dataesr/scientific-paragraphs-categorization huggingface dataset:
In the resulting fusion datasets, around 95% of all the adenines were covered at least 25x (Supplementary Figure  S2  and Table  S1 ). The detection of 6mA by SMRT-seq is based on a modification quality value (mQV or QV), reflecting the consistency by which a specific modification is observed at a given position in a subread. Using standard parameters (coverage ≥25x and QV≥20), we found respectively, 0. 158% and 0. 172% of potential 6mA in the CNS of wild type and tet null larvae (Fig. 2a , Supplementary Table  S2 ), which is much more than expected based on LC-MS/MS measurements. However, when we considered the methylation status of these adenines in the individual samples, only 2. 6% were labeled as 6mA in all 3 replicates and 13. 7% in at least two replicates (Fig. 2b , Supplementary Figure  S3  and Table  S3 ), consistent with the idea that SMRT-seq gives a high rate of false positives in organisms containing low 6mA levels  (Douvlataniotis et al.
, 2020; Kong et al. , 2023; Schadt et al. , 2013; Zhu et al. , 2018). Interestingly, increasing the QV strongly ameliorated the fraction of replication and drastically reduced the proportion of potential 6mA both in wild type and tet null datasets, whereas increasing the coverage had little effect (Fig. 2a , b, Supplementary Figure  S3  and Table  S3 ). We thus analyzed SMRT-seq results from wild type and tet null replicates using a coverage≥25x and increasing QV. However, we did not observe any significant differences either in the percentage of 6mA/A or in the fraction of methylation of these potential 6mA even with stringent QV values (Fig. 2c , d and Supplementary Table  S4 ). Hence, while SMRTseq data are noisy, as cautioned in previous studies, they did not reveal an increase in 6mA levels in the absence of TET.
SMRT sequencing analyses Around 250 brains from w 1118 or tet null third instar larvae were dissected for each sample. Tissues were crushed in lysis buffer (100 mM Tris-HCl pH 9, 100 mM EDTA, 100 mM NaCl,1% SDS), digested with RNase A for 15 min at room temperature and 30 min at 65°C, before being incubated with 4 volumes of 3. 2 M LiCl, 0. 9 M KAc for 30 min at 4°C. After centrifugation at 12000 g for 20 min at 4°C, the supernatant was extracted twice with phenol-chloroform-isoamyl alcohol (25:24:1) and DNA was precipitated with isopropanol. 5 µg of DNA were used to prepare each sequencing library. SMRT-seq was performed at the Gentyane Sequencing Platform (Clermont-Ferrand, For each sequenced sample, SMRT-seq reads were aligned using pbmm2 tool (https://github. com/PacificBiosciences/pbmm2) on Drosophila genome (dm6 Ensembl release 70).
For each condition (wild type or tet null ), a fusion of alignments of the three biological replicates was done using « samtools merge »  (Li et al. , 2009). 6mA detection was performed on individual and on merged samples with IpdSummary tool from KineticTools (http://github. com/PacificBiosciences/kineticsTools) applying the following parameters: --identify m6A --numWorkers 16 --pvalue 0. 01 --identifyMinCov 5 -methylFraction. To detect 6mA with higher confidence, we applied several thresholds on coverage and modificationQV (QV) with homemade scripts in bash and R. 6mA genomic repartition was obtained using Annotatepeaks tool from Homer suite  (Heinz et al. , 2010). To find associated motifs for 6mA, we used MEME web application  (Bailey et al. , 2009). The different heatmaps were generated with R package "pheatmap" (V.
Acknowledgements We are grateful to  Dr. A. Molaro  for critical reading of the manuscript and to the members of the iGReD for helpful discussions. We also thank  Dr V. Gautier  and the Gentyane platform at the  Clermont-Ferrand INRAE  for PacBio sequencing and the  iGReD CLIC imaging facility  for help with confocal experiments. We thank the  Drosophila Bloomington Stock Center ,  Dr. R. Steward  ( Piscataway ),  Dr. D. Chen  ( Beijing ) for fly stocks, as well as the  Developmental Study Hybridoma Bank  for antibodies. This project was supported by grants from the  Agence Nationale de la Recherche  ( ANR-17-CE12-0030-03 ),  Fondation ARC  ( PJA20171206371 ) and  I-Site Cap20-25  (EpiMob) to LW. MB was supported by fellowships from the  Université Clermont-Auvergne  and the  Fondation pour la Recherche Médicale  (FRM).
GG was supported by fellowships from the  Université Clermont-Auvergne  and the  Ligue Nationale Contre le Cancer. MH was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )  TRR-319 TP C03 ,  SPP1784 ,  HE 3397/13-2  and  HE 3397/14-2.
Glass-ceramics and joining processes The composition of the two glass-based joining materials was designed by resorting to the SciGlass 6. 6 software database. The thermal expansion and the glass transition temperature (T g ) predicted by the software were the main parameters that were considered. The glass referred to as T1 had previously been developed by D'Isanto et al. , and it had been used as an oxidation protective coating for a titanium suboxide thermoelectric material  [22]. The glass referred to as T2M contained the same oxides as T1, but with different weight percentages, although the same TiO 2 /Y 2 O 3 ratio was kept to obtain Y 2 Ti 2 O 7 as the main crystalline phase after the devitrification process. Starting from the known T1 glass composition and properties, the T2M glass was designed to obtain lower characteristic temperatures so as to allow joining to take place at lower temperatures.
Fig. 1  shows the typical joints that were obtained, and indicative dimensions of the two different joining configurations, where the average thickness of the glass-ceramic layer was around ~200-300 µm. The crystalline phases that formed within the glass T1 and T2M during the joining processes were analyzed by means of X-ray diffraction (XRD) with a Malvern PANalytical X'Pert PRO diffractometer and the support of X-Pert HighScore Software. Morphological and compositional characterizations of the joints and the glass-ceramics were conducted using scanning electron microscopy (SEM, JCM-6000Plus, JEOL) and field emission scanning electron microscopy (FE-SEM, Merlin electron microscope, ZEISS, Germany). Energy dispersive X-ray spectrometry (EDS) was utilized for the elemental mapping (Supra TM 40, ZEISS, Germany) of the glass-ceramic crystalline phases.
Acknowledgments The research carried out to write this article was funded under the  CEM-WAVE  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 958170. This document only reflects the authors' view. The European Commission is not responsible for any use that may be made of the information it contains. The authors would like to thank  Ms. Serena Paglialonga  for her help in the research.
Class Demospongiae is the largest in the phylum Porifera (Sponges) and encompasses nearly 8,000 accepted species in three subclasses: Keratosa, Verongimorpha, and Heteroscleromorpha. Subclass Heteroscleromorpha contains *90% of demosponge species and is subdivided into 17 orders. The higher level classification of demosponges underwent major revision as the result of nearly three decades of molecular studies. However, because most of the previous molecular work only utilized partial data from a small number of nuclear and mitochondrial (mt) genes, this classification scheme needs to be tested by larger datasets. Here we compiled a mt dataset for 136 demosponge species-including 64 complete or nearly complete and six partial mt-genome sequences determined or assembled for this study-and used it to test phylogenetic relationships among Demospongiae in general and Heteroscleromorpha in particular.
However, it left phylogenetic relationships among these orders mostly unresolved. Furthermore, because the new system-now accepted as the framework for the demosponge classification in the World Porifera Database http://www. marinespecies. org/porifera/index. php-was based primarily on partial nuclear rRNA and mitochondrial cox1 data, it needed to be tested with larger datasets. Mitochondrial genomes (mt-genomes) provide two primary data types for phylogenetic inference: DNA sequence and gene arrangements. Mt-sequence data (often translated and concatenated coding sequences) have been used extensively in molecular phylogenetics  [30] [31] [32] [33] [34]  and are particularly well suited for the analysis of demosponge relationships because of the low rate of evolution and relatively homogeneous composition of mtDNA sequences in this group  [26, [35] [36] [37] [38].
Mt-gene arrangement data have also been used both for the reconstruction of global animal relationships  [26, 35]  and for testing specific phylogenetic hypotheses (e. g. ,  [39] ). However, this dataset provides fewer characters for phylogenetic inference and its analysis is computationally more challenging, so the latter application is more common. As part of the Porifera Tree of Life project https://portol. org/, we determined mtDNA sequences from 64 demosponges and assembled six more from available genomic and transcriptomic data. Here we report these data and use them, along with publicly available mtgenomes to reconstruct and test demosponge phylogenetic relationships. Importantly, we included in our dataset Myceliospongia araneosa Vacelet & Perez, 1998 (currently classified as Demospongiae incertae sedis) and resolved its phylogenetic position.
Data collection Overview. For this project, we PCR amplified, sequenced, assembled, and annotated complete or nearly complete mtDNA sequences from 57 species of demosponges including 48 belonging to the subclass Heteroscleromorpha. Two additional mt-genome sequences were assembled from low coverage Illumina DNAseq data generated for this project. Furthermore we assembled and annotated five complete mtDNA sequences from publicly available DNAseq and RNAseq data (Table  1 ). Combined with 61 previously published mt-genomes, we compiled a dataset of 125 complete and nearly complete mt-genomes https://lavrovlab. github. io/ Demosponge-phylogeny/published. html. To this dataset we added partial mtDNA sequences from one species of Merliida and one species of Desmacellida as well as a partial mtDNA sequence from an unknown species most closely related to Plenaster craigi.
In addition, we utilized available RNAseq data from Scopalina sp. CDV-2016 and newly generated DNAseq data from Hymerhabdia typica and Mycale escarlatei to assemble most of the mitochondrial coding sequences for these species. Finally, we used mt-coding sequences from five incomplete mtgenomes reported by  Plese et al. (2021) , resulting in a final dataset of 136 taxa. Taxon sampling. Species used in this study were chosen to cover much of the suprafamilial diversity in Heteroscleromorpha (Table  1 ). Eleven additional species of demosponges outside of this subclass were sampled and included in the analyses as outgroups (Table  2 ). Tissue subsamples, and/or DNA aliquots were derived from three main sources: (1) PorToL-supported expeditions to the Smithsonian Tropical Research Institute at Bocas del Toro, Panama, in 2009 and 2012, where subsamples of specimens were stored in 3M Guanidinium Chloride solution; (2) the Moorea Biocode project, Moorea, French Polynesia (https://geome-db.
Sequencing was carried on MiSeq and HiSeq instruments at the DNA Sequencing and Synthesis Facility of the ISU Office of Biotechnology. Sequence assembly. Different assemblers were used depending on the type of data collected. The STADEN package v. 1. 6. 0  [43]  with Phred basecaller  [44, 45]  was used to assemble the Sanger sequences. Abyss  [46] , Mira  [47] , PCAP  [48] , and SPAdes  [49]  were used to assemble 454 and Illumina sequences. In most cases, several programs were utilized for the assembly and results compared and compiled together. When barcodes were used, sequences were first separated by the barcode. When barcodes were not used, species selection was carried out to exclude closely related species from the same library. In the latter case, assembled sequences were identified using short sequences generated for primer design.
PCR and Sanger sequencing was used to resolve any ambiguities. Sequence annotation. We used flip v. 2. 1. 1 (http://megasun. bch. umontreal. ca/ogmp/ ogmpid. html) to predict ORFs in assembled sequences; similarity searches in local databases and in GenBank using FASTA  [50]  and NCBI BLAST network service  [51] , respectively, to identify them. Protein-coding genes were aligned with their homologues from other species and their 5' and 3' ends inspected for alternative start and stop codons. Genes for small and large subunit ribosomal RNAs (rns and rnl, respectively) were identified based on their similarity to homologous genes in other species, and their 5' and 3' ends were predicted based on sequence and secondary structure conservation. Transfer RNA genes were identified by the tRNAscan-SE program  [52].
Phylogenetic inference Phylogenetic analysis based on mitochondrial coding sequences. Two datasets were constructed for phylogenetic analysis based on mitochondrial coding sequences. The first dataset comprised 136 demosponge species for which we had complete/nearly complete mtDNA data or most individual mt-coding sequences (CDS). Inferred amino acid sequences of individual mitochondrial proteins were aligned with Mafft v7. 475  [54]  using L-INS-i stategy. Conserved blocks within the alignments were selected with Gblocks 0. 91b  [55]  using relaxed parameters (parameters 1 and 2 = 0. 5, parameter 3 = 8, parameter 4 = 5, all gap positions in parameter 5). Cleaned alignments were concatenated into a supermatrix containing 3,634 amino acid positions for 136 species. The second dataset comprised the same 136 demosponge species plus nine additional species of Homoscleromorpha and was constructed as the first dataset, except the concatenated alignment was filtered with CD-Hit  [56]  to remove sequences with >95% identity.
The final alignment for the second dataset contained 83 species and 3,633 amino acid positions. Both datasets were analyzed with PhyloBayes MPI 1. 9  [57]  under the CAT+GTR model (-cat -gtr). The chains were sampled every 10th tree after the first 1000 burn-in cycles to calculate consensus trees. Gene order analysis. Mitochondrial gene orders were converted to gene adjacency matrices using the gogo program (https://github. com/dlavrov/bio-geneorder, unpublished). The matrices were further modified as required by TNT and RAxML and used in these programs to infer the Maximum Parsimony (MP) and Maximum Likelihood (ML) trees, accordingly. For the parsimony analysis, we tried both the traditional (i. e. , random addition of sequences + TBR branch swapping followed by additional branch swapping of trees in memory) and "new technology" (i.
e. , with Ratchet, Tree-Drifting, and Tree-Fusing followed by additional branch swapping) strategies implemented in TNT  [58]. Run scripts for the TNT analysis are available in the supplementary GitHub repository. For the ML analysis, we used the multistate model in RAxML-NG v. 1. 1. 0  [59] : "raxml-ng --all --msa 77taxa. phy --model MUL-TI13_MK. " To check for the effect of more frequent tRNA rearrangements in animal mitochondrial genomes, we created an alternative adjacency matrix based, where position of each gene was recorded relative to the position of the closest major (i. e. , protein or rRNA) upstream and downstream genes and repeated the MP and ML analyses.
Phylogenetic analysis based on gene order data Comparison of mitochondrial gene orders in heteroscleromorph species without major tRNA gene loss revealed that they shared with each other at least six gene boundaries, with the mean number of shared boundaries between a given species and the rest of heteroscleromorphs varying between *10 for Plenaster craigi and *29 for Svenzea zeai and Spongillida (freshwater sponges) (mean = 24. 3). The gene orders were converted into a gene-based matrix, where the identities and transcriptional orientations of the upstream and downstream neighbors of each gene were recorded. The matrix was utilized for maximum likelihood (ML) analysis in RAxML-NG  [59]  and maximum parsimony (MP) analysis in TNT  [58]. The results of these analyses (Fig  3  First, C1 clade (Spongillida, Vetulina sp.
Molecular clock analysis We used the same dataset as for the sequence-based phylogenetic analysis as well as the phylogenetic tree inferred in that analysis to estimate times of divergences among major lineages of demosponges  (Fig 4, S3 Fig). Fixing the root of Demosponge phylogeny (between Heteroscleromorpha and Keratosa+Verongimorpha) between 541 and 515 MYA, we estimated that the divergence between Haplosclerida and the the rest of Heteroscleromorpha occurred between 534 and 488 MYA, followed by the split between C1 and C2+C3+C4 between 512-456 MYA, with the splits among C2-C4 between 494 and 417 MYA. Furthermore, most mean estimates for the divergences among orders of Heteroscleromorpha fall between the Ordovician and the Devonian, while those for the basal splits within the orders (that can be used as a proxy for the crown group) between Early Permian to Late Triassic.
Assembling the new dataset of mtDNA data for demosponges Despite rapid progress in sequencing technologies, sequence data remain scarce for many taxa of marine animals, including demosponges. When such data are available, they are often limited to partial sequences of individual genes, commonly those for nuclear rRNA and/or mitochondrial cox1. While even these partial data have been instrumental for reevaluation of demosponge relationships, larger datasets are needed to test and refine proposed phylogenetic hypotheses. Ultimately, a representative sampling of genomic loci should be a dataset of choice for a phylogenetic analysis. However, such datasets are available for only a few species of sponges. Furthermore, assembling and analyzing genomic datasets present various challenges, especially in sponges, where contamination by foreign DNA is always a factor  [69].
Here we compiled and analyzed a large dataset of mt-genomic sequences of demosponges, including 66 mtDNA sequences determined or assembled for this study. Importantly, we sampled all but one (Trachycladida) of the proposed orders within Heteroscleromorpha, the largest group of demosponges. We used this dataset to test phylogenetic relationships and reconstruct the timing of major splits within the group. Our results are largely consistent with and add resolution to phylogenetic studies based on single-gene data, compiled and synthesized by Morrow and Cardenas  [29]. For example, our sequence-based phylogenetic analysis not only groups Spongillida with Sphaerocladina (Vetulina)-as proposed by Redmond et al. [70]  and investigated in more details by Schuster et al. [71] -but also provides strong support for their association with Scopalinida (clade C1).
flava differs from other members of the genus in its skeletal arrangement and lacks characteristic granular cells and large embryos/larvae found in the type species  [75]. Thus, in the future, S. flava will need to be renamed and reassigned to the family Dictyonellidae. Phylogenetic position of emerging sponge genomic model systems. Our dataset included nine species of sponges for which high throughput DNA and/or RNA data were available and which, therefore, could be considered as emerging sponge model systems. Although the phylogenetic position of most of these species was as expected, two exceptions were found. First, Stylissa carteri (order Scopalinida) grouped closely with Axinella corrugata (order Axinellida) and both of them were placed as a sister group to Axinella damicornis (order Axinellida) and Hymerhabdia typica (order Agelasida) within Agelasida.
Furthermore, the coding sequences between Stylissa carteri and Axinella corrugata were >99% identical, indicating a possible misidentification of the sponge. Second, the recently described abyssal sponge Plenaster craigi, grouped with the species from the orders Axinellida, Biemnida, and Bubarida in sequence-based phylogenetic reconstructions, but was not placed in any of these groups. Thus, it should not be consider a representative of Axinellida  [76]  and its phylogenetic position should be further investigated. Finally, we note that there was a significant level of cross-contamination between DNA sequence data from Xestospongia testudinaria and Stylissa carteri  [16] , such that both mitochondrial genomes can be assembled from either dataset.
Timing phylogenetic divergences In addition to resolving phylogenetic relationships among clades of Heteroscleromorpha, we provide molecular clock estimates for the divergences among them. While several molecular clock studies using demosponge mt-genome data have been conducted  [36, 38, 82] , they utilized much smaller datasets of mitogenomic sequences. Furthermore, two of these studies  [38, 82]  used a suboptimal selection of the outgroup species, resulting in a likely erroneous phylogenetic reconstruction, which in turn biased molecular dating results (see below). One major difficulty in molecular clock analysis of sponges is the scarcity and uncertainty of available calibration points. While the Paleozoic record of sponges is well established and extends to the Lowermost Cambrian (529-541 Ma)  [83] , the taxonomic affinities of Paleozoic sponges are often controversial.
Promise, pitfalls, and limitations of Porifera phylomitogenomics Mitochondrial DNA (mtDNA) in general and animal mtDNA in particular has been a popular and a widely used molecular marker in phylogenetic studies  [93]. This popularity is due to a combination of several features that facilitate the acquisition of mtDNA, including its typically circular organization, large copy number per cell, and a high proportion of coding sequences, with those that simplify analysis, such as absence of paralogs and introns, stable gene content and gene order. Phylogenetic analyses of sponge mt-genomes additionally benefit from relatively low rates of sequence evolution and a more homogeneous nucleotide composition (at least within individual classes of sponges)  [94]. Indeed, previous studies by our and other groups have demonstrated the utility of mitogenomic datasets for reconstructing phylogenetic relationships within the phylum Porifera and the overall congruence among the results of phylomitogenomic analyses with those based on 18S, 28S, and transcriptomic datasets  [26, 36, 42, 95].
Nevertheless, as with any dataset, phylogenetic analysis based on mitogenomic data can fail to reconstruct the true evolutionary history of organisms. The primary challenges faced by a phylogenetic analysis are erroneous (non-phylogenetic) signal due to stochastic noise and/or systematic bias  [96]  as well as incongruences between gene trees and the species tree due to non-orthology of sequences, horizontal gene transfer, or incomplete lineage sorting  [97]. Although large genomic (including mitogenomic) datasets generally benefit from the increase in the ratio of the true phylogenetic signal (which adds across genes) to random noise (which does not), they do not solve the problem of non-phylogenetic signal caused by systematic biases, which also accumulates  [98]. Similarly, while paralogs are rare in mtDNA and duplicated sequences are quickly eliminated, potential problems exist both with horizontal gene transfer (primarily through mtDNA introgression)  [99]  and incomplete lineage sorting  [100].