dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank all of the patients and families who contributed to the study,  Ina Felau  for project management, and  Jill Dolinsky ,  MS, CGC at Ambry Genetics, Inc  for sharing mutation classification and frequency data. This study was supported by  NIH  grants  U54 HG003273 ,  U54 HG003067 ,  U54 HG003079 ,  U24 CA143799 ,  U24 CA143835 ,  U24 CA143840 ,  U24 CA143843 ,  U24 CA143845 ,  U24 CA143848 ,  U24 CA143858 ,  U24 CA143866 ,  U24 CA143867 ,  U24 CA143882 ,  U24 CA143883 ,  U24 CA144025  and  P30 CA016672. Sylvia L. Asa  is part of the pathology imaging medical advisory board of  Leica Biosystems. Andrew D. Cherniack  and  Matthew Meyerson  received research support from  Bayer AG.
The most studied phages performing GT are Escherichia coli phage P1  [8]  and Salmonella enterica phage P22  [9]. Phage F116, also capable of GT, was isolated from a Pseudomonas aeruginosa clinical strain  [10]. It was reported to maintain its DNA as a plasmid  [11]  and it was not clear whether it could integrate into the bacterial genome  [12]. F116 binds to the type IV pili of its host  [13]  and can digest alginate  [14]. Upon sequencing of its genome (65,195bp, genome accession number AY625898), it was shown that 2. 6% of the packaged DNA was of bacterial origin, predominantly derived from a particular region of the chromosome  [15]. Two F116-like P. aeruginosa temperate phages have been sequenced, H66 (65,270bp, genome accession number KC262634) and LKA5 (64,746bp, genome accession number KC900378).
Nucleotide sequence accession numbers The DNA sequence of phage phiC725A has been deposited in the EMBL-EBI database under accession number LT603684 within project PRJEB14922. Sequencing reads from the phage induction and infection experiments as well as sequencing reads from the PAO1 Or lysogen for phiC725A were also deposited in project PRJEB14922 and are available from the European Nucleotide Archives (ENA) browser at http://www. ebi. ac. uk/ena/data/view/PRJEB14922.
The phiC725A phage genome PhiC725A was amplified on PAO1 Or , then phage particles were purified and treated with DNAse I before extracting the DNA for sequencing. To be able to characterize the phage genome but also the bacterial DNA packaged within the phage head, deep sequencing of the phage genome was performed so that an average coverage of almost 15,000X, was achieved. A total of 1,284,508 reads were obtained, of which 0. 6% (7,844) mapped to PAO1 Or. The phage reads were assembled resulting in a single 65,149bp long molecule with a 63. 5% GC content. BLAST analysis on Genbank (release 214) identified F116 (genome accession number AY625898. 1) as the closest phage genome with a 86% mean similarity over the whole sequence. PhiC725A showed 84% and 76. 5% similarity with phage LK5 (KC900378.
F116 has been described as a non-integrative phage  [11] , but it possesses a putative integrase, identical to that of phiC725A (F116p70) and the TGGTGCGGACGGAGAGA attP site, suggesting that this phage could in theory lysogenize strain PAO1. The 17 bp attB site sequence identified here was present in 93 out of the 95 P. aeruginosa complete genome sequences which could be queried in Genbank (the site was absent in strains ATCC 27853 and Cu1510). When the full dataset of phage reads was mapped back on the phiC725A sequence (Fig 2B ), no high peak of reads characteristic of fixed genome ends was observed  [22]. This is as expected if the phage genome is terminally redundant (TR) and circularly permuted (CP), as previously demonstrated for F116  [30]. However, we noticed a maximum in the coverage starting at position 23,500 (near the small and large terminase subunit genes) and decreasing over the whole genome (Fig  2B ).
To check whether the phage was inserted into the bacterial genome, we performed a PCR reaction for attP/B with primer pair C725-Ins containing one primer flanking the att site on one side in the bacterial genome and one primer on the other side inside the phage genome. A signal was obtained with the C7-25 strain used as positive control and with both PAO1 phiC725A and PcyII-10 phiC725A indicating that the prophage integrated at the same attB site (data not shown). The (control) parent strains PAO1 Or and PcyII-10 were negative. A PCR reaction with phage primers localized on both sides of attP, aiming at detecting non-integrated genomes (primer pair Phi725-Reg2) was also positive with all three lysogens, indicating that free viral genomes were present in the cells (data not shown).
This study was supported by the  french "direction generale de l'armement" (DGA)  through  Agence Nationale de la Recherche  ( ANR, France)  " Resisphage "  ANR-13-ASTRID-0011-01  (http:// www. agence-nationale-recherche. fr/), and a joint  DGA-IDEX UniversiteParis-Saclay doctoral fellowship  to  Dr. Libera Latino. The Transposon Mutant Collection holds grant # NIH P30DK089507. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Relevant data are within the paper and its supporting information. The DNA sequence of phage phiC725A has been deposited in the EMBL-EBI database under accession number LT603684 within project PRJEB14922. Sequencing reads from the phage induction and infection experiments as well as sequencing reads from the PAO1Or lysogen for phiC725A were http://www. ebi. ac. uk/ena/data/view/PRJEB14922.
Instrument description This study utilizes data from two dual-O 2 Navis floats (Sea-Bird Inc. , Bellevue, USA) with WMO ID 6900889 and 6900890 that were deployed in the eastern tropical North Atlantic in the oxygen minimum zone in November 2014 and September 2013, respectively. Each float is equipped with two O 2 optodes, an unpumped Aanderaa 4330 optode (AADI, Bergen, Norway) and a pumped SBE63 optode (Sea-Bird Inc. , Bellevue, USA). Aanderaa 4330 optodes have been pre-aged at the factory and were equipped with the standard foil with optical isolation. For each float, the optodes were laboratory-calibrated in the same calibration run (following  Bittig et al. , 2012)  prior to deployment. Only the unpumped Aanderaa 4330 optode, cablemounted in an elevated position on the float's top cap, is capable of in-air measurements, and the float's firmware was adjusted accordingly (see  Bittig and Körtzinger, 2015, for details).
3g, h ) and feasible for both optode models, yielding highly consistent results (Fig. 3f ). This is, however, only possible if the respective measurement times are known, i. e. , provided by the manufacturer through the instrument's firmware as well as transmitted or stored in the data stream. (a) (b) (c) (d) (e) (f) (g) (h) Interestingly, a similar analysis of the limited dataset of the third float, WMO ID 6900891, yields apparent boundary layer thicknesses l L for the 4330 optode that are 100 µm or more higher. We believe this is caused by growth of a biofilm on the sensing foil during its 5-week irregular near-surface drift, which artificially delays the time response. The SBE63 optode, being contained in the CTD's pumped path and antifouling regime, is probably better protected against such artifacts.
Optode stability Based on regular in-air measurements, the Aanderaa 4330 optodes show a significant drift of -0. 40 and -0. 27 % yr -1 for floats 6900889 and 6900890, respectively. This is in a Ocean Sci. , 13, 1-11, 2017 www. ocean-sci. net/13/1/2017/ (green) and 6900890 (black). There seems to be a conditioning effect on either one or both optodes during the first half-year / ≈ 40 profiles, after which differences between optodes are stable. similar range for in situ drift as reported by  Bushinsky et al. (2016)  for 10-12 out of 14 optodes. In contrast,  Johnson et al. (2015)  observed both significant positive and negative drifts for individual floats, but seen over a fleet of 29 floats, this drift was indistinguishable from zero. However, they did not correct explicitly for the carry-over effect but, given that O 2 is typically less than 3 % supersaturated, assumed it to be negligible as long as the data record spans at least a full year.
Data availability The float data are made freely available by the International Argo Program  (Argo, 2000)  and the national programs that contribute to it (http://www. argo. ucsd. edu,http:// argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www. esrl. noaa. gov/psd/, ERA-Interim data provided courtesy of ECMWF, MERRA2 data files acquired from the Goddard Earth Science Data Information Services Center (GES DISC), and JRA-55 data provided from the Japanese 55-year Reanalysis (JRA-55) project carried out by the Japan Meteorological Agency (JMA). Ocean Sci. , 13, 1-11, 2017 www. ocean-sci. net/13/1/2017/ H. C. Bittig and A. Körtzinger: Update on response times, in-air measurements, and in situ drift The Supplement related to this article is available online at doi:10.
Acknowledgement. The authors want to thank  Patrice Brehmer  ( IRD, Dakar, Senegal ) as well as the captain and crew of  M/V Samba Laobé Fall cruise AWARGO  for help with the deployment of 6900890,  Tobias Hahn  ( GEOMAR, Kiel, Germany) , as well as the captain, crew, and scientists of  R/V Polarstern  cruise  PS88. 2  for the deployment of 6900889, and  Sea-Bird Electronics  for their efforts with float and firmware customization. Financial support by the following projects is gratefully acknowledged:  AtlantOS (EU Horizon 2020  grant agreement  2014-633211 ), remOcean ( EU ERC  grant agreement  246777 ), and  E-AIMS  ( EU FP7  project  312642 ), as well as  O 2 -Floats  ( KO 1717/3-1 ) and the SFB754 of the  German Science Foundation (DFG). Edited by: O. Zielinski Reviewed by: three anonymous referees.
Multiple mobile elements related to phages were detected in the C. difficile LEM1 genome and may play a role in the pathogenesis of this strain. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession no. CP019469 for C. difficile LEM1 and MUJV00000000 for C. difficile VPI 10463. The versions described in this paper are versions CP019469. 1 and MUJV01000000 (which consists of sequences MUJV01000001 to MUJV01000002) for LEM1 and VPI 10463, respectively.
ACKNOWLEDGMENTS We are grateful for the excellent help from the sequencing core at  PennCHOP Microbiome Center (University of Pennsylvania, Philadelphia, PA). This work was supported by  NIH  grants  DK099071  and  DK083890  to A. T. G. , and  DK087763 ,  AI109526 , and  AI116933  to S. M. M. B. C. is a recipient of the Career Development award from the  Crohn's and Colitis Foundation of America (CCFA). The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
In contrast, the HB group was enriched for signatures of carbohydrate transport and metabolism  (Fig 1C) , suggesting HBs differ from HCCs in sugar uptake and usage. For this reason, we determined whether the differences between the two groups of liver cancer cells (HCC and HB) could be associated with different utilization of genes involved in glucose metabolism. We found a strong enrichment for glucose transporter expression in HB cells (Fig  1D ). Among them, GLUT3 was robustly expressed in HBs compared to HCCs, with Huh-6 and Hep-U2 exhibiting the highest levels  (Figs 1D and 2A, and Dataset EV1). We recently reported that SLC2A3 encoding for GLUT3 is induced by ZEB1 during epithelial-mesenchymal transition (EMT) in tumor cells from non-small cell lung cancer (NSCLC) and HCC  (Masin et al, 2014).
In contrast, genes promoting gluconeogenesis, PPARGC1A (PGC-1a), an important liver gluconeogenesis transcriptional co-activator  (Herzig et al, 2001; Yoon et al, 2001) , AQP9 (aquaporin-9), GK (glycerol kinase), and G6PC (glucose-6-phosphatase), were all less abundant in Huh-6 and Hep-U2  (Fig 3A, Appendix Fig S1A, Datasets EV1 and EV2) , supporting the increased glycolysis of these cells compared to other HB cells. As notable exception, HepG2 and Hep293TT cells expressed HK2 to a significantly higher level than Huh-6 and Hep-U2. This is interesting, as HK2 is often overexpressed in adult solid tumors  (Patra et al, 2013; Guo et al, 2015) , which is compatible with a more differentiated state of those cells, while HK1 expression is strong specifically in the developing mouse liver  (Appendix Fig S1B).
Finally, we analyzed the top 200 differentially expressed genes between the two cell clusters using Gene Ontology (http://geneontology. org/). This revealed biological processes of development, morphogenesis, and embryogenesis from the genes up in Huh-6 and Hep-U2, compatible with a block in early development or undifferentiated stage. In contrast, the same analysis highlighted metabolic processes from the genes up in HepG2 and Hep293TT, reminiscent of the physiological function of differentiated hepatocytes (Datasets EV3 and EV4). Hence, Hep-U2 and Huh-6 classify as poorly differentiated embryonal-like HB, whereas HepG2 and Hep293TT represent more differentiated, fetal-like HB. We then explored the impact of GLUT3 in the different cell lines with GLUT3 siRNA, which strongly decreased its expression (~80% decrease)  (Fig 3D).
Long b-catenin deletion mutants do not interact with a-catenin Because of the correlation between b-catenin mutation and GLUT3 binding, we investigated implications of the different CTNNB1 mutations in fetal and embryonal HB cell lines. We found that genes associated with the canonical Wnt pathway are more expressed in the embryonal-like than the fetal-like HB cell lines (Dataset EV3). We reasoned that the extent of CTNNB1 deletion could cause a loss of interaction with specific co-activators of Wnt-target genes. We first evaluated whether in fetal-like HB cell lines, the partial deletion of CTNNB1 exon 4 could alter the capability of b-catenin to interact with BCL9 and BCL9L, which are established co-activators of the Wnt pathway important to specify a stem cell-like phenotype in cancer cells  (Brembeck et al, 2004; Deka et al, 2010).
LDHB and G6PC stain different human HB components In an effort to validate our metabolic subtypes in vivo and in order to identify new diagnostic biomarkers in addition to GLUT3, we characterized the expression of LDHB and G6PC, whose coding genes were differentially expressed in embryonal compared to fetallike HB cell lines (see  Fig 3A, Datasets EV1  and EV2 ). In non-tumor liver, LDHB was moderately expressed in the cytoplasm and nucleus of interlobular bile duct cells, but never in hepatocytes. As expected, G6PC showed moderate cytoplasmic staining of the non-tumor hepatocytes while cholangiocytes remained negative. In the fetal component, tumor cells showed a diffuse reactivity to G6PC, with the exception of two tumors characterized by focal staining  (Fig 7A, B and J). Reactivity ranged from mild (1+) to strong (3+), and the pattern of staining was mainly cytoplasmic.
Seahorse analysis OCR and ECAR measurements were performed using the XF24 or XF96 Extracellular Flux analyzer (Seahorse Bioscience, North Billerica, MA). Briefly, cells were plated into XF96 (V3) polystyrene cell culture plates (Seahorse Bioscience, North Billerica). The cells were incubated for 24-28 h in a humidified 37°C incubator with 5% CO 2 (RMPI1640 medium). Prior to assay, cells were incubated in a 37°C/non-CO 2 incubator for 60 min. All experiments were performed at 37°C. Each measurement cycle consisted of a mixing time of 3 min and a data acquisition period of 2 min. Each measurement was taken in triplicates. For glycolysis assay, 10 mM glucose was added during seahorse experiment followed by 50 mM 2-DG after 24 h in RPMI medium without glucose complemented with 5% serum. Palmitate-BSA consumption was performed using the Seahorse XF Palmitate-BSA FAO Substrate (Agilent) using the manufacturer's instructions.
Acknowledgements We thank  Lorenzo Petrini ,  Caroline Contat ,  Silvia Sabatino ,  Hongbo Zhang , and the  EPFL SV Histology and Flow Cytometry Core Facilities  for technical assistance. We thank the  Lausanne Genomic Technologies Facility  for RNA sequencing. We thank the  Protein Modeling Facility of the University of Lausanne  for the support in  Molecular Modeling  and the  Vital-IT  group of the  SIB Swiss Institute of Bioinformatics  for providing computational resources. We thank  Jérôme Gouttenoire  and  Darius Moradpour  ( CHUV, Lausanne ) for critical reading of the manuscript and  Barbara Wildhaber  ( HUG, Geneva and CHUV, Lausanne ) for helpful discussions. This work was supported by the  Swiss National Science Foundation  ( PP00P3_133661  and  PP00P3_157527 ) and by  FORCE, a Foundation for Children Cancer Research.
We used the USEARCH  43  algorithm to cluster similar filtered sequences into Operational Taxonomic Units (OTUs) based on a 97% similarity threshold. We then identified and removed chimeric sequences using UCHIME  44. Since each OTU can comprise many related sequences, we picked a representative sequence from each one. Representative sequences were aligned using PyNAST against Greengenes template alignment (gg_13_8 release), and a taxonomical assignment step was performed using the basic local alignment search tool (BLAST) to map each representative sequence against a combined database encompassing the Greengenes and PATRIC databases. To correctly define species richness for the analysis of between-sample diversity, known as beta diversity, the OTU table was rarefied at 4849 sequences per sample.
The positive stained area was quantified using a morphometric analysis system. Briefly, twelve images were obtained with an optic microscope (Nikon Eclipse E600, Nikon Corporation, Japan) at magnification of x20. Images were imported using an image-analysis system (AnalySIS, Soft-Imaging System, Munster, Germany) software and automatically merged. The positive area was the sum of the area of all positive pixels. Statistical analysis. Statistical analysis was performed using the SPSS statistical package (SPSS Inc. version 17. 0, Chicago, Illinois, USA). All parameters are reported as percentages and mean ± SEM. Differences between groups were analyzed using the Fisher's exact test for qualitative variables. The Shapiro-Wilk test was used to check the normality of data distribution. The Student's t-test was used when data were normally distributed; otherwise, the non-parametric Mann-Whitney U-test was used.
Correlations were calculated using Pearson's test or Spearman's test. The probability of developing ascites was assessed by Kaplan-Meier test and then compared using the log rank test. A two-tailed P value below 0. 05 was considered statistically significant. Specifically regarding the microbiome evaluation, statistical analyses were carried out in QIIME and in R. To work with normalized data, we analyzed an equal number of sequences from all groups. The Kruskal-Wallis one-way test of variance was used to compare the mean number of sequences of the groups. The analysis provided false discovery rate (FDR)-corrected P-values. FDR < 0. 1 was considered significant for all tests. Sample size was calculated according to previous data from our group  15. Considering an α error of 0. 05, a β error of 0.
Acknowledgements The authors wish to thank  Cristina Millan  for technical support and  Carolyn Newey  for English language assistance. This study was partially funded by  Danone Research (Palaiseau, Cedex, France) , and grants  PI08/0262  and  PI14/00680  from the  Instituto de Salud Carlos III, Madrid, Spain , and cofinanced by  Fondos FEDER (Fondo Europeo de Desarrollo Regional) ,  "Una manera de hacer Europa", European Union , and  CERCA Programme, Generalitat de Catalunya. The investigational product was provided by  Danone Research. Silvia Vidal  was supported by  Fondo de Investigaciones Sanitarias (FIS)  and is a participant in the Program for  Stabilization of Investigators of the Direcció d'Estrategia i Coordinació del Departament de Salut, Generalitat de Catalunya. Pau Sancho-Bru  is funded by  Instituto de Salud Carlos III ,  Miguel Servet  ( CP11/00071 ).
Differential gene expression defines anaplastic meningioma subgroups with prognostic and biological significance We performed messenger RNA (mRNA) sequencing of 31 anaplastic meningioma samples from a total of 28 patients (26 primary tumors and 5 recurrences). Gene expression variability within the cohort did not correlate with clinical parameters including prior radiotherapy, anatomical location or clinical presentation (de novo versus progressive tumor) (Supplementary Figure  S6 ). However, multiple unsupervised hierarchical clustering methods and correlation measures consistently delineated two distinct clusters, hereafter referred to as C1 and C2 (Figure  2a-c ). The clinical trajectories of patients in these clusters markedly diverged. We retrospectively sought follow-up survival data from the time of first surgery, which was available for 25 of the 28 patients included in the transcriptome analysis (12 patients in C1, 13 in C2; mean follow-up of 1,403 days from surgery).
RNA sequencing and data processing For transcriptome sequencing, 350bp poly-A selected RNA libraries were prepared on the Agilent Bravo platform using the Stranded mRNA library prep kit from KAPA Biosystems. Processing steps were unchanged from those specified in the KAPA manual except for use of an in-house indexing set. Reads were mapped to the GRCh37 reference genome using STAR (v2. 5. 0c)  92. Mean sequence coverage was 128X. Read counts per gene, based on the union of all exons from all possible transcripts, were then extracted BAM files using HTseq (v0. 6. 1)  93. Transcripts Per kilobase per Million reads (TPM) were generated using an in-house python script (https://github. com/TravisCG/SI_scripts/blob/master/tpm. py)  92, 93. We downloaded archived RNA sequencing FASTQ files for 19 grade I meningioma samples representing the major mutational groups (NF2/chr22 loss, POLR2A, KLF4/TRAF7, PI3K mutant) (ArrayExpress: GSE85133)  8.
Reads were then processed using STAR and HTseq as described above. Cancer cell line (n=252) and triple-negative breast cancer (n = 100) RNA sequencing data was generated in-house by the aforementioned sequencing and bioinformatic pipeline. Expressed gene fusions were sought using an in-house pipeline incorporating three algorithms: TopHat-Fusion (v2. 1. 0), STAR-Fusion (v0. 1. 1) and deFuse (v0. 7. 0) (https://github. com/cancerit/cgpRna)  92, 94, 95. Fusions identified by one or two algorithms or also detected in the matched normal sample were flagged as likely artefacts. Fusions were further annotated according to whether they involved a kinase or known oncogene and whether they occurred near known fragile sites or rearrangement break points  96  (Supplementary Table  S5 ).
Differential gene expression and pathway enrichment analysis The DESeq2 R package was used for all differential gene expression analyses  97, 98. DESeq2 uses shrinkage estimation of dispersion for the sample-specific count normalization and subsequently applies a linear regression method to identify differentially expressed genes (DEGs)  97, 98. Preliminary comparison of anaplastic and externally-generated grade I meningioma data revealed evidence of laboratory batch effects, which we mitigated with two batch-correction methods: RUVg and PEER  99, 100. RUVg estimates the factor attributed to spurious variation using control genes that are assumed to have constant expression across samples  [101] [102] [103]. We selected control genes (RPL37A, EIF2B1, CASC3, IPO8, MRPL19, PGK1 and POP4) on the basis of previous studies of suitable control genes for transcript-based assays in meningioma  104.