dataesr/scientific-paragraphs-categorization huggingface dataset:
bigelow. org; SCGC). Based on 16S rRNA gene analysis, 5 Methanothermococcus SAGs were chosen for sequencing. Libraries were prepared using the Ovation Ultralow Library DR multiplex system (Nugen) and were sheared at 250bp using a Covaris S-series sonicator. All SAGs were sequenced with NextSeq 500 (Illumina) at the W. M. Keck Facility in the Josephine Bay Paul Center at the Marine Biological Library. Each SAG was sequenced on a different plate to reduce the possibility of cross-contamination through "index-switching" or "sample bleeding"  (Mitra et al. , 2015; Sinha et al. , 2017). Reads were trimmed and merged using the illumina-utils software package  (Eren et al. , 2013). To maximize contig length while minimizing the risk of chimeras, reads were assembled using 4 different software packages: IDBA-UD v.
Pangenomic analysis of single cell genomes A phylogenetic tree of all SAGs was constructed using RaxML  (Stamatakis, 2014)  with 100 bootstraps, using the PROTGAMMAAUTO flag to allow the program to determine the best protein model of evolution. The alignment was created from universal single copy marker genes that were identified and aligned using Phylosift  (Darling et al. , 2014)  Open reading frames were identified and annotated for all SAGs using the JGI IMG/ER pipeline  (Markowitz et al. , 2012). Visualizations of ORFs on contigs and cross-links between ORFs were created using the Biopython collection  (Cock et al. , 2009)  with the GenomeDiagram module. Clustering of ORFs was conducted using the Integrated Toolkit for the Exploration of microbial Pangenomes (ITEP)  (Benedict et al. , 2014).
We clustered the ORFs using an inflation value of 2 and a maxbit score of 0. 3. ITEP was also used to compare ORF presence/absence across SAGs. KEGG Decoder  (Graham et al. , 2018)  was used to determine pathway completeness for selected metabolic pathways, and the heatmap was visualized with the Python Seaborn library  (Waskom et al. , 2014). PAML v4. 9  (Yang, 2007)  was used to calculate dN/dS values for each cluster with greater than one gene assignment. We compiled the results of PAML analysis and the presence/absence table using a custom Python (v. 2. 7. 5) script available on GitHub at https:// github. com/carleton-spacehogs/SAGs. Reads from each SAG were mapped to the contigs of each of the other SAGs using bowtie2 v. 2. 2. 9 (Langmead and Salzberg, 2012) using default settings. Coverage was calculated using samtools v1.
3. 1  (Li et al. , 2009) , and bedtools v2. 26. 0  (Quinlan, 2014). Average nucleotide identity was calculated using the software package pyani v. 0. 2. 7  (Pritchard et al. , 2016). To extract DNA from Sterivex filters for sequencing, we followed methods described in  Reveillaud et al. (2016)  and  Galambos et al. (2019). We extracted total genomic DNA from half of the Sterivex filter, and we extracted RNA from the other half of the Sterivex filter using the mirVana miRNA isolation kit (Ambion), adding a bead-beating step using beads from the RNA PowerSoil kit (MoBio) as described in  Fortunato and Huber (2016). RNA was treated with DNAse using the Turbo-DNase kit (Ambion), and then purified and concentrated using the RNAeasy MinElute kit (Qiagen). We prepared metatranscriptomic libraries using the Ovation Complete Prokaryotic RNA-Seq DR multiplex system (Nugen) following the manufacturer's instructions.
We prepared the metagenomic libraries as described in  Reveillaud et al. (2016). All libraries were sheared at 175bp using a Covaris S-series sonicator, yielding paired-end reads with a 30bp overlap. We sequenced all libraries on an Illumina Hi Seq 1000 at the W. M. Keck Facility in the Josephine Bay Paul Center at the Marine Biological Laboratory. We merged and filtered metagenomic and metatranscriptomic reads using the illumina-utils package  (Eren et al. , 2013)  using iu-merge-pairs with the -enforce-Q30-check flag, followed by iu-filtermerged-reads with a maximum mismatch of 2 in the overlap region. This resulted in reads averaging approximately 170bp in length. We removed rRNA in silico from metatranscriptomes by mapping all merged, filtered reads to the Silva LSU and SSU Parc databases (release 111)  (Pruesse et al.
Metagenome and metatranscriptome mapping We mapped the metagenomic and metatranscriptomic reads (Supplementary Table  1 ) of each sample to the contigs of each SAG (Table  1 , Supplementary Table ) using bowtie2 v2. 2. 9  (Langmead and Salzberg, 2012)  with default settings. We used anvi'o v6. 1  (Eren et al. , 2015)  to identify, quantify and characterize read coverage, single nucleotide variants (SNVs) and single amino acid variants (SAAVs) for all of the metagenomic reads mapping to each of the SAGs. SNVs and SAAVs were only counted if they had a minimum coverage of 10, and SAAVs were only counted if the read matching the variant mapped to the entire codon context. Anvi'o was also used to calculate average mapping coverages of metagenomes and metatranscriptomes for each of the SAGs. A custom Python database of ORFs, contigs, SNVs, SAAVs, and clusters was compiled for cross-referencing annotation, variability, and homologous clustering data.
From this database, SNV densities were calculated based on the length of annotated ORFs, and underlying distributions of SNV density for each sample were estimated using the Python Seaborn library  (Waskom et al. , 2014). Plots for SNV density vs. n2n1 (or the ratio of the second most frequent nucleotide to the consensus nucleotide), coverage, bubble plots, and average abundance were generated using lookups in this database on a sample-or genome-specific basis. SNV densities were matched to genes annotated by the JGI-IMG annotation pipeline  (Markowitz et al. , 2012).
Data availability This Whole Genome Shotgun project has been deposited at DDBJ/ENA/BenBank under accessions All assembled SAG contigs were deposited at NCBI under accessions JAHEQB000000000 (C09), JAHEQA000000000 (E23), JAHEPZ000000000 (K20), JAHEPY000000000 (M21), and JAHEPX000000000 (N22). The versions described in this paper are versions JAHEQB010000000 (C09), JAHEQA010000000 (E23), JAHEPZ010000000 (K20), JAHEPY010000000 (M21), and JAHEPX010000000 (N22). Reads for each SAG were deposited at ENA under accession numbers ERS6485300, ERS6485304, ERS6485303, ERS6485302, and ERS648530. Metagenomic and metatranscriptomic reads are deposited under study accession code PRJEB15541 in the EMBL-EBI European Nucleotide Archive (ENA) database. All code used for this project has been deposited to GitHub at https://github.
Analysis of gene presence and absence across Methanothermococcus SAGs To determine whether variation in gene content drove the observed differences in gene and transcript abundance, we examined variable gene content across each of the SAGs. To search for shared genes across all SAGs based on sequence similarity, we clustered all ORFs based on all-v-all BLAST hit scores and compared the presence and absence of gene clusters across each SAG. It is important to note that these SAGs varied in completeness, and thus, absence of evidence for specific genes does not constitute evidence of absence. Therefore, very few genes were considered "core" (that is, found in all five SAGs). Rather than focusing on individual gene presence/absence trends, we instead searched for consistent patterns in gene presence/absence that shared similar functions across the five SAGs.
We focused on groups of functional genes whose presence/absence patterns match the phylogenetic relationships shown in Figure  1. Homologous clustering of the open reading frames (ORFs) from all five SAGs produced 1,838 clusters of homologous ORFs. ORFs with identical functional annotations and identical presence/absence patterns were tabulated and summarized (Supplementary Data File 1). Most variable genes were annotated as hypothetical proteins. The SAGs contained several ORFs that encoded functional genes related to the metabolism of hydrogen, sulfur, and nitrogen (Supplementary Table  3 ), many of which showed consistent presence/absence patterns. All five SAGs contained genes for iron transport proteins, energy-converting hydrogenases, fumarate reductase, and methyl coenzyme M. Analysis of metabolic pathway completeness revealed that all five SAGs encoded complete or nearly complete pathways for methanogenesis via CO2 and acetate, but lacked the genes required for methanogenesis via dimethylsulfide, methanethiol, or methylamines (Figure  3 ).
Acknowledgements We would like to thank  Victor Huerta  and  Jackson Raynor  for assistance with bioinformatics analysis, and  Chip Brier ,  Chris German ,  Jeff Seewald ,  Cindy Lee Van Dover ,  Jill McDermott , and  Emily Reddington  for assistance with sample collection, processing, and analysis. We would also like to thank the captains and crew of  R/V Atlantis  and  R/V Falkor  as well as  ROVs Jason  and  Nereus  for assistance with sample collection and preparation. We thank the  SCGC  staff for single cell genomics analyses.
The reference sequences were obtained from NCBI, with the following accession numbers: Methanococcus maripaludis C7 [NCBI:txid426368] (designated as the outgroup), Methanothermococcus thermolithotrophicus DSM 2095 [NCBI:txid523845], and Methanothermococcus okinawensis IH1 uid51535 [NCBI:txid647113].
Results Identification of an UNC5B isoform generated by AS in the endothelium. For the majority of protein isoforms generated through AS in ECs, functional role remains obscure  35. AS is particularly frequent in cell-surface molecules and receptors, thus potentially affecting interactome networks as well as downstream signaling pathways  36. Given its importance in EC biology  7, 8  , we investigated UNC5B AS events in diverse vertebrate cell and tissue types, or developmental stages, by using the Vertebrate Alternative Splicing and Transcription Database (VastDB)  37. We found a single AS event conserved in human (HsaEX0069379), mouse (MmuEX0050632), and chicken (GgaEX0006169). This event affects UNC5B exon 8, whose exclusion from the mature mRNA generates a shorter transcript herein named UNC5B-Δ8.
FL Δ8 67. 9 99. 9 % the YCAY cluster in Unc5b intron 7 represented a bona fide NOVA2 binding site, we performed UV crosslinking and immunoprecipitation (CLIP), which detects direct protein-RNA interactions in living cells  43. RNA of UV crosslinked ECs was immunoprecipitated using anti-NOVA2 or control antibodies. RNAs bound by NOVA2 were then analyzed by RT-qPCR with primers spanning either the YCAY cluster in Unc5b intron 7 (primers A) or intron 8 (primers B and C), used as controls Fig. 1  Inclusion of UNC5B exon 8 is regulated in ECs. a Differentially spliced exonic events in NOVA2-depleted moEC compared to control moEC (NCBI BioProject: PRJNA293346). Absolute difference of Percent Spliced-In (PSI) between means is shown. Unc5b exon 8 AS event is indicated in red. b Schematic representation of the mouse and human UNC5B genomic region with the AS exon 8 of 33 nt (gray).
13a-b ). According to EC-restricted NOVA2 expression in cancer specimens, we found that UNC5B-Δ8 was present only in the tumor endothelium (Fig. 5a  and Supplementary Fig. 13c ). Using the UALCAN web-tool (http://ualcan. path. uab. edu)  59 to compare cancer and nonpathological samples from The Cancer Genome Atlas (TCGA)  60  , we found that NOVA2 was upregulated in colon cancer tissues versus not pathological counterparts (Fig. 5b ). NOVA2 upregulation was also confirmed in colon cancer microarray datasets from Oncomine (http://oncomine. org/ resource)  61  (Supplementary Fig. 14a-b ). RNA-seq TCGA dataset also showed a significant decrease of UNC5B exon 8 inclusion in colorectal cancers compared to the normal tissues (Fig. 5c ). Accordingly, a positive correlation (r = 0. 406; P < 0. 0001) between UNC5B-Δ8 and NOVA2 expression levels was present in the TCGA-Colon Adenocarcinoma (TCGA-COAD) dataset (Fig.
5d ). We validated these findings in a small cohort of colon cancer patients by RT-PCR and RT-qPCR analysis by using RNA extracted from: (i) colon cancer specimens, (ii) normal adjacent colon tissue, and (iii) liver metastasis (Supplementary Fig. 14c-e ). A similar correlation was also found in liver hepatocarcinoma (LIHC-TCGA) and head and neck tumors (HNSC-TCGA) (Supplementary Fig. 12c-d ), where our IHC analyses demonstrated EC-restricted NOVA2 expression. Expression levels of both NOVA2 and UNC5B-Δ8 were also positively correlated with the expression of a colon tumor-specific angiogenetic signature  62  (r = 0. 729 and r = 0. 430; P < 0. 0001) (Fig. 5e  and f ). Clinical data annotated in the TCGA dataset (https://www. cbioportal. org)  63 and the Human Cancer Metastasis Database (HCMDB) (https://hcmdb.
Tube-like structure formation of UNC5B-Δ8-depleted HUVEC/TERT2 was evaluated in 96-well plate coated with 50 μl/well of Matrigel in complete medium at least 24 h after the last siRNA transfection or 36 h after morpholino treatment. Cell imaging. Transiently transfected HeLa cells were fixed with 4% paraformaldehyde (PFA; Sigma-Aldrich). Nuclei were stained with 0. 1 g/ml DAPI (Sigma-Aldrich). The same protocol was also used for transduced ECs (moEC and HUVEC), which were seeded in 35 mm Petri dishes coated with 0. 1% Gelatin. For imaging, an epifluorescence microscope (Optical Microscope Olympus IX71) equipped with ×4 or ×60 objectives was used. Photomicrographs were taken with a digital camera Cool SNAPES (Photometrics). Data acquisition was done using the MetaMorph 7. 7. 5 software (Universal Imaging Corporation).
Images were exported to Photoshop (Adobe). No manipulations were performed other than adjustments in brightness and contrast. High-resolution pictures were acquired using a Leica SP5 confocal microscope with a Leica spectral detection system (Leica 15 SP detector) and the Leica application suite advanced fluorescence software or Zeiss LSM800 confocal microscope and Zeiss Zen 2. 3 Software. RNA extraction, RT-PCR, and RT-qPCR. Total RNA was isolated using the RNeasy Mini Kit (QIAGEN, #74106) according to the manufacturer's instructions. Total RNA was extracted from matrigel-based tube-like structures by using the ReliaPrep TM RNA Miniprep System (Promega, #Z6011) according to the manufacturer's instructions (#AN296). Total RNA from wild-type AB, Tg(fli1a:GFP) y1 , nova2 morpholino-mediated knockdown Tg(fli1a:GFP) y1 embryos, and Tg(kdrl: GFP) la116;nova2io011 mutants were extracted with TRIzol reagent (Invitrogen.
5-1. 5 μg of purified RNA was retro-transcribed with a mix of oligos d(T) 18 and random hexamers by using SuperScript IV First-strand System (Invitrogen, #18091050). Resulting cDNA (1/20 v/v) was then PCR amplified with the GoTaq G2 Flexi DNA Polymerase (Promega, #M7805) according to the manufacturer's instructions. For RT-qPCR, an aliquot of the RT reaction was analyzed with QuantiTect SYBR Green PCR (QIAGEN, #204145) by using LyghtCycler 480 (Roche). Target transcript levels were normalized to those of the indicated reference genes. The expression of each gene was measured in at least three independent experiments. All PCR products were sequenced and bands intensity was quantified with NIH Image J software (version 1. 48 v). The percentage of exon inclusion was calculated as the ratio between the intensity of the band with the alternative exon included and the intensity of all bands.
Acknowledgements We are grateful to  Dr. Chiara Mondello  for providing mouse genomic DNA,  Dr. Anna Garbelli  for assistance with confocal microscope analyses,  Dr. Jon D. Moulton  for i7_UNC5B morpholino design, and  Dr. Silvia Faravelli  for support with HEK293-F cell cultures and transfections. We thank  Dr. Davide Gabellini  for the helpful discussion, and  Dr. Giuseppe Biamonti ,  Dr. Alessandra Boletta , and  Dr. Rosa Bernardi  for the critical reading of the manuscript. We are also grateful to  Prof. Elisabetta Dejana  for help in performing experiments with ECs and critically reviewing the manuscript. We also thanks  Dr. Giorgio Scita  for pEN_Tmcs vector and  Prof. Elena Seiradake  for the pHLSec-UNC5B-FL vector. We acknowledge  ARDIS Srl  for provision of the  Surface Plasmon Resonance instrumentation  and the " Fondazione Adriano Buzzati-Traverso"  for the support.
Part of the results included in this work comes from data generated by the  TCGA Research Network  (https://www. cancer. gov/tc). This research was funded by  Associazione Italiana per la Ricerca sul Cancro  to C. G. (AIRC, projects  IG-17395  and  IG-21966 ). D. P. was supported by a  AIRC fellowship for Italy. Research in F. F. lab was supported by the  Armenise-Harvard Foundation  ( CDA 2013 ), by  AIRC  ( MFAG 20075 ), and by the  Italian Ministry of Education, University and Research (MIUR) :  Dipartimenti di Eccellenza Program  ( 2018-2022 , to the Dept. of  Biology and Biotechnology "L. Spallanzani", University of Pavia ). A. C. was supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Curie  grant agreement COTETHERS -n. 745934. This research was also funded by  INCA  (P.
PSI UNC5B ex. 8 Fig. 5 85 Fig. 5NOVA2 and UNC5B-Δ8 expression levels in colon cancer patients. a IHC of NOVA2 (I and III, scale bar: 60 μm) and ISH with the UNC5B-Δ8 probe (II and IV, scale bar: 30 μm) in colon primary tumors and normal adjacent tissues. Black arrowheads indicate UNC5B-Δ8 signal in ECs in tumor blood vessels. b RNA-seq data analysis of NOVA2 mRNA expression levels, c PSI (Percent Spliced-In) of UNC5B exon 8, and d correlation between NOVA2 and UNC5B-Δ8 expression in normal (blue) and colon adenocarcinoma (red) samples in the TCGA-COAD dataset. e NOVA2 and f UNC5B-Δ8 correlation with a colon tumor-specific angiogenetic signature (TEC). g NOVA2 mRNA Z-score expression levels in colon adenocarcinoma without (N0) or with metastasis (N1 or N2) annotated in the TCGA-COAD dataset.
h NOVA2 mRNA expression levels in colon adenocarcinoma without or with metastasis (GSE14095 and GSE87211 datasets; http://hcmdb. i-sanger. com). i Kaplan-Meier plot of 5-years overall survival in colon adenocarcinoma patients classified according to NOVA2 or l UNC5B-Δ8 mRNA expression. Blue curve: high expression (1st quartile); red curve: moderate expression; green curve: low expression (3rd quartile). Log-ranked P-values are indicated. Boxplot represents upper quartile, median, lower quartile. Error bars indicate minimum and maximum. Twotailed Log-ranked test (in b) or Two-tailed Student's t-test (in c and g) and exact P values are indicated: *P < 0. 05, ****P < 0. 0001. P values in h, were calculated by HCMDB platform. For correlation analysis, linear regression (black line) and Pearson r coefficient with two-tailed P-value were also calculated.
The available tracheid analysis programs generally fail to provide multiple sectors for micro-rings and they are unable to compute most of the useful dendroanatomical parameters at fine temporal resolutions. Here, we present a program (SabaTracheid) that addresses the three main standard tasks that are necessary for QWA and dendroanatomy before running a climate analysis: (1) tracheidogram standardization, (2) sectoring, and (3) computing QWA and dendroanatomical variables. SabaTracheid is demonstrated on African Juniper (Juniperus procera Hochst. ex Endl), but it is potentially able to provide fine-resolution QWA and dendroanatomic data that could be used for dendroanatomical studies in all regions of the world. SabaTracheid is a freeware that quickly and accurately standardizes tracheidograms, divides tree rings into multiple regular sectors, computes useful dendroanatomic and QWA variables for the whole tree rings, early-and latewood portions, and each sector separately.
, 2006). However, Mork's index shows very few or no latewood cells in many conifers, especially the tropical ones, though the borders could visually be identified through finding the points where observed gradual decline in lumen diameter (LD) and increase in double wall thickness (WT) in the latewood of tree rings occurred. Following the advancement in microscopic slide preparation  (Gärtner and Schweingruber, 2014; von Arx et al. , 2016) , accompanied with high resolution imaging and measurement techniques, and softwares [e. g. , WinCELL  (Larocque and Smith, 2005) , ImageJ  (Schuldt et al. , 2013), and ROXAS (von Arx and Carrer, 2014) ], dendroanatomy and QWA has improved remarkably. Recent dendroanatomy and QWA programs  (Campelo et al. , 2016; von Arx et al. , 2016; Ziaco, 2020)  have contributed to the realization of successful fineresolution tracheid chronologies and dendroclimatic studies  (Carrer et al.
, 2016 (Carrer et al. , , 2018;; Castagneri et al. , 2018; Belokopytova et al. , 2019; Lange et al. , 2020). However, most available programs are limited to tracheid measurement  (Dyachuk et al. , 2020)  and standardization  (Campelo et al. , 2016; Ziaco, 2020)  only. This solely is not enough towards developing QWA and dendroanatomy since programs that further analyze the measured and standardized series and generate sectoral values are needed. The program presented here, SabaTracheid, has a set of functions for fast, simple, and accurate computation of dendroanatomical and QWA parameters throughout whole tree rings, and over different portions of tree rings (Figure  3 ). Once the transition from earlywood to latewood is visually identified and marked, data imported into SabaTracheid and processed can serve to generate an observation based speciesspecific lumen diameter to double wall thickness ratio (LD/WT) threshold.
Presentation of the Training Dataset SabaTracheid's functions were demonstrated using tracheid LD and WT measurement data from 30 tree rings  (AD 1901 (AD -1930) )  of an absolutely dated core sample of Juniperus procera from Gonder Qusquam church, Ethiopia, that was confirmed by radiocarbon dating  (Gebregeorgis et al. , 2020). Firstly, measured values of tracheid LD and WT (Figure  1 ) for five to eight radial rows per tree ring were entered into Excel spreadsheets named after each tree ring (e. g. , 1905; Figure  2 ).
Here, we should note that the program will only identify one such straight column per sheet to be the border between the EW and LW of each radial file. The second option is that the input file (Supplementary Figure  1  and Supplementary File 3) is prepared without manual marking as the program has a check box option "Mork's latewood" which enables us to identify the boundary using Mork's index  (Denne, 1988). According to Mork's definition, a latewood cell is one that the value of twice the tracheid's double cell wall thickness divided by cell lumen exceeds 1  (Denne, 1988). Then, the rest of the computation will remain the same as that for the first input type. It should be noted that preparation of the input files could be time-consuming unless done with R codes. The Excel workbooks (files), comprising sheets containing each tree ring's tracheid values of five to eight rows (Figure  2  and Supplementary File 1), were then imported into SabaTracheid.
, 2016). However, no similar study exists for tropical conifers. The fact that SabaTracheid is a Java program makes it compatible with most computer operating systems and easy to use. It is freely downloadable at 10. 6084/m9. figshare. 12612128 and requires no specialized installation procedure. Moreover, it provides update notifications. The input file from direct or automated measurement should be carefully prepared, as it determines the next steps in analysis of tracheid data. Importantly, even with incorrect input data file preparation, the error notification in SabaTracheid guides the user to find possible errors in particular worksheet cells. The fact that the input and output files are Excel files eases the task of exporting and further analysis of results. We believe that SabaTracheid may be useful for intra-annual dendroclimatology and dendroanatomical studies of any region as it uses a combination of standardization techniques proven (to give reliable results) on samples characterized by frequent occurrence of micro-rings, wedging rings and missing rings.