dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on this dataset, 8 OTUs were identified using the amplicon data. These OTUs all correspond to known aphid bacterial symbionts  [8, 16] , respectively Buchnera aphidicola, the aphid primary symbiont, and Hamiltonella defensa, Rickettsiella viridis, Rickettsia sp. , Regiella insecticola, Fukatsuia (also known as PAXS or X-type-), Serratia symbiotica and Spiroplasma sp. One additional lineage from the Myxococcales order was detected using the gene capture approach. However, its presence could not be confirmed by targeted PCR, despite repeated attempts based on two different sets of primers (not shown), suggesting these data either stem from contamination or from the extreme sensitivity of gene capture by hybridization. Overall, the amplicon sequencing and gene capture approaches are qualitatively equivalent, producing similar pictures of the microbial diversity present in this sample.
(A) Stationary phase cultures of the indicated S. meliloti strains were exposed or not to ionizing radiations (IR) at a dose of 300 Gy, and bacteria were serial diluted and plated. Survival was calculated as the colony forming ability after exposure to 300 versus 0 Gy (expressed in %). Results shown are the means (± SEM) of the data obtained from independent experiments (n ≥ 3). One outlier value was excluded using Grubbs test (alpha = 0. 05%). (B) S. meliloti strains CBT2173 (WT), CBT2175 ( ku2), CBT2177 ( ku1234) or CBT2496 ( ligD1234), which all carry a single I-SceI site in the chromosome, were transformed either with pLS273-25, a plasmid encoding the I-SceI meganuclease, or with the empty vector pLS257-1 as a control. Competent cells were prepared from exponential phase cultures at either 28 • C (blue) or 40 • C (red).
ACKNOWLEDGEMENTS We thank members of the CBR team for helpful discussions, and  Ludovic Cottret  for help with phylogenetic analyses. We thank  Andreas Kaczmarczyk  for providing plasmid pQF. We are grateful to  Carine Pestourie  and the  Non-Invasive Exploration Service of the Genotoul-Anexplo Platform  ( US006/CREFRE INSERM/UPS ) for the gamma irradiator.
The data received in the context of the renewal application EFSA-GMO-RX-007 contained: post-market environmental monitoring reports, an evaluation of the literature retrieved by a systematic search, updated bioinformatic analyses and additional documents or studies performed by or on behalf of the applicant. The GMO Panel assessed these data for possible new hazards, modified exposure or new scientific uncertainties identified during the authorisation period and not previously assessed in the context of the original application. Under the assumption that the DNA sequence of the events in maize NK603 x MON810 considered for renewal is identical to the sequence of the originally assessed events, the GMO Panel concludes that there is no evidence in the renewal application EFSA-GMO-RX-007 for new hazards, modified exposure or scientific uncertainties that would change the conclusions of the original risk assessment on maize NK603 x MON810.
NOVEL WAYS TO EXPLORE RNA STRUCTURE AND FUNCTION RNA functionality is best understood through its structure, but RNA structure determination is extremely challenging. Although it is formed based on simple base pairing rules, for RNA molecules of biologically relevant sizes there are an astronomical number of possible structural permutations, meaning that RNA structure cannot be predicted easily from base pairing rules alone. Biophysical methods such as crystallography  (59)  and NMR  (60)  are each able to determine RNA structure at atomic resolution but both have difficulty with large RNA substrates. This is evidenced by the paucity of atomic resolution RNA structures, compared to their protein equivalents. For example, the RCSB databank holds structural data for over 100,000 proteins, but contains only around 1000 RNA structures.
Acknowledgements We wish to thank the  National Park of Aigüestortes i Estany de SantMaurici (Lleida, Spain) , the  Conselleria de Medi Ambient, Agricultura i Pesca (Govern de les Illes Balears, Spain) , the  Natural Park of Sant Llorenç del Munt i l'Obac (Barcelona, Spain) , the  Malgrat de Mar City Council , the  National Park of Chréa (Algeria) , the  National Park of Béjaia (Algeria)  and  Dr. Ahmim Mourad  for their precious the contribution during field work. This work was funded by the  EU   FP6 program , Project RABMEDCONTROL, (http://www. rabmedcontrol. org)and  FP7  for research, technological development and demonstration under Grant Agreement No. 278433  PREDEMICS (" Preparedness, Prediction and Prevention of Emerging Zoonotic Viruses with Pandemic Potential using Multidisciplinary Approaches ").
A better understanding of these diverse evolutionary interactions might point to new molecular targets that could lead to biotechnological innovations. In this study, our main aims were to (1) provide a robust phylogeny for the whole family Baculoviridae using all the genetic data generated so far; (2) delineate BV species using a non-a-priori phylogenetic-concept-based approach; (3) study the evolutionary history of host use of BVs, assessing the level of host specialization and role of host shifts in the speciation of BVs; (4) elucidate the role of host plants in the evolution of BVs. Our results shed light on the biodiversity ecological and evolutionary factors that drive Baculoviridae diversification.
Virus Isolate Sequence Database We created a DNA sequence database containing sequences of at least one of the four lepidopteran BV core genes, lef-8, lef-9, per os infectivity factor 2 (pif-2) and polh. We chose those genes because it has been shown that they bear a strong phylogenetic signal  [16]  and they have been abundantly sequenced. The database includes sequences collated from the public databases GenBank, EMBL and DDBJ (version April 2018), and we tried to obtain sequences from one hundred historical BV samples originating from the reference collection at the Centre for Ecology and Hydrology (NERC-CEH), Wallingford, UK. Primers, PCR amplification protocols and Sanger sequencing are previously described  [7, 15]. Following current taxonomic practice, each BV sample was associated with the host species from which it was isolated and OB morphology (NPV or GV), as well as sampling information about the virus isolate or strain (if provided), and the name of the first author of the study from which BV sequences derived (Table  S1 ).
Host Ecology Database For each BV isolate studied, we associated host ecological data. We included the taxonomy of each insect host species (superfamily, family, subfamily), and their geographical distribution (ecozone), established from localities where insect hosts were observed in the field. We also included the host plant range of each insect host, from which we determined the associated insect host plant growth type, distinguishing woody perennial (including shrubby and suffrutescent plants) and annual/biennial herbaceous plants. Information was mainly extracted from the database of the world's lepidopteran host plants of the Natural History Museum of London  [29] , from the Barcode of Life Data System (BOLD)  [30]  and from the literature  [31]  (Table  S2 ).
Baculovirus Core-Genome Phylogeny A phylogenomic approach was used to reconstruct the BV core-genome phylogeny. BVs possess 37 core genes  [32] , identified in all completely sequenced genomes. Amino acid multiple alignments were performed on the 37 BV core gene products using MAFFT program  [33]  and, alignments were concatenated prior to the phylogenetic reconstruction. A maximum likelihood (ML) phylogenetic inference was performed on the concatenated multiple alignment under the Le and Gascuel amino acid substitution model, with a gamma distributed among site rate variation and a proportion of invariant sites (LG + Γ + I), as determined by ProtTest 3  [34]. The ML analysis was performed with RAxMLv. 8. 2 program  [35]  and statistical support for nodes in the ML tree was assessed using a bootstrap approach (with 100 replicates).
Baculovirus Species Phylogeny From the species delimitation analysis, one isolate per cluster was selected as a representative of a putative BV species (e. g. , isolates with a complete genome or isolates with the four core genes sequenced) and singletons were considered as distinct putative BV species. The topology of these species-representative isolates was extracted from the BV isolate phylogeny by pruning the other taxa, using the 'ape' package  [39]  for R. A new amino acid multiple alignment was created with only the set of the species-representative isolates. A Bayesian phylogenetic inference was performed on this alignment using BEASTv. 1. 8. 4  [40]. The substitution model and parameters LG + Γ + I were used as well as a fixed strict clock of 1. 0. The topology of the above species-representative isolate tree was used as target tree for the calculation of the consensus tree and posterior probabilities.
Host-Virus Cophylogeny To test for the existence of statistically significant topological congruence between BV species and Lepidoptera hosts, we estimated the maximum number of cospeciation events at different taxonomic levels (superfamily, family, subfamily and genus), needed for reconciling BV species and host phylogenies in TreeMap 3b  [42]  and compared this estimate to the distribution of corresponding values obtained by randomizing different Lepidoptera trees 100 times while keeping BV-host associations unchanged  [43]. Tests at superfamily, family and subfamily taxonomic levels compared the whole BV species phylogeny to published Lepidoptera phylogenies  [44] [45] [46]. Tests at the genus level were performed by reconstructing lepidopteran host species phylogenies based on published phylogenies (Noctuidae  [47] , Erebidae  [48, 49] , Lasiocampidae  [50] , Spodoptera genus  [51] ) or when not available on CO1 sequence data extracted from BOLD public database  [30]  and compared to the BV species tree simplified by pruning monophyletic clades of species sharing a given host genus down to a single species.
Baculovirus Phylogeny Here we present a comprehensive phylogeny of BVs, including most of the relevant BV genetic data available to date. A data-mining analysis was performed on public genetic databases and resulted in the collation of 749 BV isolates properly formatted, containing the nucleotide sequences of at least one of four lepidopteran BV core genes (lef-8, lef-9, pif-2 and polh genes). We were also able to determine the sequences (38 lef-8, 39 lef-9, 21 pif-2 and 45 polh) for 45 historical isolates from our own collection. The 143 sequences were deposited in the GenBank database under the accession numbers MH454109-MH454230 and MH458171-MH458191. Overall, our working sequence database contains 2053 nucleotide sequences (564 lef-8 genes, 498 lef-9 genes, 283 pif-2 genes and 708 polh genes) belonging to 794 BV isolates (Table  S1 ).
Species Delimitation To infer macroevolutionary process, it is necessary to conduct analyses at the species level to avoid interference from intraspecific diversity. A species delimitation analysis was performed on the BV isolate tree (Figure  1 ; see also Figure  S2 ) using the mPTP method and then compared to the commonly used genetic-distances-based method. The mPTP approach delimited 165 distinct putative BV species of which 70 putative BV species have been derived from clusters of two or more isolates and 95 putative species are singletons (based on a unique viral isolate) (Figure  1 ; see also Figures  S2  and S3 ). The dataset includes 38 new BV species from our historical isolates. The genetic-distances-based method delimited 178 putative BV species (72 clusters and 106 singletons; Figure  1 ; see also Figures  S2  and S4 ).
Phylogenetic Conservatism and Host Shifts Out of the 165 species of the genera Alphabaculovirus and Betabaculovirus identified by mPTP, 161 are associated with 187 Lepidoptera species from 24 different families. For all those lepidopteran hosts, we compiled a dataset with the taxonomy (superfamily, family and subfamily), the biogeographical distribution and the insect host plant range, from which we determined the host plant growth type (woody versus herbaceous) (Table  S2 ). We performed phylogeny-trait correlation and ancestral state estimation analyses on the BV species phylogeny to measure whether those traits have evolved randomly or show phylogenetic conservatism. The different phylogeny-trait correlation tests show unequivocal significant associations between the taxonomy of insect hosts, the insect host plant growth type and the BV species phylogeny (AI, PS, PD, NT, NR, UniFrac and MC; p ≤ 0.
As parasites replicating exclusively in host cells, BVs are involved in durable and intimate obligate interactions with their host, implicating long-term coevolution. The phylogenetic conservation results suggest an ancestral and frequent association with hosts of the Noctuoidea superfamily. This could possibly reflect the actual evolution of lepidopteran BVs and their current host range, as with ~42,000 out of ~157,000 described lepidopteran species the Noctuoidea is the most diversified superfamily (in comparison, the second most abundant superfamily is the Geometroidea with ~23,000 species)  [31]. Yet, BV genetic data from public databases is clearly biased towards agro-economically important lepidopteran pests, characterized by large populations, important for sustaining BV populations.
The addition of new BV samples from our collection tends to reduce the bias for pests, but BVs from pests still dominate our taxa sampling as these viruses have been isolated and resequenced many times (i. e. , large clusters of Cydia pomonella GV or Helicoverpa armigera NPV; Figure  1 ) and as at least 62 out of 161 putative lepidopteran BV species are associated with pests (Figure  2A ; Table  S2 ). As numerous lepidopteran pests belong to the Noctuoidea superfamily, this probably increases the representation of Noctuoidea infecting BVs in our dataset and could have biased our phylogenetic conservatism results (77 out of 161 putative lepidopteran BV species analyzed attack Noctuoidea, Figure  2A ). Only a diverse BV sampling, more representative of the lepidopteran diversity could confirm if Noctuoidea played a key role in BV evolution.
Supplementary Materials: The following are available online at http://www. mdpi. com/1999-4915/10/7/366/s1, Figure  S1 : Baculovirus core-genome phylogeny, Figure  S2 : Baculovirus isolate phylogeny (one panel Figure  1 ), Figure  S3 : Baculovirus isolate phylogeny including mPTP species delimitation results, Figure  S4 : Baculovirus isolate phylogeny including SpDelim species delimitation results, Table  S1 : Baculovirus isolate sequence database, Table  S2 : Host ecology database, Table  S3 : Baculovirus species delimitation.
Conceptualization, C. L. -V. , J. S. C. and E. A. H. ; Formal analysis, J. T. ; Funding acquisition, E. A. H. ; Investigation, J. T. and E. A. H. ; Methodology, J. T. and C. L. -V. ; Resources, J. S. C. ; Supervision, J. S. C. and E. A. H. ; Writing-original draft, J. T. and E. A. H. ; Writing-review & editing, C. L. -V. and J. S. C. Funding: The European Research Council (ERC) ERC-Stg grant 205206 GENOVIR supported this work; and Julien Thézé received further support from the ERC grant 614725 PATHPHYLODYN.
Author Contributions: Conceptualization, C. L. -V. , J. S. C. and E. A. H. ; Formal analysis, J. T. ; Funding acquisition, E. A. H. ; Investigation, J. T. and E. A. H. ; Methodology, J. T. and C. L. -V. ; Resources, J. S. C. ; Supervision, J. S. C. and E. A. H. ; Writing-original draft, J. T. and E. A. H. ; Writing-review & editing, C. L. -V. and J. S. C. Funding: The European Research Council (ERC) ERC-Stg grant 205206 GENOVIR supported this work; and Julien Thézé received further support from the ERC grant 614725 PATHPHYLODYN.
INTRODUCTION Weaning generates digestive troubles in pigs due to combinations of psychological, environmental, and dietary stressors  (Lallès et al. , 2007). Immediately after weaning, the gastrointestinal tract is strongly altered by villus atrophy, compromising the barrier function, reducing the digestive and absorptive capacity, and leading to 1 This study received funding from the European Union's Seventh Framework Programme for research, technological development, and demonstration under grant no. 613574  (PROHEALTH project). The authors are grateful to the staff of experimental pig facilities from INRA GenESI and to the laboratory technical support from INRA PEGASE. dysbiosis  (Pluske et al. , 1997). Systemic inflammation and oxidative stress can be observed after weaning in piglets weaned at either 21 or 28 d  (Zhu et al.
Statistical Analyses Performance traits for each experimental period were analyzed independently using linear models [glm procedure, SAS (SAS 9. 4; SAS Inst. Inc. , Cary, NC)]. For individual performance measurements [ADG during the postweaning period (n pigs = 264), and ADG, ADFI, and G:F during the growing-finishing period (n pigs = 161)], the experimental unit was the pig, and the batch (two levels), sex (two levels), dietary program (two levels), and line (two levels), and the interaction diet program × line were included in the model. For pen measurements during the postweaning period (ADFI and G:F, n pens = 12), the experimental unit was the pen, and the batch (two levels), dietary program (two levels), line (two levels), and the interaction diet program × line were retained in the model.
Finally, three of the piglets eliminated during the postweaning period were part of the pens tested during growth-finishing with electronic feeders. To avoid mixture of new individuals in these pens at 10 wk of age, these pigs were not replaced. Altogether, the dataset was thus reduced from 176 expected pigs at 10 wk of age to 161 pigs with data for ADFI, ADG, and G:F during the growing-finishing period.
Bacterial Identification Using 16S rRNA Gene Sequencing In order to identify the producer strain, DNA was extracted from bacteria and the 16S rRNA gene was amplified by PCR using universal primers as described by  Hamdi et al. (2013). The obtained PCR product was sequenced and the 16S rRNA gene sequence of the isolates was compared through a BLAST search  (Altschul et al. , 1990)  with 16S rRNA gene sequences available at the National Centre for Biotechnology Information (NCBI) database (http://www. ncbi. nlm. nih. gov/).
Peptide Identification Absorbance spectra of the purified peptide were measured with a UV 2401 pc Shimadzu spectrophotometer. The purified peptide was diluted using ultrapure water and the absorbance was measured from 200 to 300 nm using quartz cuvets. Data were collected and analyzed with the UV 2401 pc Shimadzu software. LC/MS analysis experiments were performed on a LC/MS Q TOF (Shimadzu Corporation Kyoto Japan) using an ESI ion source, operated under negative ion mode. LC-MS separation was achieved on a C18 Phenomenex 150 × 4 mm column at 35 • C. The wavelengths of spectrum detection were 214 and 280 nm. Elution was carried out with solvent A (Acetonitrile 0. 1% TFA) and solvent B (H 2 O 0. 1% TFA). A linear gradient from 0 to 60% solvent A was applied over 60 min. The injection volume was 20 µl and the flow rate was set to 1 ml/min.
Basic Analysis of B. cereus RC6 Genome Sequence The total genomic DNA of B. cereus RC6 was extracted with Analytik Jena DNA and RNA kits following the manufacturer's instruction. Genomic DNA was sequenced using Illumina HiSeq technology, and FASTQ-formatted sequence data was created. The resulting genome was annotated with SPADES and bioinformatically analyzed using both Prodigal and PRIAM base  (Claudel-Renard et al. , 2003).
Template structures were selected by a multiple threading approach with the Local Meta-Threading-Server (LOMETS) based on sequence similarity to the structure described. A total of five threedimensional structure models were generated by I-TASSER with each model. Among them, the best model was identified based on a confidence score (C-Score), and refined model coordinates were evaluated by MolProbity  (Davis et al. , 2007). All models were assessed using coach  (Yang et al. , 2013) , a software for protein ligand binding site prediction. The result identified the protein with protease function with a C-score. The best proteases were selected according to their C score. A final analysis with MolProbity was assessed to validate and to analyze the quality of the structures. Seven selected structures were compared to analogous structures in the goal to select their functions.
Molecular Docking The bovine ß-casein (113-120) fragment sequence was blasted against the PDB database using blastp. The PDB structure of the ß-casein (113-120) fragment was used as the ligand for molecular docking. Modeling of the selected ligand binding to protease and cleavage after Phe was performed with a rigid proteinprotein docking software package based on different approaches, namely the PatchDock and FireDock programs (Fast Interaction Refinement in molecular DOCKing: http://bioinfo3d. cs. tau. ac. il/ FireDock/)  (Andrusier et al. , 2007). PSAIA was used to determine the residue interaction pairs and binding residues  (Mihel et al. , 2008).
Phylogenetic and Phylogenomic Analysis For phylogenomic analysis, the entire genome data from 23 different strains of B. cereus, available at NCBI, were downloaded and used for comparative genomics. The previously described method for enzyme annotation was used on each of the 23 genomes and the resulting predicted peptidases (B. cereus RC6 P1 and P2) were clustered and compared using CD-HIT  (Li and Godzik, 2006).
Bacterial Strain Identification and Protease-Antimicrobial Activity Correlation Analysis of the 16S rDNA gene sequence from the B. cereus RC6 strain revealed a 99% identity with B. cereus reference strains. The rRNA gene was sequenced and the resulting sequence compared to the NCBI (National Centre for Biotechnology Information Data Base: www. ncbi. nlm. nih. gov) 16S rRNA database using BLAST. Antimicrobial testing showed that the B. cereus RC6 strain displays antimicrobial activity only when propagated on minimum medium supplemented with 5% casein. The highest antimicrobial activity was exhibited against closely related bacteria, including B. cereus ATCC 11778 (BC45) and B. thuringiensis USDA HD22. Antimicrobial activity was also observed against the food pathogen L. monocytogenes DISTAM MACa1.
7 Mbp. The longest scaffold was 358. 737 bp and the N50 parameter was 172. 765 bp. The GC content was estimated at 35. 19%. Gene prediction was accomplished using Prodigal  (Hyatt et al. , 2010) , which identified 5,711 protein-coding genes. We compared the predicted proteome against the MEROPS database  (Rawlings et al. , 2016) , which regroups information about peptidases, their substrates, and inhibitors. The database has been used to identify the proteolytic enzymes implicated in the  cleavage of the β-casein protein from Bos taurus. Two predicted enzymes with caseinolytic activity were found, however, the cleavage sites of these enzymes did not generate the desired peptide of interest. We subsequently decided to target the set of hypothetical proteins. To this end we functionally annotated all the protein-coding genes and extracted a pool of 1,751 proteins without any established function, using the Swissprot database (The UniProt C, 2017).