aligned and curated using Muscle algorithm (http://www. drive5. com/muscle/) within the Unipro UGENE software (http://ugene. net/download. html) [43].
Codon Usage Biases Analysis of Viral Coding Genes We applied the calculation of the CAI value using a frequency table for vertebrate and invertebrate genes from the CoCoPUTs platform (Table  1 )  [44, 45]. The CAI values were calculated to measure the synonymous codon usage bias using the CAIcal program  [46]. To evaluate the statistical support of the CAI values, we defined a threshold value or expected CAI (e-CAI)  [47]  by generating random sequences with similar GC content, amino acid composition and sequence length to each query sequence (TMV, YFV and ZIKV). CAI values above the e-CAI were interpreted as statistically significant, meaning that codon similarity arose from codon preferences, rather than from internal biases  [47].
In addition, temporal trends have exhibited a variation in ZIKV population size during the last decade, while YFV showed a large population size before the 2000s (Figure  1A, B). For each codon usage table, the CAI was normalized by length, percentage of GC and amino acid content for each dataset. The area of the plot points reflects the density of sequences at a specific time point (year). A CAI trend line above 1 (dashed black line) shows evidence of codon adaptation to the host. A trend line was generated using LOESS, a nonparametric regression method, with a 0. 95 confidence interval (shaded areas). Data obtained from codon usage tables are colored in dark grey for Aedes aegypti, in brown for Aedes albopictus, in green for Chlorocebus aethiops, in grey for Culex pipiens, in blue for Erythrocebus patas, in orange for human housekeeping genes (HKgs), in red for highly expressed human antiviral genes (AVgs) and in purple for Macaca mulatta.
The strain label is in reference to the strains in Table  3. Data obtained with the reference wild-type strain Asibi are colored in light blue and the curves in red, brown, purple, green, dark blue and orange represent strains 333, 17D, 307, 314, 345 and 357, respectively. The error bars indicate the range in values of three independent experiments performed using a multiplicity of infection (MOI) of 0. 01. In comparison to YFV strains, the growth of African ZIKV strains in mosquito cells was much slower, but with the exception of the human strain HD78788 *E , all African ZIKV strains replicated more abundantly (from 2 to 6Log10) than YFV strains and produced significantly more infectious particles in cell supernatants (p-values ranging from 5. 02 × 10 -6 to 0. 001) (Figure  6B -F and Figure  8A-C ).
Acknowledgments: We acknowledge colleagues of virology pole of  Institut Pasteur de Dakar, Senegal  for sharing mosquito pools and supportive information necessary for establishment and accomplishment of this study. We also thank  Nicholas di Paola (Center for Genome Sciences ,  United States Army Medical Research Institute of Infectious Diseases (USAMRIID) ,  Fort Detrick ,  Frederick, Maryland, USA. ) for sharing codon usage tables for human genes. This work was also made possible by the tripartite ( I. Pasteur ,  USP, FIOCRUZ)   ZIKV  project.
Acknowledgements I thank  Stephen N. Wegulo  and the reviewers of this special issue, who very efficiently collaborated with me to ensure timely publication of this issue. I am grateful to  Jacques Avelino  for his kind availability for interactions from the beginning until the completion of this special issue as well as his useful comments on an earlier version of this editorial. I am also thankful to  Jean-Pierre Sarthou  for his initial contribution to the special issue.
Data processing Raw fungal ITS paired-end sequences were joined using fastq-join software  (Aronesty 2011 ) and demultiplexed, filtered and trimmed using the pipeline SEED 2 (version 2. 1. 05) (Vetrovsky et al. , 2018). Low-quality sequences (mean Phred quality score < 30) and all sequences with mismatches in barcodes were removed from the dataset. After the quality filtering, all fungal sequences were extracted from the joined sequences using ITSx (v 1. The most abundant sequence from each cluster was used as a representative sequence for taxonomic classification. Fungal sequences were classified based on BLAST best hit against UNITE database, version 7. 2  (Koljalg et al. , 2013). The functional guild of each fungal OTU was assigned based on the FUNGuild database  (Nguyen et al. , 2016).
For alpha diversity estimation, all fungal samples were resampled to 909 sequences. Diversity indices were estimated using SEED 2 version 2. 1. 05. The abundances of sequence reads were plotted on the phylogenetic tree constructed using NCBI molecular data via phyloT  (Letunic 2015)  and the iTol visualisation tool  (Letunic and Bork 2019). Data were deposited in the MG-RAST database under accession number mgp91984 and in NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA639228. Processes data (extracted ITS2 reads) were deposited in the GlobalFungi Database (https://globalfungi. com,  Větrovský et al. 2020).
Statistical analysis To test the null hypothesis of no difference between termite body, gallery and wood (control) fungal community composition, PERMANOVA analysis  (Anderson 2001 ) was performed with adonis() function of vegan package  (Oksanen et al. , 2018)  in R (R Core Team, 2018). Euclidean distance on Hellinger-transformed fungal composition (i. e. Hellinger distance matrix)  (Legendre and Gallagher 2001)  was used as response matrix, and sample type (body, gallery or wood) was used as fixed explanatory variable. Since observations were paired within triplets (the three sample types were sampled in each triplet), which, in turn, were nested in logs, the permutations were constrained to occur within triplets, using the variable triplet as blocking factor (or strata). To visualize the results, non-metric multidimensional scaling (NMDS) was performed in two different ways.
In the first way, raw community data was ordinated by their fungal composition. This NMDS plot shows all the variability in the dataset. In the second way, community data was first regressed against triplet and log effects (i. e. the effect of spatial variability due to the experimental design was removed from the data) and, then, the residualized distance matrix was ordinated using NMDS as suggested by  Anderson et al. (2017). This plot shows the variability in the dataset, once the effect of triplet and log has been taken into account. To test the null hypothesis of no effect of sample type and termite species on fungal diversity (measured with Chao 1 index, Shannon-Wiener diversity index, and Pielou's evenness), linear mixed effect models were fitted using the function lme() from the R package nlme  (Pinheiro et al.
, 2018). The interaction between termite species and sample type was fitted as the fixed part of the model, and, a random structure of the form ~1|triplet/log was included in each model to account for the fact that measurements were grouped in triplets, which, in turn, were nested in logs. Tukey post-hoc tests were performed using the function lsmeans() of the R package lsmeans  (Lenth 2016). ©2020. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ To identify the fungal OTUs contributing to the separation between termite bodies, galleries and wood, partial redundancy analysis (partial RDA) was used  (Legendre and Legendre 2012)  for each termite species separately. Separating the communities by termite species allowed checking whether similar OTUs contribute to the separation between sample types in the three termite species.
Fungal diversity The diversity of fungal OTUs was significantly higher in termite bodies of all three species than in their galleries and intact wood, and was also significantly different between termite species. The estimated number of OTUs (Chao-1 estimate) in termite body samples, counted from the resampled dataset, ranged from 26 to 221 with an average 92-101 per species. Estimated OTU numbers and diversity indices were at least two times lower in termite galleries and in control wood. The fungal communities from termite bodies were significantly more even than termite galleries and control wood samples (Fig. 2 ).
and Lecanicillium spp. (Supplementary table  2 ). Multivariate analysis of the raw OTU dataset did not clearly separate samples by their types, but showed that the intestinal mycobiota of all three termite species is rather homogenous and similar, in comparison to the very heterogeneous communities colonizing their galleries and wood controls (k = 3 dimensions, final stress = 0. 24, Fig. 5A ). By contrast, once the spatial variability due to the experimental design (i. e. the effect of log and triplet identity) is removed (k = 3 dimensions, final stress = 0. 25, Fig. 5B ), body samples clearly separate from galleries and controls. The NMDS revealed a high stress value indicating that 2D graphical representation only roughly corresponds with the underlying data. However, the observed patterns were confirmed by the PERMANOVA analysis which showed that fungal communities from termite bodies were significantly different from galleries and controls (permutations = 999, P-value = 0.
Although previous works studied the presence of various fungi in termites  (Prillinger et al. , 1996; König et al. , 2006; Jayasimha and Henderson 2007; Santana et al. 2015; Menezes et al. 2018; Moreira et al. 2018 ), our study is the first large and systematic comparison targeting the fungal communities using a statistically robust dataset of termites, their environments, and their feeding substrates. In agreement with previous studies  (König et al. , 2006; Prillinger and König 2006) , our results indicate that the termite intestine is inhabited by ubiquitous environmental fungi. We showed that the termite associated community is distinct and relatively homogenous and stable over diverse environments and termite species, compared with termite galleries and control wood. In addition, termite galleries represent a specific habitat which significantly differs from wood in fungal community composition.
0. 11) (Bengtsson-Palme et al. , 2013) to acquire complete ITS2 region. All ITS2 sequences shorter than 40 bp were discarded, yielding a dataset of 3 967 992 fungal ITS2 sequences (length distribution 40-395 bp, avg. 175 bp). The dataset was clustered into operational taxonomic units (OTUs) using UPARSE implementation in USEARCH version 8. 1. 1861 (Edgar 2013) with 97% similarity threshold (109 476 fungal chimeric sequences were excluded during this step). A total of 10 742 fungal OTUs (without singletons) were obtained during the clustering step. To reduce the influence of contaminations and minimize the effects of barcode hopping all OTUs with up to 4 reads were discarded, what resulted in 2857 OTUs used for further analysis. ©2020. This manuscript version is made available under the CC-BY-NC-ND 4.
Fungal OTUs specifically associated with termite bodies. First 30 OTUs with the highest fit to RDA1 axis (see Fig. 6) are presented for each termite species. Taxonomic identity of OTUs is based on the Blast similarity search against NCBI Genbank and Unite database. Abbreviations: A -Ascomycota, B -Basidiomycota, C --98% sequence similarity with the type of Spizellomyces punctatus NR_111189 2no reliable ITS2 sequence similarity to named taxa. The best hits (≤90%) are various Mucoromycotina spp. (e. g. HQ406814, LC189046).
Acknowledgements We are grateful to the  Agence Nationale de la Recherche Investissement d'Avenir  for the Nouragues travel grant which allows us to collect the samples in French Guiana, to the  Czech Science Foundation   ©2020. This manuscript version is made available under the  CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ (project no. 16-05318S ), and  Czech University of Life Sciences Prague  (project  CIGA   20184303 ). NL is supported by an  Australian Research Council   Future Fellowship.
Ant identification through QR codes Labelling of ants with Aruco tags  (QR codes, Garrido-Jurado et al. , 2014) , along with the software USETracker (https://sites. google. com/site/usetrackerac/), allowed individual ants to. be identified continuously (Fig. 2B ). Ants were placed in the freezer until they were inert (about one minute). Each unique tag was then stuck to the abdomen and had a side length of 0. 8 mm, weighed 0. 1 mg (corresponding to approximately 5% of the average mass of an adult worker or less than 10% of the amount of food that a worker carries) and was printed on waterproof paper at a resolution of 1200 dpi. The tags were hand-cut using a scalpel and a steel ruler as a guide. Following a 5-min acclimatisation period, the labelling did not appear to impede the ant behaviours, movements or interactions.
Data scoring Each trophallaxis that occurred in the entire observation area, its duration as well as the identities (unique tag) and caste (forager vs. domestic) of both the donor and the receiver ant were scored. A trophallactic event was recorded when ants engaged in mandible-to-mandible contact for greater than 2 seconds (the minimum time needed to exchange a piece of food). The directionality of the food flow and the role of the donor and the receiver were determined by body posture and mandible positions: The donor opens her mandibles and displays a droplet of sucrose solution between them while the receiver turns and moves her head forward to receive it  (Greenwald et al. , 2015; Lee Cassill & Tschinkel, 1999). We only analysed videos where 100% of the trophallaxis interactions could be identified.
Statistical analysis Food exchanges We first analysed the direction of trophallaxes between foragers and domestics. We used the ratio between the in-strength and out-strength to assess whether one. caste gave more than it received (ratio > 1) or received more than it gave (ratio < 1). We used a student t-test to test for differences between the two castes. We used linear mixed models (package lme4 in R,  Bates et al. 2014)  to test for the effect of brood presence and colony size on global variables, such as the number of trophallaxes, the duration of trophallaxes and the forager/domestic ratio. Colony origin was included as a random effect. Data for all response variables were normally distributed (Shapiro-Wilk normality test, W>0. 93, P > 0. 074). Therefore, all models were type-III ANOVAs using Satterthwaite's method and had normally distributed residuals.
Global and individual network measures We then analysed the effect of brood presence, the number of individuals and the forager/domestic ratio for the global network measures (e. g. , centralisation, global efficiency, modularity and resilience) as well as the caste (forager or domestic) for the individual network measures (e. g. , degree, strength, betweenness and the clustering coefficient). Because data on the interactions and the network measures were not independent, we used Monte Carlo Markov Chain GLMM (package MCMCglmm in R,  Hadfield, 2010). This approach is a powerful and standard technique for comparing statistical models based on the original data observed to distributions of null models based on randomised data  (Pasquaretta et al. , 2014; Balasubramaniam et al. , 2018; Sosa et al.