dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). The intensity of the bands was measured using ImageJ software  (Schindelin et al. , 2012). Each protein band was measured across the same surface area, and the intensity of a protein band was determined as follows: maximum background light intensity -mean light intensity. The intensity of each protein band was defined for untreated samples (I 0 ) and oxidized samples (I). The ratio I/I 0 was used to control the evolution of intensities. The intensities were obtained by means of three independent experiments conducted on different days.
, 2020). Specific capsid protein peptide mass fingerprints were determined using MALDI-TOF MS. Theoretical masses were obtained by theoretical digestion of VP1 capsid proteins (GII. 4. VLP proteins) using the online software, PeptideMass from ExPaSy. 1 Acrylamide adduct and up to one missed cleavage were taken into account in this analysis. A peptide mass accuracy of up to 20 ppm was tolerated. The VP1 sequence used for reference had the following accession numbers: GenBank: EU876884. 1. Masses that were found in the blank gel (SDS-PAGE gel stained with Oriole, signaled free from proteins following the same extraction and trypsin digestion protocol) and across protein samples were disregarded, as such masses would be autolyze or trypsin peptides or human contaminants. Two independent experiments were performed.
Statistical Analysis All statistical analyses were performed using R statistical software (Rx64 v. 3. 5. 3; R Core Team, 2019). A Shapiro-Wilk test with a chosen alpha level of 0. 05 was performed to confirm the normality of the data. A paired or unpaired sample Student's t-test was performed on dependent and independent data that followed a normal distribution, respectively. Dependent and independent data with a non-normal distribution were subjected to a Wilcoxon signed-rank test or a Mann-Whitney U test. The significance level of all tests was set to 0. 05.
Together, these data show that free chlorine and ONOO - treatments change the propensity of GII. 4 VLPs to dissociate under SDS-PAGE denaturing conditions. With free chlorine at higher concentrations than 9,531 μM, the overall VP1 protein signal decreases in all areas between 10 and 250 kDa, suggesting more profound modifications to VP1. Effects of Free Chlorine and ONOO -on HBGA-Binding to GII. 4 VLPs HBGA-binding to GII. 4 VLPs was evaluated using ELISA. The specific interactions between ALe + and VLPs were confirmed using sodium periodate, which cleaves these complex carbohydrates and thus preventing the specific binding to the capsid  (Maalouf et al. , 2010 ; Supplementary Figure  S3 ). Using 2-and 10-fold dilutions of VLPs, a progressive reduction in HBGA-binding to VLPs was observed (Supplementary Figure  S3 ).
4 HuNoVs were comparable to those used to treat GII. 4 VLPs, the concentrations of free chlorine were decreased in order to allow SDS-PAGE to continue to detect protein bands. When the same free chlorine concentrations used to treat VLPs were applied to HuNoVs, SDS-PAGE was unable to observe any protein bands at all (data not shown). This could be explained by the initial protein concentration in VLPs, which was approximately 5-fold higher (200 μg. ml -1 ) than those in HuNoVs (44 μg. ml -1 ) for the majority of the experiments and this is why we tried also with 10 times diluted VLPs (Supplementary Figure  S4 ). The results being exactly the same for VLPs this might not be the only explanation. For HuNoV, the free chlorine concentrations tested therefore ranged from 2 to 57 μM. These treatments progressively decreased the intensity of the most representative protein bands (~100, 150, and 250 kDa) and increased the intensity of a protein band equivalent to VP1 monomers at ~60 kDa (Figure  4A ).
Forest tree species were distinguished from fruit tree species, according to the main use of tree products reported by the farmers surveyed. The degree of biodiversity of clove systems was estimated from the diversity of associated tree species in the systems. This was done by combining primary data such as species composition of the systems and different metrics i. e. species richness (number of different species), the relative abundance of each species (the numerical importance of individuals of a species relative to the total number of individuals, expressed as a percentage) and the Shannon-Wiener index  (Krebs, 1985; Santini et al. , 2017). The basal area of each associated tree, i. e. its cross-section, was calculated on the basis of the trunk diameter at breast height (DBH), at 1. 3 m for adult trees and 0.
Determination of ages of clove plots and clove trees The age of the clove plots, determined according to the year in which the first clove trees were planted, was estimated roughly by interviewing farmers. In the light of the difficulty in obtaining accurate information regarding the age of the older clove trees in the stand, we simply distinguished, through questioning, the clove trees aged over 15 years from those between 3 and 15 years. We continued the analysis using DBH of clove tree trunks, concentrating on the largest trunk for multi-stemmed individuals, which account for 33. 3% of the clove trees in our entire sample. This was the variable we selected as the age indicator of the clove trees in the stand. A data set produced by the Centre technique horticole de Tamatave (CTHT) indicating the DBH of forty clove trees aged between 6 and 50 years, completed by  Arimalala et al.
2015 , HEX F. and R. : Marquina et al. 2019) , to overcome the problem of the lack of primer universality among arthropods. Error-proof indices for individual sample identification were developed with the high-throughput sequencing process described by  Martin (2019). The libraries were sequenced with Illumina technology, using a Miseq 2x250 run for date 1 (April), and one lane of Hiseq 3000 each for dates 2 (May) and 3 (June). The markers for each sample were demultiplexed with CutAdapt v2. 3, and all paired-end reads were filtered for minimal length (280 bp), corrected for sequencing errors, and pairs of overlapping reads were merged with the Dada2 v1. 12 R package  (Callahan et al. , 2016). A matrix was thus obtained, containing samples as variables and amplicon variant sequences (ASVS) as observations.
A variant is a set of identical corrected and merged paired-end reads. We used Qiime2  (Bolyen et al. , 2018)  with a 2% divergence threshold, to merge ASVS, to decrease their number without the loss of taxonomic information. The summed number of reads for each merged variant for a given arthropod sample was reported as the intersection of samples and ASVS. Each ASVS was assigned, by BLAST, to a barcoding reference database of cytochrome oxidase subunit I (COI) nucleotide sequences (658 bp) compiled from three different sources and curated by expert analysis. These reference barcodes were retrieved from BOLDSYSTEM  (Ratnasingham and Hebert, 2007) , the CBGP -Continental Arthropod collection (Centre de Biologie pour la Gestion des Population, 2019) and a local database specifically designed for this study.
Our database contained barcodes of the most frequently encountered species during this sampling campaign (extra-specimens collected) and field surveys (2017-2020) conducted through Europe and North Africa for the search of S. oleraceus natural enemies (see below). In total, these three sources compiled 1 699 995 sequences from 119 299 species available for ASVS assignation. We retained successful assignments to the ranks of species, genus and family, but not those to higher taxonomic levels, because arthropod biology is too variable at higher taxonomic ranks to be informative for our purpose. The assignments obtained for each marker were grouped together in a single table and the numbers of reads were summed by assigned taxon. The resulting file was therefore an interaction matrix in BIOM file format, in which the assigned taxa replaced ASVS.
Assessment of sampling robustness and global network description We first evaluated the completeness of sampling over the entire sampling campaign, and generated taxon accumulation curves (the 57 quadrats were added in a random order, with 1,000 permutations) for plants and arthropods, using the specaccum function of the R package vegan  (Oksanen et al. , 2019). We estimated the extrapolated taxonomic richness by calculating the Chao 1 index  (Chao, 1984)  with the specpool function. Likewise, the robustness of sampling for the characterization of interactions was assessed by generating accumulation curves for pairwise interactions. We first generated an accumulation curve including all the types of direct interactions (e. g. plant-herbivores, herbivores-natural enemies, etc. ) present in the meta-network (i.
e. pooling interactions from all sites). The 57 quadrats were added in a random order, with 1000 permutations. We finally generated a curve focusing on interactions involving S. oleraceus as a source, to evaluate the performance of the sampling design for addressing our objective of establishing an inventory of the arthropods feeding on S. oleraceus, corresponding to candidate BCA. For both curves, we estimated the extrapolated interaction richness with the Chao 1 index  (Chao 1984) , using the specpool function. Prior to interaction analyses, a global description of the meta-network (pooling interactions data from all sites) and subnetwork (centred on S. oleraceus related interactions) was performed. Several metrics were calculated: the number of links (L), the number of nodes (S) (connected and isolated), connectance (C) and link density (LD)  (Bersier et al.
, 2016). Arthropods were ordered by the increasing generality values (i. e. the number of resources per taxon) characterizing ecological host range. To assess and visualize the dependence of natural enemies on these herbivores, we constructed a second level grid matrix in connexion with the previous, and calculated arthropod vulnerability values (i. e. the number of consumer per taxon). Multipartite network and grid matrices were constructed with igraph R package. Assessing the contribution of the approach for the biocontrol program To discuss the contribution of the method herein proposed, we used, as a point of reference, a survey performed following classical procedures (sampling, rearing and identification of specimens exclusively collected from S. oleraceus) in the frame of this CBC program  (Lesieur et al.
Summary of the molecular results In total, 2,834 arthropod specimens were collected and analyzed by metabarcoding, to reconstruct the interaction network at a global scale. We obtained DNA sequences and taxonomic assignments for 1,803 of the 2,834 arthropods initially collected  (63. 6 %). This proportion of exploitable information reached 71% (2,011 specimens) after manual validation of the matrix. The molecular analysis provided a total of 107,483,410 reads, 19. 2% of which were retained after screening with quality filters; we obtained a final dataset of 2,014 COI variant sequences (Supplementary Table  2 ). Before, manual validation, we observed that a large proportion of the diversity (33% of the families and 40% of the species) was recovered by the use of all markers, the rest being recovered by a combination of two markers, or specifically found with only one marker (Supplementary Figure  S3 ).
One species from Cynipidae is a known stem galler of S. oleraceus: Aulacidea follioti Barbotin (Bladmineerders Online database, 2020). However, this species is not yet present in any of the barcoding databases used here and could therefore only be assigned to family level. Further prospections to collect other Cynipidae specimens and rear them to adulthood would be required to confirm this identification. Second criterion: limited interactions with natural enemies By using metabarcoding to reconstruct interactions between arthropods, we were able to detect a wide range of parasitoids from their herbivore hosts, and some predators. We detected omnivorous dietary behavior in several groups from Heteroptera. Opportunistic predation through carnivory is common in Lygaeidae  (Burdfield-Steel and Shuker, 2014)  to supplement the low levels of protein supplied by plants.
Supplementary Texts Text 1: Curation and transformation of the interaction matrix In order to produce an interaction matrix, in which observations are resources and variables are consumers, the original dataset should be checked. First, for each sample, the taxa having the highest number of reads were set as the consumer. This assumption was confirmed in most of the cases (the specimen collected in the tube was the consumer and produced the highest number of reads). However due to the simultaneous detection of possible preys and parasitoids from the specimen sampled and the contrasted amplification rates between taxonomic groups  (Alberdi et al. 2018) , this was not always the case. Consequently, a second step of data manual validation was required on each sample (Supplementary Figure  S2 ) in order to 1) verify whether the consumer was correctly defined according to the observation from the field, 2) add the source plant for herbivorous arthropods, 3) define the status of the taxa simultaneously amplified with the consumer (prey, parasitoid or contamination).
The next step consisted in removing cannibalistic interactions (i. e. delete reads at the intersection of a same source and consumer species). Indeed, DNA-based methods are not suitable to disentangle predator and prey sequences  (Taberlet et al. 2018). As metabarcoding neither provides quantitative information about the trophic links  (Deagle et al. 2019) , the numbers of reads were converted to binary information, then occurrence frequencies of interactions between a pair of consumer/prey species were calculated as the percentage of consumer samples positive for the prey over the total number of consumer sampled in the dataset. All analyses were carried out under R version 3. 5. 2 (R Core Team 2018).
Acknowledgments We are particularly grateful to  A Coeur  d'acier,  G Delvare ,  B Derepas ,  J Harran ,  B Michel ,  A Migeon ,  E Pierre ,  JM Ramel  and  JC Streito  for providing their taxonomic expertise for arthropod identification for construction of the local database. We also thank  G Fried  for assistance with the identification of some plant specimens. We also warmly thank all our coworkers involved in field sampling:  P Audiot ,  M Corbin ,  R Guilhot ,  A Loiseau ,  L Olazcuaga ,  T Thomann , and  N Vieira. This project is supported by funding from the  Australian Government Department of Agriculture and Water Resources , as part of its  Rural R&D for Profit programme , through  AgriFutures Australia (Rural Industries Research and Development Corporation)  ( PRJ--010527 ).
Flower bud galling/seed feeding _ S -SO Oligophagous (White, 1988) Tephritidae Campiglossa producta (Loew, 1844) Flower bud galling/seed feeding Yes S -C Tephritidae Ensina sonchi (Linnaeus, 1767) Flower bud galling/seed feeding Yes S -S Oligophagous (White, 1988) Tephritidae Tephritis sp. Flower bud galling/seed feeding _ G Tephritidae Tephritis cometa (Loew, 1840) Flower bud galling/seed feeding No S -SO Oligophagous (Bladmineerders Online database, 2020) Tephritidae Tephritis formosa (Loew, 1844) Flower bud galling/seed feeding Yes S -C.
For all of these patients, a complete stool culture was performed and results were all negative for Yersinia sp, Shigella sp, Salmonella sp and Campylobacter spp other than C. jejuni and C. coli. In addition, no other differential diagnosis was made. The detection of DNA or antigen does not necessarily prejudge the viability and infectivity of the organism found but these clinical data reinforce the probability that the intestinal infection is indeed related to a Campylobacter and not due to an overly sensitive test as can be the case with some molecular tests  [18]. Our study confirmed the work performed by Schnee et al. who showed that CAMPYLOBACTER QUIK CHEK ™ is relevant for clinical practice. They evaluated the performance of this test on diarrheal stools from children aged 0-24 months, living in Bangladesh.
Overall, the TEdenovo pipeline reconstructed 28,009 consensus sequences. After manual curation (Supplementary File S1. 6), the MCTE library ended up with 165 consensus sequences, which are 34 more sequences than the ones present in the Berkeley Drosophila Genome Project (BDGP) dataset for D. melanogaster  (Kaminker et al. 2002)  (Table  S5 ). The MCTE library sequences are representative of 146 TE families (13 of them represented by more than one consensus sequence), including three new families (see below). The second step of the annotation process used the TEannot pipeline of REPET to annotate all the TEs present in each one of the 32 genomes and the reference ISO1 genome using the MCTE library. The euchromatic region analyzed ranged from 100. 1Mb to 103. 9Mb (Table  1 ), which is a slightly larger region than previous similar analysis (e.
melanogaster, two of them also present in other Drosophila species Three consensus sequences in the MCTE library that failed to be assigned to any known family in the BDGP or the RepBase database were further analyzed using PASTEC  (Hoede et al. 2014). These new consensus sequences were classified as a Miniature Inverted Repeat Transposable Element (MITE), a Terminal Repeat Retrotransposons in Miniature (TRIM), and a Terminal Inverted Repeat (TIR) element (Figure  3A ). Numerous Bari-like MITEs  (Palazzo et al. 2016)  and Mariner-like MITEs  (Wallau et al. 2014 ) have been previously described in D. melanogaster. However, the MITE consensus sequence showed no significant alignments with any previously described MITEs (nucleotide identity percentage < 50%), suggesting that they belong to a new undescribed MITE family.
On average, more than eight MITE copies were found in each D. melanogaster strain. Identified copies were of variable length (Figure  S4A ) and highly similar (average identity >89%, Figure  S5 ). Moreover, the consensus sequence of the new MITE family showed no significant similarities with TEs identified in other five Drosophila genomes (Table  S7 , Supplementary File S1. 8), suggesting that this element could have invaded the D. melanogaster genome recently. Regarding the new TRIM element, while the consensus sequence showed the typical TRIM structure (less than 1,000bp, with LTRs sequences between 100bp -250bp, Figure  3A ), no similarities with any known TE in public databases was found. Notably, this sequence was not the only TRIM element in the MCTE library since other TRIM consensus sequence showing similarities with a Kepler element was also found.
10). The total number of TE insertions detected by each software was more similar for REPET and TEMP (6,632 and 7,430, respectively) than for TIDAL (9,066) (Table  S8A ). The number of TE insertions detected both by REPET and TIDAL (4,041) is higher that the number of TE insertions detected by  REPET and TEMP (3, 254). The overlap of the insertions detected both by TIDAL and TEMP is higher (4,786), probably because the methodologies of these two software are more similar (Table  S8A ). To estimate the false negative rate of TEMP and TIDAL and the false positive rate of REPET, we performed manual inspection for 300 TE insertions annotated by REPET. When comparing the TE annotations between REPET and TEMP, 120 TEs (40%) were correctly annotated by the two software, while 170 (57%) TEs annotated by REPET were missed by TEMP (Table  S8B ).
When comparing REPET and TIDAL annotations, 212 TEs (71%) were correctly annotated by the two software, while 78 TEs (26%) were correctly annotated by REPET and missed by TIDAL (Table  S8B ). Finally, 10 of the 300 TEs annotated by REPET, were false positives as we could not confirm their presence using Blast (see Material and Methods). Additionally, we performed manual inspection of 50 TEs that were identified by TEMP/TIDAL but were not identified by REPET (Table  S8C ). None of these insertions were present in the genome assemblies. For these TEs, we could not distinguish whether they were REPET false negatives or TEMP/TIDAL false positives. However, the majority of these insertions (39/50) have a frequency estimate <20% according to TEMP, suggesting that they could be false positives  (Zhuang et al 2014).
For the 11 TEs with frequencies >20% we cannot discard that these correspond to REPET false negatives as REPET is run on the assembled genomes that contain a single haplotype, while software based on short-reads allow the interrogation of all the haplotypes present in a given sample (Table  S8C ). Thus overall and depending on the tool, short-read tools fail to annotate 26% to 57% of the TEs annotated using long-read tools, while REPET false positive rate was 3%.
Hundreds of de novo annotated TEs are associated with the expression of nearby genes To determine whether TE insertions were associated with the level of expression of nearby genes, we looked for significant associations between cis-eQTLs and TE insertions using RNA-Seq data available for 20 of the strains in our dataset (Table  1 , Table  S2C ). We focused on TE insertions located in high recombination regions as those insertions are more likely to be causal mutations. We identified 503 significant associations (adjusted p-value <0. 05), including 481 genes and 472 TEs, the majority of them annotated in this work for the first time (470; Table  S12A ). Also, most of them (433 out of 472; 91. 7%) were present at low frequencies in populations (£ 5%) suggesting that their effect on gene expression could be deleterious.
2002; Schmidt et al. 2010). We discarded the presence of other structural variants linked to our 18 candidate adaptive TEs that could also be driving positive selection (Table  S14 ). Moreover, our set of candidate adaptive TEs was enriched for signatures of selection compared with the whole dataset of TEs present at >5% population frequency (the minimum frequency required to calculate the selection statistics; X 2 test, p-value = 0. 0081). Given the small number of genomes analyzed, strong selection appears to be acting on these 18 insertions as exemplified by the Accord insertion  (Daborn et al. 2002; Schmidt et al. 2010). However, further functional validation is needed before arriving at any conclusive evidence on the functional role of these TEs. Note that for one of these 18 insertions, we found significant association with the level of expression of the nearby gene in whole-body non-stress conditions (Figure  6 ).
1). PacBio libraries were prepared using 20Kb SMRTbell and were delivered to Macrogen Inc. Korea to be sequenced using the PacBio RSII System. ONT libraries were constructed using the Ligation Sequencing Kit (SQK-LSK108 or SQK-LSK109) following manufacturer's instructions (Table  S2 ; Supplementary File S1. 1) and were sequenced in house using the MinION device. Basecalling of ONT reads was performed using the Albacore Sequencing Pipeline Software (v. 2. 2). The quality of the long-read sequencing was assessed using NanoPlot (v. 1. 19) (De Coster et al. 2018).
Genome assemblies We performed de novo genome assembly of the 32 strains sequenced with long-read sequencing technologies. For PacBio sequences, we used Canu (v. 1. 7) (Koren et al. 2017)  for building draft genome assemblies followed by FinisherSC (v. 2. 1)  (Lam et al. 2015)  for improving contig continuity. We then aligned PacBio reads to the draft assembly using pbalign (SMRT Link v. 5. 0. 1) and used quiver (SMRT Link v. 5. 0. 1) to obtain the consensus sequences (polished assembly). PacBio-related programs were all run using default parameters (Figure  S12A ). For ONT genomes, we also started with Canu (v. 1. 7) (Koren et al. 2017)  with default options for building raw de novo assemblies. We then applied Racon (v. 1. 0) (Vaser et al. 2017) , Nanopolish (v. 0. 10. 1) (https://github.