dataesr/scientific-paragraphs-categorization huggingface dataset:
Small RNA sequencing Total RNA was extracted from the cell line using the standard RNA extraction method with QIAIzol (Qiagen), quantitated with NanoDrop-1000 spectrophotometer (Thermo Fisher Scientific) before integrity assessment with an Agilent 2100 Bioanalyzer (Agilent Technologies). For small RNA-seq, 1 μg of total RNA from SH and SC cells was used for library preparation with Illumina TruSeq small RNA sample preparation Kit. Three independent experiments (two clones per cell line) for each condition, were sequenced (10 pM) on HiSeq2500 (Illumina) with single read for 51 cycles. Small RNA sequencing data was analyzed using iSmaRT  [34]  to identify the sncRNA families studied, i. e. miRNAs (miRBase v21), PIWIinteracting RNAs (piRNABank), and tRNA-derived fragments (tRF, Human genome assembly, GRCh37/hg19) with Minimum Read Count of 3.
Bioinformatics analysis To identify differentially expressed miRNAs, piRNAs or tRF between SH and SC samples, we utilized the DESeq2 algorithm based on the normalized number of counts mapped to each sncRNA transcript  [36]. Functional enrichment analyses were performed using the databases DAVID, http://david. abcc. ncifcrf. gov/), Enrichr (http:// amp. pharm. mssm. edu/Enrichr/) and FunRich (www. funrich. org), based on the list of genes associated to the deregulated sncRNAs (P-adj. ≤0. 05; FC ≥|1. 5|). Data integration, heatmap visualization of differentially expressed transcripts and functional enrichment plots were done with R/Bioconductor packages and the Multi Experiment Viewer software (MeV v4. 9)  [37]. To validate the bioinformatic analysis of small RNA-seq experiments, we compared the global miRNA expression profile of SC from our study with the global miRNA expression profile of the MDA-MB-231 and MCF10 cell lines obtained from the study of  Zhou et al.
, 2014 [14] , in which the authors profiled the cellular small RNAs isolated from these two cell lines by Solexa deep sequencing. Briefly, normalized data were downloaded from GEO (ID#GSE50429) and the miRNAs in common to our libraries were selected (n=228). The comparison was made using a linear regression model in R. The name or GenBank ID, chromosome number, genomic position, strand orientation and sequence length of piRNAs was obtained from piRNAbank (http:// pirnabank. ibab. ac. in/simple_search. html), and validated with the NCBI Nucleotoide Database (https://www. ncbi. nlm. nih. gov/nuccore/). The number of copies in the genome and the genomic loci was obtained from the UCSC Genome Browser. To identify potential target genes of relevant piRNAs, we employed the NCBI database (Human Genomic plus Transcript) based on sequence complementarity using the reverse complement of the piRNA sequence as input.
The HomoloGene tool from the NCBI database was employed to evaluate the grade of conservation of the selected putative mRNA targets among different mammalian species. For miRNA target prediction and functional annotations, we used the miRDB online resource (http://www. mirdb. org/miRDB/). To evaluate differences in the abundance of each species of tRF among the upregulated and downregulated group, we used Fisher Test to compare their frequencies with the expected frequencies according to the Genomic tRNA database (http://gtrnadb. ucsc. edu/). For the identification of putative proteins based on amino acids composition, we employed the AAcompIdent tool (http://web. expasy. org/aacompident/). For piRNA and snoRNA expression levels across human tissues and cell lines we employed the DASHR database (http://lisanwanglab.
org/DASHR/ smdb. php). To evaluate and compare differentially expressed miRNAs found in this study with miRNAs deregulated in breast cancer tumors, we used the YM500v3 database (http://driverdb. tms. cmu. edu. tw/ym500v3) which employ TCGA data to contrast normal vs cancer tissue. We selected the comparison of 1096 primary solid breast cancer tumors against 104 samples of normal breast tissue  [38]. Survival section of YM500 database was employed to survival analysis of common deregulated miRNAs.
Chandra X-ray spectroscopy We observed HE 0227-0913 with the Chandra Advanced CCD Imaging Spectrometer (ACIS-S; PI: G. Tremblay) on 12 October 2015 (obs. ID 18188), 10-14-2015 (obs. ID 18684), and 16 October 2015 (obs. ID 18685) for a total of 93. 4 ks. HE 0351+0240 was observed on 14 December 2015 (obs. ID 18189) and 15 December 2015 (obs. ID 18726) for a total of 93. 7 ks. Due to the high flux of the objects, we used a 1/8 subarray to minimize pileup. The data were reduced and analyzed with the CIAO 4. 9 software  (Fruscione et al. 2006 ). The 'chandra_repro' script was used with the CALDB 4. 7. 3 set of calibration files to reprocess the level 1 event files. We filtered the data to eliminate cosmic rays and bad grades, and verified no significant changes in the background with time.
The host galaxy of HE 0227-0913 is a disk which is clearly supported by the ionized gas velocity field. This velocity field was modeled using the KinMS software 2  (Davis et al. 2013)  to obtain an inclination angle of 67. 4 ± 4. 5 •. Ongoing star formation dominates the gas excitation in a circumnuclear disk, with an inner star forming ring a few arcsec (< 1 kpc) in diameter and an apparently outer star forming ring at ∼20 (7 kpc) distance that is likely related to the spiral arms. The presence of regions dominated by ionization from OB-stars is consistent with star formation within the ISM of the galaxy; it has an estimated H 2 mass of 4 × 10 8 M  (Bertram et al. 2007) , and an H i mass of 2. 6 × 10 9 M  (König et al. 2009). Another striking feature is a bright AGN-ionized arc-like feature of ionized gas about 5-15 (or 5 kpc) North of the nucleus, which is redshifted in velocity space with respect to the underlying disk motion but nearly at zero offset from the systemic velocity.
The high velocity dispersion may not entirely be intrinsic to the AGN-ionized gas, but additionally broadened due to the superposition of the emission line kinematics of the gas disk. 2 https://github. com/TimothyADavis/KinMS The interacting galaxy system of HE 0351+0240 does not show any strong signatures of ongoing star formation (there is no 12 CO(1 -0) detection for this galaxy;  Bertram et al. 2007 ), as all of the ionized gas is ionized by the AGN and displays a filamentary structure with a complex kinematic pattern. The velocity dispersion is elevated close to the AGN nucleus, but not in the extended filamentary structures. In the following we will test the hypothesis that the AGNionized gas filaments represent the interface of a slowly expanding hot gas bubble with the surrounding cool gas.
X-ray analysis We searched for extended X-ray emission by comparing our observations to the Chandra PSF, the accurate modeling of which requires the point-source spectrum. As Type 1 AGN, the nuclei of HE 0227-0913 and HE 0351+0240 are very bright in X-rays, with high enough fluxes for pileup to be significant in warping the spectrum. However, the shutterless ACIS detector on Chandra cause photons from a bright source to be detected while data are being read out, forming a linear streak through the source in the image. We can therefore use photons in this readout streak to model the point source spectrum for each source, as only the unresolved bright AGN is represented in this region without the pileup issues. We used the CIAO tool 'dmextract' to extract the spectrum in each observation, and the tools 'mkacisrmf' and 'mkarf' to make the response files at the location of the central source.
To simulate the PSF we used the ChaRT online ray tracing tool  (Carter et al. 2003) , where we input the modeled source spectrum, and then used the MARX software to project the rays onto the ACIS-S detector. We corrected for SIM drift and offset, and set the 'AspectBlur' parameter to 0. 19" to avoid pixelization effects in the pseudo-event file (the standard value for ACIS-S). The 'marxpileup' program was used to simulate pileup, where the 'FrameTime' parameter was set to 0. 7 seconds, as it is reduced when using 1/8 of the chip. The coadded data are shown in Figure  5 , along with the simulated PSF, shown in a log color scale. We used the CIAO tool srcflux to obtain the X-ray fluxes of each source using the resulting PSF models. We calculated the 0. 3-10 keV flux to be 2. 12 × 10 -11 erg cm -2 s -1 for HE 0227-0913 (consistent with  Dewangan et al.
2007 ) and 1. 91 × 10 -11 erg cm -2 s -1 for HE 0351+0240. This corresponds to luminosities of L 0. 3-10 keV = 10 43. 1 erg s -1 and L 0. 3-10 keV = 10 43. 6 erg s -1 , respectively. Assuming a 2-10 keV bolometric correc- Notes. The 0. 3-10 keV (absorbed) flux is in units of erg cm -2 s -1. The luminosities are in units of erg s -1 and are corrected for Galactic absorption. The model fits were performed on the binned data using Sherpa. tion of 20  (Lusso et al. 2012) , we estimate the Eddington ratios (λ Edd ≡ L bol /L Edd ) to be ∼ 1 for HE 0227-0913 and ∼ 0. 5 for HE 0351+0240. We compared the radial surface brightness profiles of the data to the PSF images to look for evidence of extended emission. We used the CIAO tool 'dmextract' to extract the events in 20 concentric annuli centered on each source from 1 to 20 (excluding the region of the readout streak).
Aphelids are little-known phagotrophic parasites of algae whose life cycle and morphology resemble those of the parasitic rozellids (Cryptomycota, Rozellomycota). In previous phylogenetic analyses of RNA polymerase and rRNA genes, aphelids, rozellids and Microsporidia (parasites of animals) formed a clade, named Opisthosporidia, which appeared as the sister group to Fungi. However, the statistical support for the Opisthosporidia was always moderate. Here, we generated full life-cycle transcriptome data for the aphelid species Paraphelidium tribonemae. In-depth multi-gene phylogenomic analyses using several protein datasets place this aphelid as the closest relative of fungi to the exclusion of rozellids and Microsporidia. In contrast with the comparatively reduced Rozella allomycis genome, we infer a rich, free-living-like aphelid proteome, with a metabolism similar to fungi, including cellulases likely involved in algal cell-wall penetration and enzymes involved in chitin biosynthesis.
Results Aphelids occupy a deep pivotal position and are the sister group to fungi. To generate the aphelid transcriptome and because P. tribonemae has a complex life cycle (Fig. 1 ), we maximized transcript recovery by constructing two cDNA libraries corresponding to young and old enrichment cultures of its host, the yellow-green alga Tribonema gayanum, infected with the aphelid. Accordingly, transcripts corresponding to zoospores, infective cysts, trophonts, plasmodia, sporangia and sporocysts were represented  11. After paired-end Illumina HiSeq2500 sequencing, we assembled a metatranscriptome of 68,130 contigs corresponding to the aphelid, its host and bacterial contaminants. After a sequential process of supervised cleaning, including comparison with a newly generated transcriptome of the algal host, we obtained a final dataset of 10,439 protein sequences that were considered free of contaminants.
The final predicted proteome (Paraphelidium tribonemae version 1. 5; Supplementary Data 1) was 91. 4% complete according to BUSCO  17. We found no stop codons interrupting coding sequences. Therefore, in contrast to Amoeboaphelidium protococcarum, for which the TAG and TAA stop codons appear to encode glutamine  12  , P. tribonemae does not possess these modifications from the canonical genetic code. Because resolving the phylogenetic relationships in this part of the eukaryotic tree is challenging due to the occurrence of lineages with very different evolutionary rates, notably the fastevolving Microsporidia, we used three different datasets and carried out a battery of phylogenomic analyses including and excluding the fastest-evolving species and amino acid positions from our datasets. The three datasets have been previously used to study the phylogeny of animals and related protists (Holozoa, the sister group to Holomycota) within Opisthokonts (SCPD)  18  , Microsporidia (including highly conserved genes for this fastevolving group, BMC)  19  , and Microsporidia and related deeperbranching lineages (maximizing the putative orthologs in the whole clade, GBE)  15.
First, we incorporated P. tribonemae orthologs to a dataset of 93 single-copy protein domains (SCPD dataset) previously used for phylogenomic analyses of basalbranching opisthokonts  18  , updating it with several microsporidian species including the early-branching Mitosporidium daphniae  20  , the metchnikovellid Amphiamblys sp. 15  , and the recently released genome of Paramicrosporidium saccamoebae  21. We then generated Bayesian inference trees reconstructed under the CAT-Poisson mixture model  22  and maximum likelihood (ML) trees reconstructed under C60, the closest mixture model to CAT  23  , for a taxon sampling including 49 species (22,976  positions). The two phylogenomic analyses yielded the same topology. We recovered the monophyly of all opisthokont lineages previously reported  18, 24.
In contrast with previous analyses based on 18S rRNA and RNA polymerase genes  12, 14  , Opisthosporidia appeared as paraphyletic and the aphelid was placed as the sister lineage to fungi (Fig. 1 ). However, the ML bootstrap support was moderate (ultrafast bootstrapping; 96% ubs) and the Bayesian probability was low (0. 66 pp; even if the two Bayesian chains converged in terms of likelihood, the consensus topology was not distinctively supported by the other chain). To limit potential long-branch attraction artefacts derived from the inclusion of fast-evolving Microsporidia, we built a reduced dataset without the thirteen fastest-evolving microsporidian species (SCPD36, 36 species; 24,413 positions). Bayesian and ML trees again reproduced the same topology (Supplementary Fig. 1 ) with higher pp (0.
88) and ubs (95%) values, but still not fully supporting the consensus topology (Fig. 1 ; Table  1 ). To resolve the phylogenetic position of the aphelid with higher support, we analyzed two additional datasets previously used for phylogenomic analyses of Microsporidia with the same two sets of 49 and 36 opisthokont species. The resulting datasets were called BMC49 and BMC36 for a set of 53 protein markers  19  which, after concatenation, yielded 15,744 and 16,840 amino acid positions, respectively; and GBE49 and GBE36 for datasets with 259 protein markers  15  , which resulted in 84,810 and 96,640 amino acids, respectively. For all datasets, Bayesian and ML trees recovered the monophyly of Paraphelidium and fungi (Supplementary Fig. 1 ) and, for the two datasets without fast-evolving Microsporidia, posterior probabilities and ML bootstrap support were maximal (Fig.
1 , Table  1 , Supplementary Data 2). In addition, we carried out alternative topology tests for the three datasets containing 49 species. All of the alternative topology tests supported P. tribonemae as the sister group to fungi (AU test pvalue significantly excluded if <0. 1), and both BMC49 and GBE49 significantly excluded the Opisthosporidia monophyly (AU p-value 0. 0039 and 0. 0000, respectively; Supplementary  Data 2). Finally, to minimize systematic error in ML analyses due to the inclusion of fast-evolving sites in our protein datasets, we progressively removed the 5% fastest-evolving sites and reconstructed the phylogenomic trees. This resulted in the increase of support for the monophyly of fungi and the aphelid, which became very high (>98% ubs; see Supplementary Data 2) until 50% (SCPD dataset) or up to 80% (GBE dataset) of sites were removed, when phylogenetic signal was expected to be insufficient to resolve such deep phylogeny.
2a ; Supplementary Data 1 and 5) lend credit to the hypothesis that class V chitin synthases are involved in germ tube polar growth. Among the rest of chitin-related enzymes, we identified twelve sequences (at least five different homologs) of chitin deacetylase  30, 31  (Supplementary Fig. 2b ). We detected at least three class II chitinase homologs (eight total sequences), which are ancestral in opisthokonts, containing the Glyco_hydro_18 (PF00704) domain, and a class I chitinase (CTSI) with a Glyco_hydro_19 (PF00182) domain  32. The latter included the catalytic site, an N-terminal predicted signal peptide and a transmembrane region, suggesting an extracellular chitinase activity. CTSI has a peculiar phylogenetic distribution in eukaryotes, occurring only in Viridiplantae, Fungi, Opisthosporidia and Ecdysozoa (Supplementary Fig.
Surprisingly, we identified two 1,3-beta-glucan synthase (FKS1) homologs (four sequences), with split Glucan_synthase (PF02364) and FKS1_dom1 (PF14288) domains (fused for the phylogenetic analysis) (Supplementary Fig. 2d ). The presence of FKS1, absent in Rozella and Microsporidia, in aphelids traces its origin back to the ancestor of fungi and aphelids. To feed on the algal cytoplasm, aphelids need to traverse the algal cell wall but, so far, the specific penetration mechanism, whether mechanical (germ-tube penetration through the gap between the two algal cell-wall halves) or enzymatic (digestion of algal cell-wall components) was uncertain  7, 11. Scanning electron microscopy (SEM) observations showed both, clear cases of mechanical penetration between the two algal cell-wall halves, and also infecting cysts scattered on the algal cell-wall surface (Fig.
Tribonema cell walls contain cellulose II based on 1,6linked glucan (alkali soluble cellulose), 1,3 and 1,4-linked xylose, 1,3-linked rhamnose and, mostly, 1,3; 1,4; and 1,6-linked glucose  33. We performed sequence similarity searches of known fungal cellulases  34  using the database mycoCLAP  35  , which contains functionally characterized proteins, to search for these enzymes in aphelids, followed by phylogenetic analyses. In support of an enzymatic algal cell-wall penetration, we identified various cellulases in the Paraphelidium transcriptome belonging to glycosidehydrolase families GH3 and GH5. We detected three homologs of the GH3 cellulase beta-glucosidase/xylosidase 36 , which is not found in other opisthosporidians but is present in fungi, amoebozoans, several opisthokonts and other protists, as well as bacteria.
To comparatively assess the metabolic potential of aphelids, we investigated the presence of orthologous groups related to eight primary metabolism categories (Gene Ontology) in the transcriptome of Paraphelidium, using eggNOG annotation  38. We thus identified 1172 orthologous groups in Paraphelidium and a set of 41 eukaryotic species including representatives of major fungal lineages, opisthokont protists and other eukaryotic parasites (Supplementary Data 3). Based on their orthologous group distribution, we built a dissimilarity matrix that was analyzed by Principal Coordinate Analysis. The first axis clearly separated Paraphelidium from Microsporidia, Mitosporidium, Paramicrosporidium and Rozella, the latter two positioned near one another and having an intermediate position similar to other protist parasites (e.
After quality/Illumina Hiseq adapter trimming using Trimmomatic v0. 33  55  , we retained 8,601,196 forward and 6,535,164 reverse reads (young) and 10,063,604 forward and 4,563,537 reverse unpaired reads (old). Resulting reads were assembled using Trinity  56  with min_kmer_cov 2 and nor-malize_reads options. This initial assembly, Partr_v0, contained 68,130 contigs. 47 of them were identified as ribosomal RNAs using RNAMMER  57  and included sequences of Paraphelidium tribonemae, its host Tribonema gayanum, chloroplasts and bacteria. No other eukaryotic ribosomal genes were detected. To decontaminate P. tribonemae from host and bacterial sequences, we used Blobtools  58  and, as input files, our assembly, a read map file obtained with the fast gapped-read alignment program Bowtie2 59 and a formatted hit list obtained after applying diamond-blastx  60  to our sequences against the NCBI RefSeq database (e-value threshold 1e-10).
To build the blobDB file, we used the first 10 hits for each putative transcript with the bestsum as tax-rule criterion to get a taxonomic affiliation at species rank. Sequences with no hit (41,836 sequences, 61. 4% of the assembly) and sequences affiliated to bacteria, archaea, or viruses were removed. The remaining 21,688 eukaryotic sequences were blasted against a partial REFSEQ protein database (e-value threshold 1e-10) containing sequences of 5 fungi (Spizellomyces punctatus DAOM BR117, Batrachochytrium dendrobatidis JAM81, Agaricus bisporus var. bisporus H97, Cryptococcus neoformans var. grubii H99 and Cryptococcus neoformans var. neoformans JEC21) and 6 stramenopiles (Nannochloropsis gaditana CCMP526, Phytophthora sojae, Phytophthora parasitica INRA-310, Phytophthora infestans T30-4, Aureococcus anophagefferens and Aphanomyces invadans).
If the first hit was a stramenopile (potential host origin), the sequence was discarded. 13,786 transcripts appeared free of contamination. Transdecoder v2 (http://transdecoder. github. io) with-search_pfam option yielded 16,841 peptide sequences that were filtered using Cd-hit v4. 6  61  with a 100% identity resulting in 13,035 peptides. These peptides were functionally annotated with eggNOGmapper  38  using both DIAMOND and HMMER mapping mode, eukaryotes as taxonomic scope, all orthologs and non-electronic terms for Gene Ontology evidence. This resulted in 10,669 annotated peptides for the Paraphelidium tribonemae predicted proteome (version Partr_v1). Finally, we generated transcriptomic data for the Tribonema gayanum host using the same methods and parameters for sequencing, assembly and annotation, as described above.
After blasting Partr_v1 against a database of Tribonema gayanum proteins, we further excluded 219 + 11 additional proteins that were 100% or >95% identical to Tribonema proteins. After these additional cleaning steps, version 1. 5 of the Paraphelidium tribonemae predicted proteome contained 10,439 proteins (Supplementary Data 1). The completeness of this new transcriptome was estimated to 91. 4% with BUSCO 17 using the eukaryotic ortholog database 9 (protein option). Phylogenomic analyses. We used three different protein datasets previously used for phylogenomic analyses of Opisthokonta and, more specifically, Microsporidia that included, respectively, 93 single-copy protein domains (SCPD)  18  , 53 proteins (BMC)  19  and 259 proteins (GBE)  15. We updated these datasets with sequences from the Paraphelidium tribonemae transcriptome, thirteen derived Microsporidia as well as Mitosporidium daphniae  20  , Paramicrosporidium saccamoebae  21  and Amphiamblys sp  15.
Some stramenopile taxa such Ectocarpus siliculosus 62 , Thalassiosira pseudonana  63  or Phytophthora infestans (GenBank NW_003303758. 1) were also included to further prevent any previously undetected Tribonema gayanum contamination. Markers were aligned with MAFFT v7  64  , using the method L-INS-i with 1000 iterations. Unambiguously aligned regions were trimmed with TrimAl 65 with the automated1 algorithm, then approximate ML trees were inferred using FastTree 66 , all visually inspected in Geneious  67  to check for possible contamination. Contamination-free alignments were relieved from distant outgroup taxa. For in-depth analyses, we used opisthokont, mainly holomycotan, sequences, including apusomonad and breviate sequences as outgroup. We then made phylogenomic analyses including or excluding thirteen fast-evolving microsporidian parasites (49 or 36 species).