dataesr/scientific-paragraphs-categorization huggingface dataset:
Docking Study Using the Discovery Studio tool (version 2021), 3D structures of the compounds found in the bioactive extract were constructed, establishing a database that was then used as an input file for DockingServer. Docking calculations were performed using DockingServer  [76]. Ligand atoms were prepared for docking through the addition of gaseous partial charges and non-polar hydrogen. Single bonds were allocated, and all spatial confirmations were identified. Virtual docking of ligand atoms on the active site of the HMGB1 protein structure was performed. AutoDock tools (version 4. 2. 6. ) were used for the addition of essential hydrogen atoms, Kollman-type charges, and dissolution conditions  [77] , as well as the calculations of van der Waals forces, electrostatic energies, and distance-dependent dielectric functions.
Experimental Design and Selection of the Best Achieved Nanophytosomal Formula Utilizing Design Expert ® software version 7, a full factorial design (2 2 ) was created to assess the effect of several factors on nanophytosomal formulation (Stat Ease, Inc. , Minneapolis, MN, USA). Four runs were required by the design. Two factors were chosen as independent variables, namely PC amount (X 1 ) and cholesterol amount (X 2 ). The dependent variables were EE% (Y 1 ), PS (Y 2 ), PDI (Y 3 ), ZP (Y 4 ), and Q6h (Y6) (Table  2 ). Afterward, the optimum formula was selected using the desirability function, which allows the simultaneous investigation of all variables. The selection of the formula with the best characteristics relied on having the smallest PS, PDI, and Q6h and the highest EE% and ZP as absolute values.
Stability Study of Optimum Nanophytosomal Dispersion (SPNP) The physical stability testing of SPNP was performed to examine any physical changes that may take place during storage. SPNP was stored at 4 • C for 1 year then the pH, PS, PDI, EE%, ZP, and Q6h were measured and the received data were compared with that of the freshly prepared formulation. The results were statistically analyzed using Student's t-test (SPSS ® 25. 0 software). The differences were considered significant at p ≤ 0. 05. The system was also visually examined for any signs of particle aggregation or sedimentation.
Western Blotting The following parameters were determined using the Western blotting assay: HMGB-1, AIF, LC3BII/I, and Beclin-1 (primary antibodies were purchased from Thermo Scientific Co. , IL, USA), and the steps adhered to the formerly mentioned technique by El Gazar et al. (2022)  [82]. Specific bands were visualized with a ChemiDocTM imaging system (Image LabTM software version 5. 1, Bio-Rad Laboratories Inc. , Hercules, CA, USA). The optical density (OD) of the following findings was standardized against β-actin.
Editor's evaluation This important manuscript employs a rigorous and multi-pronged comparative genomics approach to unravel how lifestyle modulates the acquisition and domestication of viral genetic elements in the genomes of hymenopteran insects. Using an extensive dataset of over 120 hymenopteran genomes, the authors provide convincing evidence that endoparasitism (where parasite development occurs within hosts) facilitates the uptake and domestication of double-stranded DNA viral elements.
Results We screened for EVEs 124 Hymenoptera genome assemblies, including 24 ectoparasitoids, 37 endoparasitoids, and 63 free-living species (the list can be found in Supplementary file 2). EVEs were identified using a sequence-homology approach based on a comprehensive viral protein database. Different confidence levels (ranging from A to D) were associated with the various EVEs inferred, where the A score indicates a maximal confidence level for endogenization. This confidence index is based upon sequencing depth combined with the presence of eukaryotic genes and/or transposable elements in the genomic environment of the candidate loci (as detailed in the Material and methods section). By default, the four categories are included in the analysis, but unless otherwise stated statistical tests based on the A category only led to the same conclusions (see Figure  1 figure supplements 2-7 for more details).
Endogenizations involve all viral genomic structures A total of 1261 EVEs have been inferred in the whole dataset (Supplementary file 1, Figure  1 ). These come from 367 endogenization events, the majority of which involved ssRNA and dsDNA viruses (41% and 35%, respectively) (Supplementary file 1). Among the 124 species under study, 113 underwent at least one endogenization event, with a maximum of 14 events and a median of 3 (Figure  1 ). In total, 91% of the events (331) occurred on tip branches, and the remaining 9% are shared by at least two closely related species (Supplementary file 1, Figure  1 ). To assess the validity of the procedure used to aggregate multiple EVEs into a single shared ancestral endogenization, we assessed whether EVEs inferred as homologous shared a common genomic environment.
More specifically, it shows that dsDNA viruses are more frequently endogenized than expected on the basis of their representation in the database, while ssRNA viruses are under-represented ( χ 2 =56. 9 and 30. 9, respectively, for endogenization events and domestication events, d. f. =3, both p-value <2. 2e-7) (see data in Supplementary file 6). Notably, this result is not purely driven by the presence in our data set of the four positive controls (previously described cases of viral domestication, that all involve dsDNA viruses as donors, both p-value <2. 2e-7). Finally, among endogenization events involving ssRNA viruses, we found an over-representation of negativestranded ssRNA compared to their relative abundance in public databases (72. 2% compared to 32. 5% in the databases, χ 2 =147.
Endogenizations of dsDNA viruses are more frequent in endoparasitoid species Next, we sought to characterize the factors that could explain the patterns of endogenization events inferred (Figure  1 ). To this end, for each viral genomic structure, we assessed whether endogenization events were evenly distributed among the three wasp lifestyles, taking into account their respective frequencies in the dataset. No significant departure from the null hypothesis was detected for endogenization involving ssDNA, dsRNA, or ssRNA viruses (Fisher exact test p-values BH corrected >0. 05). On the contrary, we detected a highly significant enrichment of dsDNA viruses endogenization events in endoparasitoid species, and conversely a deficiency in free-living and ectoparasitoid species (corrected p-value = 7.
We then split the dataset according to the genomic structure of the donor viruses. For RNA or ssDNA viruses, the analysis did not reveal evidence of a correlation between wasps' lifestyles and the rate of endogenization events (Figure  4 -figure  supplement 1G,I  and K ). On the contrary, in the case of dsDNA viruses, we found a highly significant effect of the wasp lifestyle: endogenization rates appear to be 2. 47 times higher in endoparasitoids than in free-living species (89% CI  [1. 56-3. 56] , Figure  4A ). The corresponding probability of direction (pd, an index representing the confidence in the direction of an effect) was equal to 99. 9%. In contrast, ectoparasitoids did not differ from free-living species (Figure  4A ). Accordingly, more than 98% of the MCMC iterations led to a higher coefficient value for endoparasitoids than for ectoparasitoids (so-called P MCMC in Figure  4A ).
By controlling for the endogenization input (the denominator), these binomial models make it possible to test whether the probability of domestication after endogenization of dsDNA viruses is correlated with the lifestyle. Based on this analysis, the probability that an endogenization event will lead to a domestication event is not significantly different between endoparasitoids and free-living species (Figure  4     supplement 2A, pd = 89. 18%). However, the probability of domestication was found to be significantly higher in endoparasitoids than in ectoparasitoids (Figure  4 -figure supplement 2A, P MCMC =99. 81%). The same trend was observed if we focused on high-confidence scaffolds and/or if we removed the four known controls from the dataset (Figure  4 -figure supplement 2B,C and D, pd <86%).
Most importantly, this analysis revealed again a greater number of endogenization events or domestication events involving dsDNA viruses in endoparasitoid compared to free-living species (pd >99%, Figure  4 -figure  supplement 3C, D ). The conclusions remained unchanged when controls were removed from the dataset (Figure  4 -figure  supplement 3E  and F ). When we only considered loci annotated with an A score (highly confident EVEs), we found the exact same pattern, although the effect was only marginally significant for the number of events (pd = 98. 6%) (Figure  4 -figure supplement 3C and D). In addition, more than 98% of the MCMC iterations led to a higher coefficient value for endoparasitoids compared to ectoparasitoid or eusocial species in that combination. When combining both filters (only A score and without the controls), the same tendency was observed, although the effects were only marginally significant (pd min = 96%) (Figure  4 -figure supplement 3E and F).
All the best partitioned models as well as the number of genes used for each taxa can be found in Supplementary file 7. All free-living dsDNA viruses used in this phylogeny were obtained from published complete viral genomes. More details on the phylogenetic inference can be found in the methods. The '?' for DoEFV/DoFV refers to the uncertainty regarding the endogenous/exogenous status of this sequence. All Cluster sequence alignments can be found in the Figure  5 -source data 1. The online version of this article includes the following source data and figure supplement(s) for figure  5 : Source data 1. File including all aligned cluster sequences included in the Naldaviricetes phylogenetic analysis. endogenized elements are related to a poorly characterized family of filamentous viruses denoted AmFV  (Hartmann et al.
Discussion All kinds of viruses can integrate arthropod genomes, although the mechanisms underlying these phenomena remain unclear  (Katzourakis and Gifford, 2010; Gilbert and Belliardo, 2022). Prior to the present analysis, 28 viral families had been described as involved in endogenization in arthropods  (Gilbert and Belliardo, 2022). Our study of hymenopteran genomes which relied on sequence homology search, further revealed the ubiquity of this phenomenon, with 1261 EVEs found, belong to at least 40 viral families (or family-like clades). Because the identification of EVEs necessitates the availability of related viruses in the database, we should see these numbers as an underestimation of the real number of EVEs. In addition, our pipeline necessitates the availability of either related species sharing the same EVEs (or at least the presence of paralogs within a single species) or the availability of RNAseq data to infer domestication.
dsDNA viruses are more frequently involved in endogenization than expected by chance Despite the observations that all viral genomic structures can be involved in endogenization, we clearly identified differences in their propensity to do so. Based on a comparison between the respective proportions of the various viral categories in the inferred endogenization events and in public databases, we found that dsDNA viruses are much more represented than expected, while ssRNA viruses are under-represented (Figure  2A ). We acknowledge that current knowledge on the actual diversity of free-living viruses (as approximated through the NCBI taxonomy database) remains incomplete, but the strength of the effect reported here makes this conclusion rather robust to variations in the null distribution. On the basis of current knowledge, RNA viruses, and in particular ssRNA viruses, appear to be much more diversified and prevalent than DNA viruses in insects.
Furthermore, the sequence identities with known viral sequences, which is needed for this mechanism to work, is generally low in our dataset. Accordingly, previous work revealed that EVE-derived piRNAs studied in 48 arthropod species were also probably too divergent to induce an efficient antiviral response  (Ter Horst et al. , 2019). At that stage, the ability of EVEs to generate PIWI-interacting RNAs that play a functional role in antiviral immunity seems questionable. Further studies involving small RNA sequencing in hymenopterans would be required to shed light on this issue. Protection of the eggs and larvae against the host immune system is recognized as an important trait, where EVEs play a critical role. Because of their peculiar lifestyle, endoparasitoids are all targeted by the host immune system, a matter of life or death to which other hymenopterans are not exposed to.
Genome sampling, assembly correction, and assembly quality A bioinformatic pipeline mixing sequence homology search, phylogeny, genomic environment, and selective pressure analysis was built to search for viral endogenization and domestication events in Hymenoptera genomes. We used 133 genome assemblies in total, of which 101 were available on public repositories (NCBI and BIPPA databases) and 32 were produced by our laboratory (all SRA reads and assemblies available under the NCBI submission ID: SUB11373855). Concerning the last 32 samples, DNA was extracted on single individuals (usually one female) or a mix of individuals when the specimens were too small using Macherey-Nagel extraction kit, the DNA was then used to construct a true seq nano Illumina library at Genotoul platform (Toulouse, France).
These UCEs (along with 400 bp of flanking regions) were extracted from the low coverage scaffolds with a custom script. We used a twostep process to assign the unknown sample to species. First, UCEs+ flanking regions were analyzed with a set of UCEs+ flanking regions were obtained from early diverging families of Chalcidoidea by  Cruaud et al. , 2019; Rasplus et al. , 2020  to assign the unknown sample to a family. Then, unknown sequences were analyzed with a larger set of species belonging to the identified family (Eulophidae; loci taken from  Rasplus et al. , 2020). In both cases, only loci that had a sequence for at least 75% of the samples included in the analysis were retained. Loci were aligned with MAFFT (-linsi option;  Katoh and Standley, 2013). Positions with >90% gaps and sequences with >25% gaps were removed from the alignments using SEQTOOLS (package PASTA;  Mirarab et al.
Pipeline outline EVEs were identified from the 124 Hymenoptera assemblies using a sequence-homology approach against a comprehensive viral protein database (including all categories of viruses: ssDNA, dsDNA, dsRNA, and ssRNA). In order to validate viral endogenization within Hymenoptera genomes, we developed an 'endogenization confidence index' ranging from A to X (Figure  1 -figure supplements 2-7). This index takes into consideration the presence of eukaryotic genes and/or transposable elements around candidate loci, and scaffolds coverage information (coverage for a valid candidate should be similar to that found in BUSCO containing scaffolds). Finally, the pipeline also included an assessment of the evolutionary history and of the selective regime shaping the candidates (based on dN/dS and/ or expression data).
Search for viral homology We collected all protein sequences available in the NCBI virus database  (Hatcher et al. , 2017) , removing phage and polydnavirus (virulence genes from wasp origin found within PDVs) sequences. This database contained 849,970 viral protein sequences (download date: 10/10/2019), to which the 40 putative viral proteins encoded by the Hyposoter didymator genome were added (so-called IVSPER sequences,  Volkoff et al. , 2010). The sequence homology search was performed with a BlastX equivalent implemented in Mmseqs2  (Steinegger and Söding, 2017)  using each genome assembly as queries and the viral proteins collected as a database. The result gave a total of 81,953,678 viral hits (max E-value 5e-04 with an average of 660,916 hits per genome). We kept only candidates with a percentage coverage of the viral protein ≥ 30%, an identity score ≥ 20% and an E-value score <5e-04 (Figure  1 -figure supplement 2).
The threshold parameters were optimized to maximize the detection of the 13 endogenous viral sequences within the genus Leptopilina (Di  Giovanni et al. , 2020). Once all the viral hits were recovered, we formed putative EVEs loci (n=238,108) corresponding to the overlap of several viral hits on the same scaffold using the GenomicRanges R package  (Lawrence et al. , 2013 ; Figure  1 -figure supplement 2). To remove false positives corresponding to eukaryotic genes rather than viral genes, we then performed another generalist sequence homology search against the Nr database (downloaded the 09/11/20) using mmseqs2 search (-s 7. 5, E-value max = 0. 0001) (Figure  1 -figure supplements 2-3). We did not select our candidate based on the best hit, since it does not necessarily reflect the true phylogenetic proximity.
Instead, candidates with more than 25 hits with either eukaryotic non-hymenoptera species or prokaryotic species were removed, except if they also had hits with at least 10 different virus species (bits ≥ 50). We chose to eliminate Hymenoptera hits from the database because if a real endogenization event concerns both one of the 124 species of our dataset and some species in the NCBI database, then an apparent 'Hymenoptera' hit will be detected, possibly leading to its (unfair) elimination. Since viral diversity is poorly known, we also kept sequences with even one single viral hit, as long as it did not have more than 5 eukaryotic or prokaryotic hits. Using these filtering criteria we removed a total of 234,036/238,108 (98,3%) candidate loci leaving 4,072 candidates with convincing homology to viral proteins.
Note that among these loci a certain proportion actually corresponded to non-endogenized 'free-living' viruses. To study the evolutionary history of these candidate EVEs, we then performed a general protein clustering of all the candidates and the NCBI viral proteins (Figure  1 -figure supplements 2-4, Mmseqs cluster; thresholds: E-value 0. 0001, cov% 30, options: -cluster-mode 1 -cov-mode 0 -cluster-reassign -singlestep-clustering  Steinegger and Söding, 2018). We eliminated from the dataset the chuviral glycoproteins that have been captured by LTR retrotransposons  (Dezordi et al. , 2020) , as these loci have complex histories mostly linked to the transposition activity after endogenization. For this purpose, we systematically searched among the candidates for the presence of TEs within or overlapping with the EVE (more details in Supplementary file 9).
, 2016; Gilbert and Cordaux, 2017; Loiseau et al. , 2020)  because TE insertions are mostly deleterious and are, therefore, quickly eliminated by negative selection  (Gilbert et al. , 2016; Gilbert and Cordaux, 2017). We searched for transposable elements with a BlastX-like approach (implemented in Mmseqs2 search -s 7. 5), taking as query the scaffolds of interest and as database the protein sequences of the transposable element (TE) available in RepeatModeler database (RepeatPeps, v2. 0. 2)  (Flynn et al. , 2020). We only kept hits with an E-value <1e-10 and with a query alignment greater than 100 amino acids. We then merged all overlapping hits and counted the number of TEs for each scaffold. To find eukaryotic genes within genomes we used Augustus v3. 3. 3  (Stanke et al. , 2004)  with BUSCO training and then assigned a taxonomy to these genes via sequence homology with Uniprot/Swissprot database using mmseqs2 search  (Steinegger and Söding, 2017) , and only retained genes assigned to insects.
, 2011)  in order to merge possible candidate loci (which may in fact correspond to various HSPs). All loci (=HSPs) within the same scaffold presenting no overlap in the alignment were thus merged, as they probably correspond to multiple HSPs and are not duplications. We then performed a new codon alignment from the augmented sequences in the clusters using the MACSE v2 alignsequence program  (Ranwez et al. , 2018; Figure 1-figure supplements 2-8). This alignment allowed us to obtain a protein and nucleotide codon alignment. We used the protein alignment to infer the phylogeny of each cluster with the program Iqtree2 v2. 1. 2  (Minh et al. , 2020)  (-m MFP -alrt 1000 (partitioned))(Figure  1 -figure supplements 2-9). No trimming was performed at the aminoacid level, since this may result in a loss of topology information  (Tan et al.
012, TP = 0. 922); 100,00,000 bp = 74262 bp (FP = 0. 012, TP = 0. 878); 100,00,00 bp = 21000 bp (FP = 0. 014, TP = 0. 28); 100,000 bp = 1332 bp (FP = 0. 012 TP = 0. 198) and 10,000 bp = 180 bp (FP = 0. 008, TP = 0. 208)). Events were linked to viral families based on the closest match information between the viral blastx (GenBank accession number and/or viral protein and/or viral species) and the classification proposed in  Shi et al. , 2016.
dN/dS with dS greater than 10 were removed, since this indicates substitution saturation (Figure  1 -figure supplement 2). The other way we choose to study the domesticated nature of a viral gene was to study their expression profile (Figure  1 -figure supplement 2). We reasoned that domesticated genes are likely to be significantly expressed. To test this, when RNAseq reads were available on NCBI (SRA), we mapped them on the assembled genomes (until reaching 300x coverage as far as possible). Using the TPMCalculator program (Vera  Alvarez et al. , 2019) , we measured expression in ovaries and the whole body if available or alternatively in any tissue (see Supplementary file 10). An EVE was considered as domesticated if the gene was expressed with a Transcripts Per Kilobase Million (TPM) index above 1000.
Sensitivity and specificity of the analysis Capacity to find EVEs Among the species included in our dataset, seven were known to contain a domesticated virus (two with similar PDV  (Bézier et al. , 2009) , and five with different VLPs  (Pichon et al. , 2015; Burke, 2019; Di Giovanni et al. , 2020) , corresponding to four independent endogenization events). Our pipeline was able to detect the vast majority of the corresponding virally-derived genes (88. 6%, details in Supplementary file 3 and Figure  1 -figure supplement 9). The 11. 14% false negatives corresponded to sequences that were too divergent or with a region of similarity is too small to be detected by our pipeline. We found that 88. 7% of the control EVEs were located within that scaffolds scored as A (i. e. having a depth of coverage falling within the distribution of those containing BUSCO genes, as well as having one or more eukaryotic genes and/or transposable elements in the vicinity).
Assessing the distribution of virus infecting insects We estimated the number of viral species infecting insect species based on the virushostdb database (version of 24/03/2023 on  Mihara et al. , 2016)  which lists a wide diversity of viral species associated with their putative hosts. We kept only viruses found in interaction with insects. Genomic structures were retrieved through the ICTV report (V2022_MSL38) and information available in ViralZone (all viral species details can be found in Supplementary file 5). We counted the number of viruses per genomic structure, and viruses from unknown genomic structures were discarded. Importantly, the distribution of viruses among genomic structures found in Hymenoptera was not different from the distribution found in the whole Insecta class ( chi 2 = 6.