dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA reference databases Six DNA reference databases (Fasta format) with different taxonomic resolution were constructed for DNA identification: a) Insecta mitogenomes, obtained by downloading all available (at November 2013) insect mitogenomes of 588 species from GenBank (Table A in S1 File), supplemented by sequencing the mitogenomes of 17 potential prey species (Tables B and C in S1 File) with long-range PCR or shotgun methodology  [31, 34, 35] ; b) cytochrome oxidase 1 (cox1) barcode sequences of 58,367 Insecta species using a MegaBLAST based pipeline implemented by Hunt et al. [36]  and adapted by Srivathsan et al. [22, 37] ; c) aphid nuclear genomes obtained from AphidBase (http://www. aphidbase. com/) consisting of the preliminary genome assemblies of Myzus persicae and Aphis gossypii, and the complete nuclear genome of Acyrthosiphon pisum (assembly Acyr_2.
0; placed and unplaced scaffolds; GenBank Assembly ID: GCA_000142985. 2); d) parasitoid barcode sequences (ITS, rRNA genes, elongation factor, and cytb) from known parasitoid genera of the predators and aphids available at GenBank; e) rRNA genes containing data on 3,000,000 bacteria, 150,000 archaea, and 250,000 eukaryote sequences obtained from the SILVA rRNA database (release 115)  [38] ; f) genomes of bacteria genera known to include insect endosymbionts  [31] : Arsenophonus, Blattabacterium, Buchnera, Cardinium, Hamiltonella, Midichloria, Nosema, Regiella, Rickettsia, Rickettsiella, Serratia, Spiroplasma and Wolbachia.
Prey and other foreign DNA detection After quality control, reads from each predator gut content dataset were matched against the DNA reference databases using BLASTn v2. 2. 27+ (E-value <1e-5; maximum target sequences 3; no dust) (for Insecta mitogenome, parasitoids, and rRNA databases) and MegaBLAST 2. 2. 27 + (E-value <1e-9) (for cox1, aphid genomes and bacterial genome databases)  [39]. The matched reads were filtered for minimum overlap length of 225 bp and identity of 95% (for the bacterial genome database), 98% (for the mitogenome database) and 99% (for all other reference databases) using a set of Python customized scripts to eliminate false taxon identifications as in Paula et al. [31]  and Srivathsan et al. [22, 37]  (https://github. com/asrivathsan/ readidentifier). The mapping region for each matched read was checked manually.
Reads matching to the following regions were discarded: a) control region of mitogenomes; b) nuclear repeat regions, such as short sequence repeats-SSR; c) Coccinellini rRNA because this region does not differentiate prey from predator. Using these rigorous criteria, a matched read was assigned to a taxon for the mitogenome, cox1, aphid genomes and bacterial genome databases. For the rRNA and parasitoid databases, taxon assignment was made to the lowest taxonomic level that could be reliably identified.
Prey and other foreign DNA detected in the predator guts The Illumina sequencing of the libraries of the four predator species generated >2 million reads per species after quality control. Several thousand reads matched across the mitogenome of the respective focal predator (Figures B-E in S1 File) ranging from 0. 07% to 0. 7% of total reads depending on the library (Table  1 ). An additional 15 to 239 reads per library were identified as insect mitochondrial DNA but foreign to the focal predator (Table  1 ). The highest number of foreign insect taxa identified based on these reads was found in Hi. convergens (n = 8, Table  2 ). For the other libraries, the numbers ranged from two to three, and were not related to the number of pooled predator specimens. In addition, non-insect DNA reads were found using the various databases (Table  1 ).
Doru luteipes had the highest richness of symbionts (nine genera), plants, fungi and non-symbiont bacteria, while Cy. sanguinea had the lowest. We did not detect foreign reads in the gut of the unfed recently emerged (control) H. axyridis, except for one widespread and ubiquitous bacterium (Table  1 ). The taxonomic assignment of the foreign DNA in the gut content of each predator is presented in Table  2 , along with the number of reads matched to each of the DNA reference databases. There were fewer matches and fewer species identified using the cox1 database than the mitogenome database, consistent with the ~20x greater size of the mitogenome and the greater number of target genes which were hit apparently randomly (Figures B-E in S1 File). In addition, to search for aphid prey we also used available whole-genome sequences to map the shotgun reads, which revealed the presence of Aphis gossypii in the H.
axyridis gut, consistent with the matches to the Insecta mitogenomes (Table  2 ). The most common identified prey were intraguild prey and aphids. Evidence of intraguild predation was detected in all predators. Harmonia axyridis was preyed upon by all other predators, whereas predation on Cy. sanguinea and Hi. convergens was not detected. Hippodamia convergens had the highest intraguild prey richness of three coccinellid species and one noncoccinellid predator (Orius insidiosus). Other extraguild prey detected were the lepidopterans Spodoptera frugiperda, Helicoverpa sp. , and Plutella xylostella, the parasitoid Di. coccinellae, and the stink bug Euschistus sp. Plutella xylostella had a population outbreak in the organic cabbage field where D. luteipes was collected. Non-prey foreign DNA was detected using the parasitoid, nuclear rRNA, and bacteria genome databases.
Parasitoids were detected in all predators using the parasitoid reference database. However, at the 99% identity threshold used here, the reads matched the conserved V5 region of 18S rRNA that was identical in closely related species. Consequently, we reported the taxonomic level at which the match provided a reliable taxonomic determination (families of Chalcidoidea and Aphidiinae in Braconidae). For aphid parasitoids, this was sufficient to distinguish hyperparasitoids from primary parasitoids and to identify two broad groups of primary parasitoids. Chalcidoidea parasitoids were detected in all coccinellids, while Aphidiinae were detected in all but Cy. sanguinea. The nuclear rRNA database revealed that all of the predators harbored a remarkable diversity of foreign DNA (Table  2 ). Reads matching Spermatophyta and fungal species (Streptophyta, Ascomycota, and Basidiomycota) were found in all of the species, but the dermapteran D.
luteipes had by far the highest number of these reads (Table  2 ). The abundance of plant DNA may be from indirect consumption because P. xylostella caterpillars are typically stuffed with largely undigested leaf tissue in their guts. The large quantity of fungal DNA may be associated with the scavenging behavior of this species. The bacteria genome database returned several bacterial species associated with insects, including facultative and obligate symbionts of aphids. Three aphid-specific symbionts, Hamiltonella, R. insecticola, and S. symbiotica, were detected in all four predators, but the strict obligate Buchnera, which is a powerful marker of aphid consumption immediately after feeding  [31] , was not detected in any of the samples. This probably happened because Buchnera DNA detection drops very fast after aphid consumption, while the other prey symbionts keep in the predator guts longer  [31].
coccinellae. The total insect trophic links identified for each focal predator using the Insecta mitogenome, cox1 and aphid genome databases was: two (plus one indirect link) for Cy. sanguinea, eight (plus one indirect) for Hi. convergens, two for H. axyridis, and three for D. luteipes, of which 93% were species or genus specific (Table  2 ).
[45] , neither of which were in the aphid genome or mitogenome reference databases, the probability of detection of aphid DNA was reduced. In the dermapteran D. luteipes the observed direct consumption of aphids was also confirmed by the indirect linkages of an Aphidius parasitoid and three aphid-specific symbionts. Intraguild predation was commonly detected and was asymmetrical. Hippodamia convergens fed on three other ladybirds, while no other predator species was detected in the gut of H. axyridis. In the temperate zone, H. axyridis is considered to be an aggressive intraguild predator and less commonly an intraguild prey  [46]. However, in this Brazilian system, it was commonly detected as the intraguild prey. Finally, the detection of the coccinellid parasitoid, Di. coccinellae, represents an interesting addition to the trophic network with its potential to regulate the species composition of coccinellid assemblages.
While we can plausibly assume that the Chalcidoidea and Aphidiinae parasitoids and symbionts of aphids (Table  2 ) were indirectly preyed upon through predation on their hosts, rather than direct predation on the free-living adults, Di. coccinellae pupates outside its host, and thus can be acquired by direct predation, rather than by feeding on a parasitized immature or adult coccinellid. The latter scenario seems unlikely because the parasitoid prefers adults  [47] , and intraguild predation is not common on adults  [48]. A third scenario is that one of the ladybird specimens used for the gut analysis was itself parasitized by Di. coccinellae, and traces of its non-degraded DNA (from live tissue) was transferred into the gut extraction. The key step of the DNA shotgun-sequencing analysis is the identification of reads as being foreign to the focal predator genome, which is established based on a stringent match to known barcode and genome sequences in the reference databases.
For this, important issues are the completeness of identifying foreign DNA (reducing false negatives, i. e. , identifying all of the foreign DNA in the predator gut) and the accurate taxonomic assignments of these DNA sequences (reducing false positives, i. e. , not misidentifying taxa). Regarding the issue of false negative risks, the foreign reads have to be detected among millions of others that are unidentified and presumably mostly originate from the predator nuclear genome. Thus, the taxonomic breadth and resolution of foreign DNA detection is directly related to the comprehensiveness of the DNA reference databases and the diagnostic power of the available genetic markers. Barcode and rRNA data were available in our reference databases for tens of thousands of species, but this may correspond to just a few percent of the total species in existence  [49].
Thus, many species represented in the shotgun read mixture may remain unrecognized for the lack of representation in the reference database. Genetic variation and highly degraded prey DNA also add to the probability of missing a species represented in the mixture. The use of the much more comprehensive cox1 database with 58,367 Insecta species reference sequences did not detect more or unique prey species compared to the mitogenome database (Table  3 ). This database differs from the mitogenome database in its much greater species representation, but unlike the mitogenome database it was not supplemented with the local pool and this may result in recovery of fewer prey species. Secondly, the ~20x shorter length of the cox1 barcode compared to mitogenome likely resulted in a lower recovery probability.
A greater taxonomic coverage of mitogenomes is expected to aid in particular the resolution of parasitoid identifications and the detection of more aphid reads. Parasitoid detection was mainly against the parasitoid database, a collection of widely sequenced genes for diverse aphid parasitoid species, but the reads only matched the conserved regions of the 18S rRNA gene, which were invariable across entire clades and so parasitoid identifications could not be at the species level (Table  2 ). The underrepresentation of mitochondrial and nuclear genomes of the aphid and parasitoid species in the DNA reference databases clearly limits the taxonomic resolution and the number of reads recovered. Where nuclear genome sequences available, the detection of a prey is expected with higher read numbers.
For example, Paula et al. [31]  detected Acyrthosiphon pisum in feeding trials using its nuclear genome at ~50x higher read numbers than by using the mitogenome. The predator gut content DNA datasets obtained here can be reanalyzed using a more comprehensive set of DNA reference databases to improve the taxonomic breadth and resolution of prey and other foreign species. In particular, investments in the construction of local mitogenome and draft nuclear genome databases of the species that share the habitat with the predators are needed because the single-locus cox1 barcode database provides comparatively low read coverage for read matching (Table  2 ). Regarding the risk of false positives, this is reduced because a high stringency of overlap length and sequence identity for taxonomic identification was applied, although this approach resulted in a low number of matching reads.
For example, when we filtered the Hi. convergens gut content dataset against the Insecta mitogenome database using 90% identity, we obtained 775 reads for 11 foreign species, instead of 198 reads for eight foreign species when using 98% identity (Table  1 ). In the three species eliminated, all of the reads mapped to the mitochondrial D-loop region. Most of the remaining eliminated reads also mapped to this region. The proportion of D-loop matching reads was unexpectedly high at a 90% identity threshold, and does not give confidence in a prey species assignment. The D-loop matches are probably spurious, perhaps resulting from the high AT content and possibly conserved tandem repeats characteristic of these regions that limit the power for species discrimination. Therefore, the 98% identity threshold for prey identification in the mitogenome database was appropriate to eliminate false positive identifications.
In a similar way, we establish identity thresholds for the other reference databases. Possible ways to address the issue of low number of matching reads could be increasing the sequencing depth and optimizing the library insert size. In our various libraries we used insert sizes between 300 and 900 bp and paired-end sequencing in some cases, but while this produces high-quality reads for secure species-level identification, it probably reduces the number of reads to be detected for the prey DNA in digestion in the gut of the predator. Degraded DNA in the gut is expected to be short (e. g. , 200 bp  [50, 51] ) depending of the elapsed time since ingestion. Consequently, long library insert size may discriminate against the ingested DNA in favor of the non-degraded DNA from live predator tissue.
Clarke et al. [17]  observed in silico that a set of widely used 'generic arthropod' cox1 metabarcoding primers only managed to recover 43 to 64% of the species in a known mixture of arthropod DNA. Ji et al. [56]  found that many cox1 metabarcoding identifications are reliable, but were only able to link the most commonly recovered foreign sequences to operational taxonomic units (OTUs), despite the large cox1 reference database. In addition, some of the reads can originate from contamination or chimeras  [13, 57]. Metabarcoding studies therefore use an arbitrary threshold for the minimum number of reads per sample (e. g. , discarding reads occurring less than four times, as in  [42] ). In conclusion, even with modest sampling effort for four arthropod generalist predators, this DNA shotgun-sequencing approach provided similar taxonomic breadth and higher taxonomic resolution for prey identification compared to other works using multiplex-PCR or metabarcoding on insect gut contents (Table  3 ).
The specific heat experiments were carried out by relaxation method on a 2. 2 mg single crystal, within the temperature range 0. 4-300 K, also employing the PPMS platform. For the neutron diffraction experiment a high-quality single crystal with an approximate volume of 27 mm 3 was selected. Measurements were carried out on VIP, a two-axis neutron diffractometer at the Laboratoire Léon Brillouin, CEA-Saclay, 53 using a neutron wavelength of 0. 84 Å. Data were collected at two temperatures: 1. 6 and 6 K. sample, 1 and the value 6. 605 Å reported recently for for powdered single crystals. 2 As can be inferred from Fig. S2 , the experimental PXRD pattern of our single-crystalline HoPdBi can be very well modeled with the MgAgAs-type crystal structure with the Ho atoms located at the crystallographic 4a (0, 0, 0) sites, the Pd atoms occupying the 4c (1/4, 1/4, 1/4) sites, and the Bi atoms placed at the 4b (1/2, 1/2, 1/2) sites.
4 and total glucose (free glucose+glucose obtained from glycogen hydrolysis) was measured using the same kit as before. Glycogen content was evaluated by subtracting free glucose levels. Total lipid content was determined in duplicate by the sulphophosphovanilun method described by  Barnes and Blackstock (1973)  using a fish oil standard (Sopropeche, Boulogne-sur-Mer, France) instead of cholesterol. Total protein content was also measured in duplicate by using the Kjeldahl method as for diet analysis. In silico analysis Orthologous pfkl and pklr genes in the rainbow trout genome  (Berthelot et al. , 2014)  were identified in the Oncorhynchus mykiss genome browser (Genoscope: http://www. genoscope. cns. fr/trout/) extracted from the SIGENAE database (http://www. sigenae. org) using the BLAST tool (all accession numbers are given in Table  2 ).
Sequences from other species were collected from the Ensembl Genome database (Ensembl release 77, October 2014: http://www. ensembl. org). The sequence analyser tool from the EMBL Pfam software (http://pfam. xfam. org) was used to confirm sequence identities. Genomicus software, v01. 01 (http://www. genomicus. biologie. ens. fr/genomicus-trout-01. 01/cgibin/search. pl) was used to confirm the identities of the pfkl genes. Phylogenetic analysis of pklr (Fig. 1 ) was performed using the MEGA package v6 software  (Tamura et al. , 2013) , as previously described  (Marandel et al. , 2015). The phylogenetic tree, based on deduced full-length amino acid sequences, was produced using the neighbour-joining (NJ) method and confirmed by the minimum evolution method (data not shown). The reliability of the inferred trees was estimated using the bootstrap method with 1000 replications.
The Petromyzon marinus pk (Ensembl Accession ENSPMAP00000000233) protein sequence was used to root the tree. The evolutionary distances were computed using the Poisson correction method  (Zuckerkandl, 1965)  and expressed in units of the number of amino acid substitutions per site. The analysis involved 10 amino acid sequences. All positions containing gaps and missing data were eliminated, leaving a total of 127 positions in the final dataset. New gene annotations were allocated according to ZFIN Nomenclature guidelines (http://zfin. org/). All sequences and deduced partial amino acids are given in Fig. S2.
Total RNA extraction and cDNA synthesis Relative gene expression was determined by quantitative real-time RT-PCR. Samples were homogenized using Precellys ® 24 (Bertin Technologies, Montigny-le-Bretonneux, France): (1) in 7 ml tubes containing Trizol reagent (Invitrogen, Carlsbad, CA, USA) and 2. 8 mm ceramic beads for 2×30 s, separated by 15 s, at 5500 rpm for oocytes and embryos (30 per sample and per spawn); and (2) in 2 ml tubes containing Trizol reagent (Invitrogen) and 2. 8 mm ceramic beads for 2×20 s, separated by 15 s, at 5500 rpm for alevins (1 alevin per tube, 6 alevins per spawn/diet for mixed and exogenous feeding fish). Luciferase control RNA (Promega), 10 pg per 1. 9 mg of embryo/alevin or oocyte, was added to each sample to allow for data normalization during early development as previously described  (Desvignes et al.
1. Phylogenetic analysis of Pklr (liver and red blood cell pyruvate kinase). Deduced amino acid sequences were aligned using MUSCLE software  (Edgar, 2004). Phylogenetic analysis was carried out using Molecular Evolutionary Genetics Analysis (MEGA) software v6. 0  (Tamura et al. , 2013). The phylogenetic tree was built using the neighbour-joining (NJ) method. The reliability of the inferred tree was estimated using the bootstrap method with 1000 replications. The lamprey pklr sequence (Petromyzon marinus, Ensembl ID: ENSPMAP00000000233) was used to root the tree. Protein accession numbers are given in parentheses. and 0. 76 µl DNAse/RNAse-free water (5 Prime GmbH, Hamburg, Germany). The PCR protocol was initiated at 95°C for 10 min for initial denaturation of the cDNA and hot-start Taq-polymerase activation, followed by 45 cycles of a two-step amplification program (15 s at 95°C; 10 s at 60°C).
RESULTS In silico analysis of rainbow trout pfkl and pklr genes Using the Genomicus software (v01. 01), three rainbow trout predicted pfkl genes were identified (Fig. S2 ), two of which on the scaffold 7584 (Genoscope accession no. GSONMG00009460001 and GSONMG00009459001) were syntenic to the zebrafish pfkla gene (Ensembl accession no. ENSDARG00000060504) and one of which, on the scaffold 8651 (Genoscope accession no. GSONMG00001975001), was syntenic to the pfklb zebrafish gene (Ensembl accession no. ENSDARG00000099755). Using the sequence analyser tool from the EMBL Pfam software (http:// pfam. xfam. org), we confirmed that these two rainbow trout sequences were related to a phosphofructokinase-encoding gene. Amino acid sequences deduced from these rainbow trout predicted genes were shorter than the corresponding zebrafish sequences and were thus not appropriate for phylogenetic analysis.
Indeed, scaffold 7584 sequences corresponded to exons 2-5 of the zebrafish pfkla (Fig. S3A ) and scaffold 8651 sequences corresponded to exons 15-17 of the zebrafish pfklb (Fig. S3B ), suggesting that these two scaffolds were too short to cover the lengths of these genes as a whole. This analysis was therefore complemented by investigating the EST (expressed sequence tags) rainbow trout Sigenae database (www. sigenae. org). One EST contig (FYV3OTN01AZR97. s. om. 10; Fig. S3A ) was shown to correspond to exons 7-22 of the zebrafish pfkla, demonstrating that the two automatically predicted genes (GSONMG00009460001 and GSONMG00009459001) on scaffold 7584 were in fact only one. Similarly, we found an EST contig (FYV3OTN02GK8A7. s. om. 10; Fig. S3B ) astride exons 1-5 of the zebrafish pfklb, suggesting that the corresponding orthologous gene in rainbow trout was partially due to the incomplete assembling of the rainbow trout genome at this locus.
No pklr orthologous gene was found in the rainbow trout genome (either using Genomicus software or by direct BLAST against the rainbow trout genome) but an EST contig (Sigenae, AF246146. s. om. 10) sharing high sequence homology (around 75%) with teleost pklr genes was identified. A phylogenetic analysis was performed to confirm its identity and this showed that the contig identified grouped with vertebrate pklr orthologues (Fig. 1 ). Moreover, Pfam analysis confirmed that the trout sequence was related to a pyruvate kinaseencoding gene. In conclusion, our in silico analysis identified two     , 5, 6, 7, 8, 10, 12, 15, 22 and 23, and 31 (alevin). Data are expressed as means±s. e. m. (N=3 pools of embryos, one pool per spawn). paralogous pfkl genes in the rainbow trout one orthologous to the zebrafish pfkla (scaffold 7584) and the other to the zebrafish pfklb (scaffold 8651, GSONMG00001975001), and one pklr orthologous gene (Sigenae, AF246146.