dataesr/scientific-paragraphs-categorization huggingface dataset:
The MCC calculation is preferred over an F1 score here because, in practice, environmental datasets will likely have a disproportionate amount of phage to archaeal viruses, so a test that incorporated both the true positives and true negatives will be more informative than one that only includes the true positives. TPR, SPEC, ACC, and MCC were also calculated for the MArVD analysis. AUROC and AUPRC analysis were conducted on each of the datasets using the R package "PRROC"  [90]. Visualization of the probability vs host phylum and the statistical assessments were plotted with the R package "pROC"  [91] , and "gglpot2"  [92]. Gene sharing between archaeal viruses and phage was assessed using vConTact2 with default settings by adjusting the "keywords" in the input "proteins. csv".
ACKNOWLEDGEMENTS This material is based on work supported by the  U. S Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSC) program. The SCGSR program is administered by the  Oak Ridge Institute for Science and Education (ORISE)  for the DOE. ORISE is managed by  ORAU  under contract number  DE-SC0014664. All opinions expressed in this paper are the author's and do not necessarily reflect the policies and views of DOE,  ORAU , or ORISE. This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  located at  Lawrence Berkeley National Laboratory , operated under Contract No. DE-AC02-05CH11231.
Programmatic funding awards to MBS ( NSF-OCE1829832 ,  NSF-ABI1759874 ,  DOE-BER-248445 ), and MK was supported by l' Agence Nationale de la Recherche  (grant  ANR-20-CE20-009-02 ). The work conducted by the  U. S. Department of Energy Joint Genome Institute  (https://ror. org/04xm1d337), a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  operated under Contract No. DE-AC02-05CH11231  (SR). We would also like to thank the  NSF EMERGE Biology Integration Institute  for access to data and resources (funding to MBS award  29640 ).
DATA AVAILABILITY All databases, training data, benchmarking data, OcAVdb, and the random forest model described herein are available on Cyverse at https://de. cyverse. org/data/ds/iplant/ home/shared/commons_repo/curated/DeanVik_MArVD2_Apr2022 https://doi. org/10. 25 739/1ttq-2q60 and Zenodo at https://zenodo. org/record/7768113/files/MArVD2_files. tar. gz MArVD2 is available at bitbucket https://bitbucket. org/MAVERICLab/marvd2/ and as a bioconda package at https://anaconda. org/bioconda/marvd2.
Basal activity was measured at 0. 1 Hz. Paired-pulse facilitation was recorded by delivering two stimuli at an interval of 40 ms. In some experiments, a Cx43 hemichannel blocker peptide and its scramble version (Gap26/Gap26 scramble; 100 µM; with 5 min pre-incubation) or glutamine (4 mM; with 1-4 h pre-incubation in storage chamber) were included in the ACSF, both before and during the experiments. The recordings were acquired with Axopatch-1D amplifiers (Molecular Devices, San Jose, USA), digitized at 10 kHz, filtered at 2 kHz, as well as stored and analyzed on a computer using pClamp9 and Clampfit10 software (Molecular Devices). Stimulation artifacts were removed in representative traces for illustration. For dye filling experiments, CA1 pyramidal neurons were visually identified and filled under whole-cell configuration via glass pipettes  (5) (6) (7) (8) , containing an intracellular solution (in mM): 105 K-gluconate, 30 KCl, 10 HEPES, 10 phosphocreatine, 4 ATP-Mg, 0.
This was followed by a transfer of proteins onto nitrocellulose membranes, which were then saturated with a 5% fat-free dried milk in a triphosphate buffer solution and were then incubated overnight at 4 • C with appropriate primary antibodies. On the next day, membranes were treated with peroxidaseconjugated secondary antibodies for 2 h at room temperature and were revealed using a chemiluminescence detection kit (ECL, GE Healthcare, Thermo Fischer) and visualized using a LAS 4000 imaging system and with ImageQuant LAS 4000 software (Fujifilm, Valhalla, NY, USA). The densitometry measurement of protein bands was performed using the Gel Analysis function in ImageJ (National Institutes of Health, USA). All readings were normalized to the +/+ bands on the same blot, and a relative expression was plotted.
1 MW, Spectra-Physics, Santa Clara, CA, USA) in the presynaptic terminals with low background non-specific binding. Next, the fluorescence unloading of FM1-43 was monitored using a two-photon live imaging of a region of interest (ROI) in the CA1 region. Image acquisition was performed using 20× water immersion objective with 2× zoom, 12 frames per plane, and 20 planes per stack with z-spacing of 0. 5 µm, at a rate of 1 z-stack/15 s. After a baseline recording of 150 s, an unloading stimulus at 10 Hz for 2 min was delivered to the CA1 Schaffer collaterals using the same MEA electrodes that were used for dye loading. MEA was controlled using MC_Rack 4. 5. 1 software (Multichannel Systems), and image acquisition was performed using SciScan software (Scientifica). Image analysis was performed using ImageJ software (National Institutes of Health).
Confocal Image Acquisition Pyramidal neurons filled with sulforhodamine B were imaged in fixed hippocampal slices via a confocal laser-scanning microscope (TCS SP5, Leica) and the image acquisition software LAS X (Leica). The image acquisition was performed using an excitation laser (DPSS 561 nm). For the analysis of neuronal morphology, z-stacks of ~100 µm (with 1 µm z-spacing) were imaged per cell using a 20× objective (NA = 1. 25) at 1× zoom. For the analysis of dendritic spine morphology, the z-stacks of 5-10 µm (with 0. 2 µm z-spacing) were imaged per dendritic segment by using a 63× objective (NA = 1. 4) at 2. 5× zoom.
Dendritic Spine Analysis The fluorescence images of the dendritic spines were first deconvolved by using Huygens Essential software (Scientific Volume Imaging B. V. , Hilversum, The Netherlands). The analysis of the dendritic spine morphology was performed using the NeuronStudio software package  [24]. The following parameters were automatically generated for each spine segment: dendrite length, spine number, spine length, spine head diameter, and spine neck diameter. Spine density was determined as the number of spines per 10 µm of dendrite. Each spine was then categorized as one of the four categories: thin, filopodia, stubby, and mushroom  [25]. This was determined mathematically based on the ratios between (1) the spine head diameter and length (HLr) and (  2 ) the spine head and neck diameter (HNr) of the individual spines.
Prior to transmission electron microscopic examination, the sections were routinely stained with 5% aqueous uranyl acetate for 20 min and a lead citrate for 5 min  [27]. The ultrathin sections through the stratum radiatum of the hippocampal CA1 subregion were examined with a Zeiss Libra 120 electron microscope, which was equipped with a Proscan 2K digital camera and the ImageSP software at a magnification of ×8000. Serial EM images (20. 46 µm × 20. 63 µm) were imported, stacked, and aligned into the software OpenCAR  [28]. Subsequently, ~20 randomly chosen synaptic boutons per animal were outlined as contours and 3D reconstructed [29] in order to obtain measurements for the volume of the synaptic boutons and surface areas of postsynaptic densities (PSDs). The values obtained were in line with those recorded in rat CA1  [30].
Statistical Analysis Data were stored and analyzed using Microsoft Excel (Microsoft, Redmond, WA, USA). The statistical analyses was performed using GraphPad Prism software v7 and v8 (USA). All data are expressed as the mean ± standard error of the mean (SEM), where n represents the number of the independent experiments. The statistical significance was determined by one-sample t-test, unpaired Student's t-test, Mann-Whitney test, one-way ANOVA-or two-way ANOVA-followed by Bonferroni's multiple comparisons test. The test for normality was performed by using the Shapiro-Wilk normality test. To compare the within-group variance between the experiment groups, F-test was performed.
We used the well-annotated A. mellifera reference genome to trace venom genes and their flanking genes based on exon regions. We identified orthologs for each exon in other genomes, which were collected into an extended database. We searched all genomes using this database and then manually inspected the results before we inferred the phylogeny of each protein family to establish completeness and microsynteny. "Synteny" refers to shared patterns of gene arrangement ("colinearity") in homologous genomic regions across taxa which reflect the arrangement and position of flanking exons of genes around venom protein genes (see details in " Methods"). When sufficiently high-quality genomic sequences are available and genes of interest are located in stable regions, the ability to utilize microsyntenic analysescomparisons of synteny/colinearity in short stretches of the genome-is a key advantage of comparative genomics.
[29]. Novel, and further undescribed peptides and proteins are shown in grey. The hymenopteran groups are based on the recent phylogeny according to Peters et al. [33]. Please note that three melittin sequences from wasps are falsely annotated in UniProt as wasp melittins (marked by a black X). Our analyses clearly show that genes encoding peptides highly similar to honey bee melittin are not present in wasps, see also von Reumont et al. [22]. The phylogeny is pruned to the groups for which data is available based on Peters et al. [33]  (See figure on next page. ) a multidimensional "protein space" similar to the concept of a "configuration space" in physics, or an "arbitrary space" in multi-scale cognition  [35]. Our protein space incorporates data concerning the structure and function of mature proteins to generate a multidimensional model of protein relations.
Abundant venom proteins are encoded by more widespread single-copy genes Phospholipase A2, hyaluronidase and icarapin are among the most abundant bee venom components  [5, 18, 27]. Phospholipase A2 and icarapin are encoded by four-exon single-copy genes, whereas the hyaluronidase singlecopy gene features nine exons. Dipeptidyl peptidase-4 has a strongly conserved single gene, which was present in all hymenopterans in our dataset, probably due to its enzymatic role in the maturation of some toxins. These protein families were highly conserved and ubiquitously present in the genomes of bees, wasps and ants (Fig. 4 , see also Additional files  10, 11, 12, 13, 14, 15, 16, 17, 18 and 19  for phylogenetic alignments and trees). Our results support the hypothesis that these genes were recruited into venom functions without any associated duplication-similar to co-option of single-copy genes proposed as the main process of venom protein evolution in Nasonia  [36].
Some venom proteins form multi-copy gene families with ancient duplication events Larger duplication and diversification events appear restricted to families of enzymatic or larger proteins and not toxin peptides or proteins. Three venom protein classes in the PBVP showed copy number variation across the dataset: venom allergens 3/5, venom acid phosphatases (APHs), and venom serine proteases (VSP), see Fig. 4. These genes were in stable genomic regions allowing for the tracing of homologous regions between species by screening for microsynteny. Among the 10 subfamilies of venom acid phosphatases (Fig. 4 ), the largest expansion of genes occurred in subfamily 7, found exclusively in parasitoid wasps. This may support the hypothesis that ancestral APHs functioned as pre-digestion factors that allowed the offspring of parasitoid wasps to feed more easily on their host  [39].
Melittin is restricted to the bee lineage Melittin is a pain-inducing peptide in A. mellifera venom  [27, 43]. The synteny of the A. mellifera genome shows that melittin is encoded by a two-exon single-copy gene located between two four-exon genes, one of which Fig. 3  Machine learning generated protein space representations of hymenopteran venoms corresponds with gene phylogeny-based clustering. In each case, the left panel shows the breakdown of subgroups revealed in this study (protein families for the entire dataset, subfamilies for each of the protein families) while the right panel shows the same space coloured by taxa, clearly highlighting that each protein group is a gene clade, not a species clade. A Representation of the entire non-redundant dataset. B Acid phosphatase family. C Serine protease family.
Gene synteny and assisting machine learning model of "protein space" cast doubt on aculeatoxin hypothesis Given the short peptide sequences and consequent challenges for phylogenetic analyses, we utilized a novel, alignment-independent machine learning approach (see " Methods" section for the detail) to test the proposition of aculeatoxins by Robinson et al. [29]. We focused on small peptidic aculeate toxins, but especially on melittin, which is according to Robinson et al. based on signal and propeptides, a member of the "aculeatoxin" family which origins with aculeates (see Fig. 1 ). We used all sequences that Robinson et al. [29]  presented in their study (kindly provided to us by the authors) and all melittin-like toxins known from bees that are included in our study. We created three datasets: (panel A on Fig.
7 ) mature sequences from Robinson et al. data, all unique hymenoptera venom peptides from this study and ToxProt as part of UniProt (see Additional file 33); (panel B on Fig. 7 ) Robinson et al. data with (Additional file 34) and without (Additional file 35) signal/propeptides-the latter dataset includes more sequences since bee melittins are mostly known from proteomic studies and therefore only their mature sequence is known. When comparing proposed aculeatoxins with other hymenopteran peptides, it becomes apparent how the sequence space occupied by this hypothetical protein family bleeds into that occupied by apamins and icarapins, and also exhibits a very clear separation by taxa (in contrast to established protein families from Fig. 3 ). Sequences from bees, wasps and ants do not intermix, which is even more apparent on the smaller scale seen in Fig.
We did not find any genomic location and sequences that match eumenine, vespakinins and myrmecitoxin genes in any of our bee data and thus did not include them in further analyses. However, we located poneratoxin-like sequences near or within the atrial natriuretic peptide-converting enzyme in all ant genomes of our dataset. The number of the genes seems to vary between the species. Although the genomic region appears unstable, we located atrial natriuretic peptide-converting enzymes and some of its nearby and proximal neighbouring genes in Vespa and Apis. No genomic feature in the genome of Vespa seems to relate to poneratoxins, while Apis features an uncharacterized small 2 exon gene in a similar position. Aligning it with poneratoxins from H. saltator, S. invicta and O. brunneus (extracted from genomic regions) revealed its close similarity to the 5′ end of prepro mRNA of the longest of H.
Data mining of hymenopteran venom proteins and genomes Reviewed venom proteins of hymenopterans were searched in UniProt resulting in 372 protein matches from 101 species (Fig. 1  and Additional file 8). Additionally, we searched publications for sequences that are not provided in UniProt and included finally three bee toxins Halictin I and II from Halictus sexcintus, and Codesan from Colletes daviesanus. For our comparative genomic analysis of venom toxin proteins across the order Hymenoptera, we made use of 29 publicly available genome sequences given in Additional file 9 and three novel genomes of solitary bees.
For H. scabiosae, an in-house ultra-low input protocol was used by Novogene due to very low RNA concentration and quantity. All RNASeq raw data of X. violacea, A. mellifera and H. scabiosae were generated within the present study and are accessible in GenBank via the BioProject PRJNA733472 (SRA entries: SRR14690757, SRR14690758, SRR14690759)  [56] [57] [58]. Venom gland transcriptomes were assembled separately using Oyster River Pipeline v2. 2. 6  [59] ; for resulting BUSCO values, see Additional file 7. The resulting assemblies were processed using Transdecoder (minimum length 20 amino acids) to predict peptides, and Kallisto v0. 46  [60]  to calculate individual transcript abundance, see Additional files 1, 2 and 3. The assembled transcripts and their corresponding longest open reading frames ORFs from Transdecoder were used as local BLAST queries against ToxProt and UniProt (the latter limited to insects only) with an e-value cutoff of 1 × 10 -3 , see Fig.
9. Any highly abundant (TPM > 100) transcripts without significant matches were manually screened using BLAST, InterPro scan and Predict Protein online suites to determine the closest characterized homologue. For subsequent venom protein identification, we only included transcripts identified in our proteomic dataset representing proteins secreted in the venom system. To compare subsequently all venom proteins in the three datasets we calculated the percentage of scaled TPMs using the package txtimport on R, the script is available via github (https:// github. com/ mariv elasq ue/ Venom Evolu tion. git), see Fig. 2  and Additional files 1, 2 and 3.
Proteome analysis of crude venom We extracted crude of all specimens from glands and venom reservoirs by squeezing with forceps in sterile ultrapure water (Thermo Fisher Scientific, Waltham, MA, USA) after prewashing twice to minimize hemolymph contamination. All transcriptome assembly-based predicted ORFs were used as specific databases to identify peptides and proteins detected by mass spectrometry from crude venom of the collected specimens. For the tryptic digestion of the crude venom from H. scabiosae, we dissolved 10 µg of protein in 10 µl 10 M urea containing 0. 1% ProteasMax (Promega, Madison, WI, USA). Cysteine residues were reduced with 5 mM DTT (30 min at 50 °C) and modified with 10 mM iodoacetamide (30 min at 24 °C). The reaction was quenched with an excess of cysteine and trypsin was added at a protein:enzyme ratio of 40:1 in 100 µl 25 mM ammonium bicarbonate (Sigma-Aldrich, Taufkirchen, Germany).
violacea and A. mellifera venom samples against an in-house database resulting from the annotated transcriptome of each species. Carbamidomethylation was set as a fixed modification, and oxidation of methionine as a variable modification, with a maximum of three missed cleavages for trypsin digestion. Parent and fragment mass error tolerances were set at 5 ppm and 0. 015 Da, respectively. A false discovery rate (FDR) of 1% and a unique peptide number ≥ 2 were used to filter out inaccurate proteins. A -10lgP value > 120 was used to estimate whether detected proteins were identified by a sufficient number of reliable peptides. In order to identify more relevant sequences, the Spider algorithm (PEAKS Studio) was used to find additional mutations or to correct sequences. This algorithm corrects the sequences stored in transcriptomic databases with de novo sequences based on MS/MS spectra, allowing the detection of post-translational modifications (PTMs) and mutations.
The minimum ion intensity for PTMs and mutations was set to 5%, and the ALC score was set to ≥ 90 for de novo sequences, leading to low precursor mass errors. Transcripts supported by proteomic data were manually filtered by excluding non-venom-related proteins and peptides, such as house-keeping and structural genes (Additional files 1, 2 and 3). All proteome raw data are accessible via PRIDE (PXD029934, PXD029823, PXD026642)  [61] [62] [63].
Genome sequencing The genomes and annotations of the stingless bees Tetragobula carbonaria and Melipona beecheii will be published as part of another study, but are already accessible in Genbank (JAUCRC000000000 and JAUCMO000000000)  [64]. To sequence the genome of X. violacea high molecular weight DNA was extracted from four legs of X. violacea adapting the protocol from Miller et al. [65]. Final DNA purity and concentrations were measured using NanoPhotometer ® (Implen GmbH, Munich, Germany) and Qubit Fluorometer (Thermo Fisher Scientific, Waltham, MA). Two SMRTbell libraries were constructed following the instructions of the SMRTbell Express Prep kit v2. 0 with Low DNA Input Protocol (Pacific Biosciences, Menlo Park, CA). The total input DNA for each library was 1. 6 µg. The libraries were loaded at an on-plate concentration of 80 pM using diffusion loading.
Two SMRT cell sequencing runs were performed on the Sequel System IIe in CCS mode using 30-h movie time with 2 h pre-extension and sequencing chemistry v2. 0. The PacBio sequencing was outsourced to the Genome technology Center Nijmegen, Netherlands. All reads were assembled using HIFIASM assembler  [66]  after fastq read files of Xylocopa sp. were generated by consensus calling of Pacbio HIFI sequencing data using CCS tool (https:// github. com/ Pacifi cBio scien ces/ ccs). Reads, which did not take part in the formation of circular consensus sequences, were separated out using in-house developed Perl script (Additional file 36) and were used for closing the gaps with the help of Dentist software  [67]. The gap-closed assembly was further polished using Bowtie2  [68] , Deepvariant  [69] , Samtools and BCFtools  [70].
Genomic microsynteny analysis We traced abundant venom gland transcripts that potentially encoded toxins to homologues in the annotated, highly continuous publicly available genomes of bees (and wasps, ants, parasitoid wasps and sawflies as outgroup species) using the online BLAST suite against genomic databases. To identify conserved synteny blocks, we first identified the reciprocal best-match paralogs from hymenopteran all-against-all BLASTP comparisons of the venom genes. Based on the matching sequences, we then extracted exons from the candidate venom genes and their flanking genes. We used those to create local BLAST databases to survey the selected genomes using local tblastx with an e-value cutoff of 0. 01. We then applied filters to select venom genes containing scaffolds at least 20 kbp in length (to exclude partial genes) with at least two exons.
Orthology prediction and phylogenetic analysis All toxin transcripts together with toxin genes and their outgroup venom-unrelated homologues (e. g. trypsins and chymotrypsins in case of serine proteases) were arranged by gene family and aligned as translated amino acids using MAFFT [119] (L-INS-I, 1000 iterations). Name convention was established to differentiate between genomic sequences (first two letters of both genus and species name, followed by the last three digits of a bioinformatic scaffold ID, followed-if applicableby an abbreviation of a pre-existing gene annotation, followed by letters a to z to differentiate between sequences from the same scaffold); proteo-transcriptomic sequences (names kept the same as generated by transcriptome assemblers); homologues from UniProt and SwissProt databases used to provide outgroups and fill the gaps in sequence space (kept as UniProt or SwissProt IDs, but reduced to 10 characters if needed due to strict limitations of phylip format used by Exabayes).