dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported in part by the following projects:  Italian National  Project  PRIN MIUR 2010-2011  "Past Excess CO 2 worlds: biota responses to extreme warmth and ocean acidification" (F. B. and J. S. );  Polish-Norwegian Research Programme  operated by the  National Centre for Research and Development  under the Norwegian Financial Mechanism 2009-2014 in the frame of Project Contract No. Pol-Nor/196260/81/2013 (J. S. );  European Research Council Advanced  Grant  246749  (BIOCARB) (AM), and  MNHN   ATM 'Biomineralization' program  (A. M. and I. D. -C. ). We thank  Jill Darrell  (NHM), and  Marco Taviani  ( ISMAR, CNR ) for loaning supplementary specimens used in this study. The manuscript benefited from the thoughtful reviews of  Alberto Perez-Huerta  ( The University of Alabama, U.
Thermal cycling conditions were as follows: 95°C for 5 min, 45 cycles at 95°C for 10 s, 60°C for 15 s, and 40°C for 10 s. Data were acquired on the BioMark Real-Time PCR system and analyzed using the Fluidigm Real-Time PCR Analysis software to obtain crossing point (CP) values. Assays were performed in duplicate and two negative water controls were included per chip.
Validation of B. henselae prevalence by conventional PCR and sequencing Conventional PCR using primers targeting the Bartonella spp. gltA gene were used to confirm the presence of B. henselae DNA in tick samples. Amplicons were sequenced by Eurofins MWG Operon (Germany), and then assembled using BioEdit software (Ibis Biosciences, Carlsbad). An online Blast (National Center for Biotechnology Information) was used to compare results with published sequences listed in GenBank.
Detection of associations between pathogens and symbionts in ticks The statistical likelihood of all possible combinations of pathogens and/or symbionts detectable in this study was analyzed via an association screening approach as described previously  [56]. Briefly, the association screening approach comprises a statistical test based on a simulated theoretical distribution of a statistic and its associated confidence interval, under the null hypothesis H0, that pathogen associations are random. The occurrence (i. e. counts) for all possible combinations was theoretically simulated for each pathogen combination, and each combination was unique. The 'envelope' function from the 'boot' package in R software was used to estimate the 95% confidence envelope for the combination count distribution profile, simultaneously including all infection patterns.
Environmental drivers of infection We investigated the effects of environmentally-linked variables (habitat and locality) on the local prevalence of microorganisms (either pathogens or symbionts) within tick populations. Statistical logistic regressions were performed with the R statistical platform using the package MuMIn v. 1. 7. 2 and lme4, with prevalence as the dependent variable, and habitat (forest vs. hedges), and sampling site (nested within habitat) as fixed variables. Model selection was performed using Akaike's Information Criterion (AIC)  [57]. The model with the lowest AIC value was viewed as the most parsimonious, i. e. the model which explains the majority of variance with the fewest parameters  [57].
This study was partially funded by the  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext 421 (http://www. edenext. eu). This work was also supported by the  MEM   Metaprogram of the INRA , the  COST Action TD1303 (EurNegVec)  and the  CoVetLAb (ANSES, DTU ,  CVI ,  SVA, APHA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Number of moth sequences in populations of the baculovirus AcMNPV The first AcMNPV genomic dataset we analyzed was generated by sequencing the 134-kb AcMNPV genome at 187,536X average depth after in vivo amplification of the virus in T. ni larvae (G0 in S1 Fig). The viral population that produced this dataset was then independently passaged in ten lines of T. ni larvae and ten lines of S. exigua larvae, each line consisting of ten successive infection cycles (G10 in S1 Fig). OBs recovered from the last infection cycle of each line were sequenced at between 9,211X and 33,783X average depth for the ten T. ni lines (total depth = 145,386X) and between 3,497X and 35,434X average depth for the ten S. exigua lines (total depth = 163,610X). Viral sequencing reads were used as queries to perform Blastn searches against sequences from both moth species.
Host sequences included RNAseq data corresponding to 70,322 T. ni contigs and 96,675 S. exigua contigs  [13, 14] , as well as 469 and 486 contigs from T. ni and S. exigua, respectively, that were assembled in this study using sequencing reads that did not map onto the AcMNPV genome (see methods). All viral reads aligning to moth contigs were then used as queries for Blastn searches against the AcMNPV consensus genome  [9]  to identify chimeric reads (i. e. sequences containing both AcMNPV and moth DNA), as evidence of junctions between host and viral DNA. After applying various filters to eliminate false positives, we extracted a total of 27,504 chimeric reads from all 21 AcMNPV genomic datasets. Chimeric reads were identified in the initial AcMNPV population from T. ni (n = 9,464), as well as in all ten T.
Nature of moth sequences integrated into AcMNPV genomes The 27,504 host-virus DNA junctions involved 38 T. ni and 48 S. exigua contigs (S1 Table ; S2 Dataset). Similarity searches and structure analyses revealed that 69 of these contigs are TEs (29 in T. ni and 40 in S. exigua) belonging to both major groups of eukaryote TEs (Table  1 ; S1 Table ): retrotransposons (three superfamilies) and DNA transposons (10 superfamilies). The remaining 17 contigs did not show any sequence or structural similarity to any known TE or protein. However, their closest Blastn hits in the GenBank whole-genome sequence database were found in Lepidoptera, suggesting these 17 contigs indeed originate from the host genomes. The large proportion of TEs among the host contigs found to be joined to viral DNA may indicate that transposition is the main mechanism of insertion of host DNA into viral genomes.
The relatively high frequencies of AcMNPV genomes carrying host DNA fragments at any given time raise the question of whether such host sequences are inherited over viral infection cycles. We thus tested the residual presence of T. ni sequences (inserted at G0 in S1 Fig) in viral populations, that had subsequently been passaged 10 times in S. exigua (G10 datasets in S1 Fig). Using the libraries from S. exigua G10 viruses as Blastn queries against T. ni contigs revealed 24 new insertions that were not previously found in Blastn searches against S. exigua contigs (see S1 Text for details). These insertions likely involve S. exigua sequences homologous to T. ni but absent from the S. exigua contigs. None of these insertions were identical (in terms of position in the virus genome and host contig) to any found in the G0 virus population.
Virus-mediated horizontal transfer of moth transposable elements Under the hypothesis that viruses can act as vectors of HT of TEs, once inserted in a viral genome, viral-borne TEs should then be able to jump from the viral genome to the genome of a new host organism. To evaluate the possibility that AcMNPV can shuttle TEs between insects, we first checked whether some TEs found integrated in our AcMNPV genome datasets have retained the structural features necessary for transposition. Among the 41 contigs inserted in at least 10 different viral sites, 11 correspond to TEs for which we recovered both TIRs and that encode a putative full-length intact transposase gene (Fig  1 ; S3 Fig). Provided that these TEs can be transcribed in a new host, they should thus be able to jump from the viral genome into the genome of this new host.
However, it is important to note that the transfer would only be effective if the host survived viral infection in the first place. This is more likely to happen if the host shows resistance to the virus or if the virus harbors deleterious mutations. We then reasoned that if AcMNPV is able to act as vector of HT of TEs between its insect hosts, the TEs we found integrated in the AcMNPV genome populations might have been horizontally transferred relatively recently between insects of various susceptibility to AcMNPV. To test this hypothesis, we assessed whether some of the TEs uncovered in this study have been horizontally transferred between T. ni and/or S. exigua and other insect lineages. We used the 69 TE sequences as queries to perform Blastn searches against the 144 non-Noctuidae insect genomes available in GenBank as of March 2015.
Viral population genomics data sets The GenBank accession numbers of the 21 AcMNPV genomic datasets analyzed in this study are: SRS533250, SRS534469, SRS534534, SRS534575, SRS534677, SRS534587, SRS534590, SRS534631, SRS534673, SRS536572, SRS536571 and SRS534470, SRS534499, SRS534514, SRS534536, SRS534537, SRS534543, SRS534542, SRS536937, SRS534588 and SRS534589  [23]. They consist of 101-bp paired sequences (reads), except for dataset SRS533250, which consists in 151-bp paired reads. These datasets were produced through experimental evolution, which consisted in generating ten per os infection cycles on ten lines of T. ni and S. exigua larvae using 2500 occlusion bodies from an AcMNPV stock derived from a viral sample originally isolated from a single Alfalfa looper (Autographa californica) individual collected in the field.
The full experiment is described in Gilbert, Chateigner  [23]  and in S1 Fig. The  AcMNPV DNA samples used to produce the 21 sequencing datasets were all extracted using the QIAamp DNA Mini Kit (Qiagen) after purification of AcMNPV occlusion bodies by a percoll-sucrose gradient, Na 2 CO 3 dissolution and enzymatic removal of host DNA  [23]. Data analyses were performed in R  [50] , unless another tool is mentioned. All Blastn searches were carried out under default settings.
Assembly of non-viral sequences To identify host DNA sequences integrated in genomes of the AcMNPV baculovirus, we used viral reads as queries to perform Blastn searches on T. ni and S. exigua transcripts generated by Pascual, Jakubowska  [51]  and Chen, Zhong  [52]. In addition, to recover as many insertions of host DNA as possible, we assembled non-viral DNA elements present in the viral genomic libraries. These elements may represent inserted host DNA sequences absent from (or incomplete in) the available transcriptomes of both moth species. We applied the following procedure on genomic libraries obtained from each moth species. We aligned all reads on the AcMNPV genome using the end-to-end mapping strategy of Bowtie 2  [53]. We used Samtools view on resulting alignment files to extract read pairs for which at least one read did not align.
5  [56] , allowing a maximum mismatch of 10% in overlapping regions. This yielded 469 contigs for genomic libraries generated from T. ni lines and 486 contigs for S. exigua lines. These contig sequences were added to known transcriptome sequence of the corresponding moth species  [51, 52] , which we hereafter simply refer to as "transcripts", in order to constitute the host databases for the Blastn searches designed to identify junction between moth and viral DNA.
Detection of junctions between host and virus DNA Blastn searches with default parameters  [57]  were carried out using the 21 AcMNPV genomic datasets as queries to identify similarities of at least 28 nucleotides (as defined by the default settings) between reads obtained from each viral line and the sequence database corresponding to its host. Each read showing similarities was then blasted against the AcMNPV reference genome, together with the other read of its pair (mate) so as to detect junctions occurring between paired reads. For a given read listed in a blast output, we retained the alignment with the best score, randomly choosing between alignments of identical scores. This selection was done separately for alignments with transcripts and for alignments with contigs, in order to help selecting between homologous contigs and transcripts (see below).
Selection of host sequences We discarded all alignments (junctions) involving contigs or transcripts not meeting the following criteria. To ensure that a contig we assembled represented moth DNA, it had to be partly similar to a transcript of the corresponding host species, as determined by Blastn searches of contigs against host transcripts, or to align with at least one read of a pair that also aligned with a known host transcript, as determined by the Blastn search of reads against the contig and transcript databases. Because the contigs we assembled are, as expected, partly similar to some host transcripts, a chimeric read may have similarities to a contig and to a transcript (after selecting the best alignment in each category, as explained above). In other words, contigs and transcripts can be candidates for the same insertions.
Characterization of junctions and insertion types For simplification, we hereafter refer to contigs assembled in this study and to previously assembled host transcripts as "contigs". We identified each junction producing a chimeric read by the offset it involves between the virus genome coordinates and the host contig coordinates (S1 Text). Reads having the same offset, involving the same viral DNA strand, host contig, and coming from the same genomic library in the case of S. exigua lines (which do not share a ancestor on this host) were considered likely to come from viral amplification of the same original insertion of host DNA. In the following analyses, we selected only one chimeric read per original junction, favoring the read with best alignment score on the host contig. These reads were mapped onto their corresponding contig by inserting gaps of appropriate length before their sequence, based on alignment coordinates reported by blast, to produce multifasta alignment files.
Visualization of these files in Geneious  [56]  and BioEdit  [59]  (example shown in S7 Fig)  showed  that junctions clustered at one or two positions in a contig likely representing the end(s) of a TE. For many contigs, this was further supported by the presence of terminal inverted repeats (TIRs), which are typical of class II DNA transposons, the presence of long terminal repeats (LTRs), which are typical of LTR retrotransposons and by similarities to known TE protein motifs returned by blastx on the GenBank non-redundant protein database. For each cluster of at least 10 junctions, which likely represent insertions of the same TE end, we analyzed sequence conservation at insertion sites in the virus genome. This was done by computing insertion coordinates based on the previously obtained offset (S1 Text), and by building sequence conservation logos  [60] [61] [62]   To check whether these similarities were overall longer than expected by chance, we extracted from each chimeric read resulting from this type of junction the last 20 bp that aligned to the host contig (next to the junction point), and computed the lengths of similarities this 20-bp sequence had with 20 random 20-bp regions of the AcMNPV consensus genome.
Detection of horizontal transfers of transposable elements across insects We assessed whether T. ni and S. exigua TEs found integrated in the AcMNPV genome underwent HT between insects. We used the T. ni and S. exigua TEs we identified as queries to perform Blastn searches against the 144 non-Noctuidae insect whole genome sequences available in GenBank as of March 17 th 2015. We identified candidate HT events when a T. ni or S. exigua TE aligned to a sequence from another insect genome with at least 85% nucleotide similarity over at least 100 bp. To assess the level of neutral genetic distance expected under vertical inheritance between T. ni/S. exigua and all insect species in which we found Blastn hits meeting the above criteria, we calculated synonymous distances for 11 conserved genes between T.
(PDF) S7 Fig. Visualization of an alignment between a host contig and chimeric reads in geneious (modified). The black segments represent regions of mismatch between the reads and the contig and grey segments represent similarities. Note the clustering of reads at the contig ends and the fact that, for each end, the same part of the reads (right OR left) aligns with the host contig. The other reads scattered between the two ends do not represent junctions by transposition. (PDF) S8 Fig. Coordinates of alignments between a chimeric read and the virus genome and host transcriptome, as returned by blast. These were used to compute an offset that identifies a junction between host and virus DNA (see S1 Text). (PDF) S1 Dataset. Moth sequences found integrated into AcMNPV genomes. Contig names starting with "SEUC" are from Spodoptera exigua while those starting with "GBKU" are from Trichoplusia ni.
(XLSX) S2 Dataset. Blastn score and coordinates of all 27,504 chimeric reads on the AcMNPV genome and on moth contigs. The "lineage" column indicates which population genomics dataset a given chimeric reads belongs to (TniG0: Trichoplusia ni G0; Tni1-10: Trichoplusia ni G10; Spodo1-10: Spodoptera exigua G10; see S1 Fig). The column "chimera_type" indicates whether the host-virus junction identified in a chimeric read pair lies within a read (intra-read) or between the two mates of a pair (inter-read). "Score" in column names refer to blast scores of reads against virus or host sequences. Sv, Srv, Erc, Ec, Ev, Src and Sc are positions of starts and ends of alignments returned by Blastn, as illustrated in S8 Fig. Asterisks in the "PCR" column indicate host-virus junctions that we were able to recover by PCR/Sanger sequencing.
Other sequences corresponding to mites from humans, raccoon dogs, foxes, jackal and dogs from various geographical areas were retrieved from GenBank. Phylogenetic analyses were performed using the Otodectes cynotis cox1 sequence as outgroup. Maximum Likelihood and Bayesian Inference analysis approaches were used. To visualize the relationship between the haplotypes, a median joining haplotype network was constructed using Network v4. 6 according to host. Results: Twenty-one haplotypes were observed among mites collected from five different host species, including humans and canids from nine geographical areas. The phylogenetic trees based on Maximum Likelihood and Bayesian Inference analyses showed similar topologies with few differences in node support values. The results were not consistent with a human origin of S.
Sequence and phylogenetic analyses The PCR-amplified products of 400 bp were purified and directly sequenced. The Otodectes cynotis cox1 sequence (KF891933) was retrieved from GenBank. Multiple sequence alignments of nucleotide sequences in this study and sequences available from GenBank (n = 81) were generated using MAFFT v. 6. 951. The dataset was analyzed with Maximum Likelihood using MEGA5 and RAxML-HPC v7. 0. 4 under General Time-Reversible (GTR + G) model and Bayesian Inference analysis. Support of internal branches was evaluated by non-parametric bootstrapping with 500 replicates. Bayesian Inference analysis was performed with MrBayes v. 3. 2. 1 conducting in two simultaneous runs with four parallel Markov chains (one cold and three heated) for 1 million generations, sampling every 1000 generations and discarding the first 25 % of samples as burn-in.
Potential Scale Reduction Factor approached 1. 0 and average of split frequencies under 0. 01 were used for examining convergence. All trees were visualized using FigTree with Otodectes cynotis as outgroup (http://tree. bio. ed. ac. uk/software/figtree). To visualize the relationships between haplotypes, a median joining haplotype network of cox1 sequence was constructed using Network v4. 6 according to host.
Results The sequences of cox1 fragment were obtained in mites from nine dogs and three foxes (Table  1 ). All sequences were deposited [GenBank: KT961021-KT961032]. Other sequences corresponding to 50 mites from humans, raccoon dogs (Nyctereutes procyonoides) (n = 6), fox (n = 1), jackal (Canis aureus) (n = 1) and domestic dogs (n = 11) and from various geographical areas were retrieved from GenBank and from a previous study (Table  1 ). All of the successfully sequenced samples were assigned to only one haplotype. In all, 21 haplotypes were observed among mites collected from five different host species, including humans and canids, and nine geographical areas (Table  1 ). Seven haplotypes were observed among mites collected in humans (H12-H18); two haplotypes were shared with mites collected from canids and human (H3 and H11) and 12 haplotypes (H1-H2, H4-H10, H20-H21) were observed among mites collected from canids.
According to the historical hypothesis, behavioral transmission between humans and dogs occurred when humans domesticated various species of animals at the beginning of agriculture and sedentarization  [3]. The origin of the domestic dog is still debated. Recent data indicate that domestic dogs evolved from a group of wolves that came into contact with hunter-gatherers between 18,800 and 32,100 years ago  [31]. Those data contradict the historical hypothesis as agriculture was developed later, around 11,500 years ago. We included all the cox1 nucleotide sequences of S. scabiei available in GenBank that were from canids and from all human mites sharing the same clade as canid mites in published phylogenetic studies (Table  1 ). Cox1 gene, including a very high number of polymorphisms, was found to be valid and best suited for this type of phylogenetic analysis according to previous studies on the same topic  [32, 33].
A search on the Web of Science TM (http://apps. webofknowledge. com) database as per January 2016 returned 767 references for the expression [deep root* and plant] with citations increasing from <100 to about 2500 per year from 1995 to 2015 (h-index 74); 411 references for the expression [deep root* and tree], with citations increasing from <26 to > 1100 per year from 1995 to 2015 (h-index 55); and 276 references for the expression ['deep rooting'], with citations increasing from <25 to nearly 900 per year from 1995 to  2015 (h-index 49). By comparison, searching the expressions ['plant root*'] and ['fine root*'] yielded >48 000 and 8200 references, respectively. Current research efforts allocated to the study of deep roots remain incommensurate with those devoted to shallow roots, leaving a wide array of fundamental questions regarding deep roots underinvestigated and unresolved, notwithstanding the growing realization that deep roots may be a key functional element of the Critical Zone  (Richter and Billings, 2015).
THE CHALLENGE OF DEFINING DEEP ROOTS Defining deep roots is challenging. Compiling a large database of published root profiles encompassing virtually all terrestrial biomes,  Schenk and Jackson (2002)  ascertained that the median depth of root profiles was 0Á88 m. Based on this value,  Maeght et al. (2013)  proposed that 'deep roots' be defined as roots growing at soil depths of at least 1 m. Applying such a universal 1 m threshold to all vegetation types in all climates and soil types can of course be seen as highly controversial, as it can be argued among a number of other criticisms that (1) perennials and annuals do not stand on an equal basis regarding their potential to explore large volumes of soils; (2) according to the principle of allometry, plants with smaller/shorter aerial parts may often have a lower potential for deep rooting than larger plants; and (3) environmental conditions certainly affect the deep rooting potential of all plant species, in a way not dissimilar to the so-called bonsai effect observed in aerial parts of plants  (Passioura, 2002).