dataesr/scientific-paragraphs-categorization huggingface dataset:
As the EMT spectrum of CTCs resemble their tumor and cancer cell line counterparts more closely, this observation may add further evidence to substantiate that the gene expression data for the five cancer studies have been derived from a relatively pure population of CTC and not from cells of hematological origin. Future CTC expression studies with improved isolation techniques and comparison with expression profiles of different cell types will help to comprehensively characterize the molecular signatures of CTCs. Considering the disparate nature of the CTC_FC dataset, we built a gene interaction-based functional EMT network using a selected subset of gene expression data from colorectal, pancreatic and breast CTCs (DEGs when compared to PBMCs); this was done to acquire an accurate comparison of differential values and allow us to segregate high-confident EMT hub molecules from the network.
The EMT network (Fig. 7 ) was built using Cytoscape 88 (version 3. 3. 0; http://www. cytoscape. org/) by including key EMT regulators. The differential gene expression data from CTCs were then overlaid on this network. We noted an upregulation in SPARC, MYLK, MYL6, GG11, F13A1 and FHL1 (mesenchymal markers) and CD9, LCN2, and ABCC3 (epithelial markers) in  Regulatory network depicting molecular interactions of EMT genes in CTCs across three cancers. The epithelial markers reported in our dataset are represented in green ovals; mesenchymal markers in red ovals. The yellow ovals represent other key EMT molecules reported in literature but not identified in our dataset. CTCs of at least two cancers. SPARC (also observed as top molecule in unsupervised clustering) is a well-known marker associated with invasion and an important mediator of bone metastasis  89, 90.
Interestingly, MYLK and CD9 have been implicated in lymph node metastasis in different cancers  91, 92  , whereas ABCC3 overexpression is reportedly involved in glycolysis, drug resistance and overall poor prognosis in bladder cancer [not in our dataset]  89, 93. However, some markers may play a tumor-specific role, as the loss of FHL1 has been linked with increased metastasis in esophageal and gastric cancers  93, 94  [these cancers are not represented in our dataset]. Increased secretion of lipocalin-2 (LCN2), a tissue-specific factor not found in peripheral blood leukocytes, was reported in a CTC-derived cell line (BHGc7) from relapsed lung cancer patients  95. Thus, CTC EMT phenotypes in different cancers may provide important, high-confident leads to monitor tumor-specific or CTC-specific events following therapeutic intervention.
4%) were thoroughly annotated for gene expression in CTCs [see Additional Supplementary Information and Supplementary Table  S5 ]. Gene expression data were taken from semi-quantitative and qualitative experiments for gene annotation. This lenient but statistically significant (p < 0. 05) gene list constituted genes reported to be overexpressed or downregulated in CTCs when compared to primary tumors, cancer cell lines, peripheral blood mononuclear cells (PBMCs), or normal tissue (each classified as CTC sub-group in the manuscript). Gene expression data were taken from studies that evaluated cancer patient CTC levels, cancer cells cultured ex vivo and spiked into the blood collected from healthy individuals, or CTCs isolated from blood of orthotopically grafted tumors in mice. From this extensive dataset compiled from 98 papers, data of differentially expressed genes from five large-scale transcriptomics studies across five different cancers was collated separately to generate a unique gene list with fold change values.
Transcriptomics dataset analysis. Transcriptome datasets were compiled from both high-throughput RNA-Seq experiments and microarray experiments (whichever available) related to CTCs. Supplementary results were compiled where available; otherwise data were processed to obtain the final set of genes. From the semi-quantitative experiments, sets of statistically significant, differentially expressed genes (DEG) with a log2 fold change cutoff of 1. 5 and p-value < 0. 05 were considered for downstream analysis. The colorectal and pancreatic microarray gene expression data (Gene Expression Omnibus repository; Accession Number: GSE31023 and GSE18670, respectively. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE31023 and https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE18670) were reanalyzed using GEO2R (http://www.
ncbi. nlm. nih. gov/geo/geo2r/) package to obtain the set of DEGs in CTCs. RNA-Seq data from the prostate cancer study were also reanalyzed to obtain genes that were differentially expressed with log2 fold change of 1. 5 or higher using an in-house bioinformatics pipeline. Genes were filtered to include only the protein-coding genes for pathway analysis. Any pseudogenes, uncharacterized genes, non-coding RNA gene annotation were excluded. Entrez Gene IDs and gene biotype information were retrieved using the Ensemble Biomart (http://asia. ensembl. org/biomart/ martview/) tool for human genome assembly; GRCh38p. 5. Genes from various datasets were aggregated to form a master list of non-redundant DEGs. For EMT scoring, transcriptome datasets of CTC from the five cancer studies (Table  1 ; Supplementary Table  S6 )-GSE18670, GSE31023, GSE38495, GSE45965, and GSE67980 were downloaded from GEO.
The pre-processed data GSE45965 was also used. Dataset on Affymetrix platform (GSE18670) was RMA normalized using R version 3. 3. 1, Affy 1. 50. 0, whereas dataset on Agilent platform (GSE31023) was normalized using R limma 3. 28. 21. The RPM and RPKM normalized were extracted for RNA-Seq data GSE38495 and GSE67980. EMT score was computed using signatures and methods detailed in our previous study  18. Further, we computed the EMT scores for each of the five cancer CTC datasets using the gene expression data from genes overlapping across the datasets (n = 3167) and compared them to previously computed EMT scores for each tumor type and its cancer cell line collection  18  (Supplementary Fig. 4 ) Pathway analysis. Two sets of data were used for pathway analysis: (1) the lenient gene list collated from 98 studies and (2) the differentially expressed genes list from the five high-throughput transcriptomics studies.
(Denoted as CTC_ALL and CTC_FC (FC = fold-change), respectively for the purpose of differentiating between the datasets. ) The Ingenuity pathway analysis (IPA-Build: 377306 M; Version: 27216297; IPA, Qiagen, Redwood City, http:// www. ingenuity. com/) tool was used for pathway analysis. Relationships of molecules differentially expressed in our dataset were inspected using the Core Analysis module in IPA Ingenuity Knowledge Base reference repository. Only the overexpressed genes from the CTC_ALL and CTC_FC datasets were uploaded into IPA, separately. Canonical pathway analyses identified the top canonical pathways that were significant in our datasets. PathVisio (3. 2. 1; revision 4025; www. pathvisio. org), a free, open-source software for drawing and editing biological pathways, was used to manually depict the enrichment for Leukocyte Extravasation Signaling.
Molecular interaction data were sourced from the KEGG pathway database (http://www. genome. jp/kegg/) and IPA pathways (IPA, Qiagen, Redwood City, http://www. ingenuity. com/). Literature mining was done for reported signaling patterns in CTCs, and only a handful of publications were available. Molecular interactions were annotated from these publications and appended onto the depicted pathway. An internal pathway review system was followed to carefully inspect for erroneous annotations. The pathway map was uploaded to GeneSpring software provided by Agilent Technologies (software version 13. 0; http://www. genomics. agilent. com/en/ Microarray-Data-Analysis-Software/GeneSpring-GX/?cid= AG-PT-130&tabId= AG-PR-1061) for enrichment. The gene list used for enrichment was identical to that used for IPA analysis (CTC_ALL, overexpressed genes n = 3,251).
STRING (version 10. 0; http://string-db. org/) database was used for generating protein-protein interactions and Cytoscape (version 3. 3. 0; http://www. cytoscape. org/), an open-source platform, was used for visualizing the EMT biological pathways and molecular interaction network, and integrating the CTC gene expression data. In-house python scripts and the Adobe Illustrator (version CS5. 1) graphics editor were used to draw or edit the figures.
Acknowledgements We thank the  Department of Biotechnology (DBT) ,  Government of India  for research support to the  Institute of Bioinformatics. We thank the  Council of Scientific and Industrial Research (CSIR) ,  University Grants Commission (UGC), and Government of India and Infosys Foundation  for financial support. PK is a recipient of the  Ramanujan Fellowship  awarded by the  Department of Science and Technology (DST), Government of India , and AKM is a recipient of  BINC Junior Research Fellowship  from  DBT, Government of India.
Moreover, for our simulations, if in the first cohort grade 2 toxicities (but no DLT) were observed for all patients, we assigned the same dose for the next cohort. If in the first cohort DLTs were observed for all patients, we stopped the trial. For the Bayesian approach, we considered two distinct parametrizations and associated prior distributions. In the first case each parameter β l , where l = 1, 2, is parametrized in the exponential form, that is β l = e γ l , where each γ l is assumed to have independent normal distribution with zero mean and standard deviation of 1. 34 as prior distribution. This is the conventional parametrization and prior used for the empiric working function in the dfcrm package in R  (Cheung, 2013; R Core Team, 2013). In the second case, we use the parametrization proposed for the CRM with multiple constraints  (Lee and others, 2011) , and assume independent exponential distributions with a rate of 1 as the prior distribution for each β l in line with the CRM convention (O'  Quigley and others, 1990; Yuan and others, 2007).
The tables also display information on the number of patients that were assigned before full heterogeneity was observed to invoke both constraints. Using likelihood estimation and the staged approach, the median number of patients before full heterogeneity was observed ranged between 10 and 22 depending on the true probabilities of toxicities and the entry time. This could explain why under some scenarios the performance of the TITE-CRM with multiple constraints is similar to the TITE-CRM. Moreover, the two parametrizations used along with the Bayesian estimation yielded identical results (data not shown). Thus, only the results using the normal prior are displayed.
ACKNOWLEDGMENTS We would like to thank the reviewers and the Associate Editor for their thoughtful comments which improved the manuscript. Conflict of Interest:  None declared. FUNDING American Cancer Society  (Grant Number  MRSG-13-146-01-CPHPS  to S. M. L. );  NIH/NCATS  ( UL1TR000040  and  UL1TR001873  to Y. K. C. and S. M. L. ), in part;  InSPiRe (Innovative Methodology for Small Populations Research) Project of the European Union Seventh Framework Programme for Research, 28 S. M. LEE AND OTHERS Technological Development, and Demonstration  under grant agreement ( FP HEALTH 2013-602144  to S. Z. and M. U. ).
L173R, GNRH1 c. 18-19insA, and GNRHR p. R139H (Bouligand et al, 2009; Avbelj Stefanija et al, 2012; Beneduzzi et al, 2014; Choi et al, 2015). Haplotype analysis based on sequence variants within KLB was insufficiently informative to distinguish between a founder effect and a mutational hot spot for p. F777delF. This variant is significantly more frequent in the CHH cohort compared to ethnically matched controls from ExAC database (P = 0. 01), as are the other KLB mutations except p. R309W and p. K815E (Table  1 ). In addition, none of the identified KLB variants were found in a homozygous status in the ExAC database.
Thus, both deficiency and excesses of FGF21 may lead to defects in GnRH function, consistent with the concept that a tightly regulated energy balance is required for optimal reproductive capacity. While no CHH patients were found to harbor mutation in FGF21, 4% exhibit heterozygous mutations in KLB. All KLB mutants were confirmed to be loss of function in vitro as well as in vivo using a rescue assay in C. elegans. Notably, the p. F777delF mutation is found in seven probands of European descent. Although this variant is present in one out of 133 non-Finnish European controls (0. 7%) in the ExAC database, it is significantly enriched in our CHH cohort versus controls. As most mutations in CHH are private, this occurrence suggests either a mutational hot spot or a founder effect (Avbelj  Stefanija et al, 2012; Choi et al, 2015).
3)]. Variants were confirmed by Sanger sequencing of both strands and with duplicate PCRs, and are described according to HGVS nomenclature (den  Dunnen & Antonarakis, 2000). The clinical and genetic studies were approved by the Institutional Review Board of Partners Healthcare and the ethics committee of the University of Lausanne, and were conducted in accordance with the guidelines of the Declaration of Helsinki. All participants provided written informed consent prior to study participation. Our datasets were obtained from subjects who have consented to the use of their individual clinical and genetic data for biomedical research, but not for unlimited public data release. Therefore, we submitted the data to the European Genome-phenome Archive (EGAS00001002568), and researchers can apply for access to the raw data.
e. EndoH-resistant band, 140-kDa) to the total protein using the ImageJ program (Wayne Rasband, NIH, USA). Three independent experiments were performed in quadruplicate. Cell surface antibody binding assay COS7 cells were transiently transfected with N-terminus flagtagged KLB constructs and analyzed for KLB expression on the cell surface using an anti-flag antibody (1:1,500) and [ 125 I]rabbit anti-mouse IgG (300,000 cpm/well; Perkin Elmer, Waltham, MA, USA) as previously reported  (Raivio et al, 2009). Samples were run in quadruplicate and the experiment repeated three times.
Statistical analysis For in vitro and in vivo experiments, sample sizes were chosen according to the standard practice in the field. Unless otherwise indicated, all analyses were performed using Prism 7 (GraphPad Software), assessed for normality and variance, and log-transformed when appropriate. The analysis of differences between the allele frequency of KLB mutations in CHH patients and ExAC controls was performed by Fisher's exact test. For in vitro reporter gene assays, the activity of each FGFR1 or KLB mutant was compared to WT in terms of EC 50 dose and the maximal FGF response by F-test. Expression levels of WT and KLB mutants were compared using an unpaired t-test. In the C. elegans assay, a two-tailed Fisher's exact test was used to determine significance between human KLB WT vs.
Acknowledgments We are grateful to the patients and families who contributed their time, medical information, and DNA samples to this study. We thank  Prof. Nabeshima  ( Kyoto University School of Medicine, Kyoto, Japan ) for providing the KlbKO mice,  Sarah Gallet  and  Emilie Caron  ( Inserm U1172 ) for expert technical assistance, and  Sue Stewart  ( Birmingham Women's and Children's NHS Foundation Trust)  for the assistance in patient recruitment. This work was supported by the  Swiss National Science Foundation   Sinergia Grant  ( CRSII3_141960 ,  N. Pitteloud ,  U. Albrecht  and  M. Mohammadi ); the  Swiss National Science Foundation  grant ( SNF 31003A 153328 ,  N. Pitteloud ); the  Agence National pour la Recherche (ANR, France)  Grant  GlioShuttles4Metabolism  ( ANR-15-CE14-0025 , V.
L1011P c. 3032T>C 1 0 6. 0e10 À5 0. 049 2/2 ↓ ↓ NS ↓ None Uncertain significance CHH, congenital hypogonadotropic hypogonadism; MAF, minor allele frequency; P-value, Fisher's exact test of allele frequency in CHH cohort versus ethnically (Richards et al, 2015)ExAC database; PPH2, polyphen-2; ↓, decreased; ↑, increased; NS, not significant. ACMG,American College of Medical Genetics and Genomics(Richards et al, 2015). ª 2017 The Authors EMBO Molecular Medicine Vol 9 | No 10 | 2017.
For the typical linewidths observed in NGC 1068 we can resolve in velocity the two fine groups but not the lines they consist of. If the opacity of the lines is low, we expect that the strongest hyperfine component corresponds to J = 3/2 -1/2, F = 2 -1 at ν = 87. 317 GHz (rest frequency). We therefore take this frequency as the reference for the velocity scale, which is hereafter derived relative to v HEL sys =1136 km s -1 (García-  Burillo et al. 2016). We calibrated the data using the ALMA reduction package CASA 1. The calibrated uv-tables were transformed to GILDAS 2  readable format where the mapping and cleaning were performed. The angular resolution obtained using natural weighting was 1. 0 × 0. 7 (70 pc × 50 pc) at a position angle PA = 69 • in the line and continuum data cubes. The conversion factor between Jy beam -1 and K is 240 K Jy -1 beam.
The line data cube was binned to a frequency resolution of 2. 92 MHz ( 10 km s -1 ). The estimated 1σ-rms in the line data cube, derived in line-free emission areas, is 0. 4 mJy beam -1 in 10 km s -1 -channels. An image of the continuum emission at a mean frequency of 86. 3 GHz was obtained by averaging 60 channels ( 600 km s -1 ) free of line emission around the C 2 H group. The corresponding point source sensitivity for the continuum is 80 µJy beam -1. All the maps have been corrected for the attenuation by the primary beam, assuming a FWHM of 70 for the latter. We estimate that the absolute flux accuracy in the maps is about 10-15%. As our observations do not contain short-spacing correction, we expect to start filtering out an increasing fraction of the line and continuum emissions on scales > 4 ; the latter corresponds to the expected largest angular scale of the emission that can be recovered in the ALMA maps used in this work.
This suggests that a different mechanism is at play in this region. For temperatures T ex ≥ 25 K the derived C 2 H abundances lie in the range X(C 2 H) 10 -7 -10 -6. It is worth noting that these higher temperatures are specifically required in order to fit the multiline CO dataset of the outflowing CND  (Viti et al. 2014 ). In particular,  Viti et al. (2014)  used LTE to conclude that the rotational temperature, T rot , of the CO emitting gas in the CND 40 -60 K. This is also in excellent agreement with the values of the dust temperature required to fit the SED of dust emission in the outflow region out to r ≤ 400 pc: T dust 46  ± 3 K (García-Burillo et al. 2014). Furthermore  Viti et al. (2014)  used non-equilibrium radiative transfer schemes (RADEX) to conclude that the kinetic temperature of molecular gas traced by CO lines in the outflow is significantly high: ≥ 100 K.
Chemical models: description We note that a comprehensive chemical modeling of the C 2 H molecule is beyond the scope of this observational paper. Moreover, we are aware of the limitations of having observed only one N transition of the C 2 H multiplet, leading to an uncertain derivation of the fractional abundance. Hence we can only qualitatively discuss the origin of this emission and its potential role as a tracer of distinct gas components. We use and augment the grid of chemical models from  Viti (2017)  run using the UCL_CHEM time dependent gas-grain chemical model  (Viti et al. 2011) , which includes a parametrized shock module  (Jiménez-Serra et al. 2008) , as well as a very small grid of PDR models taken from  Bell et al. (2006)  and  Vasta et al. (2010) , run using the UCL_PDR code.
We assume for all the models a solar C/O abundance ratio ∼ 0. 6. Below we briefly present the codes, which are more extensively described in the above references. The code UCL_CHEM (now publicly available at https://uclchem. github. io and published in  Holdship et al. 2017 ) is a time dependent gas-grain chemical model that computes the evolution of chemical abundances of the gas and on the ices as a function of time, starting from a diffuse and atomic gas. The code is coupled with the parametrized shock model of  Jiménez-Serra et al. (2008). The code is run in two distinct temporal phases, denoted as Phase I and Phase II. The initial density in Phase I is ∼ 10 cm -3. The gas is allowed to collapse and reach a high density by means of a free-fall collapse process. In this context the collapse is not meant to represent the formation of protostars, but it is simply a way to compute the chemistry of high density gas in a self-consistent way starting from a diffuse atomic gas, i.
Acknowledgements. We acknowledge the staff of ALMA in Chile and the  ARC-people at IRAM-Grenoble in France  for their invaluable help during the data reduction process. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2013. 1. 00055. S and # 2011. 0. 00083. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. We used observations made with the  NASA/ESA Hubble Space Telescope , and obtained from the  Hubble Legacy Archive , which is a collaboration between the  Space Telescope Science Institute (STScI/NASA) , the  Space Telescope European Coordinating Facility  ( ST-ECF/ESA ), and the  Canadian Astronomy Data Centre  ( CADC/NRC/CSA ).
SGB and AU acknowledge support from the  Spanish MINECO  and  FEDER  funding grants  AYA2016-76682-C3-2-P ,  AYA2016-79006-P  and  ESP2015-68964-P. CRA  acknowledges the  Ramón y Cajal Program  of the  Spanish Ministry of Economy and Competitiveness  through project  RYC-2014-15779  and the  Spanish Plan Nacional de Astronomía y Astrofísica  under grant  AYA2016-76682-C3-2-P. AAH acknowledges support from the  Spanish MINECO  and  FEDER  funding grant  AYA2015-6346-C2-1-P.
Defining robust sets of gene orthologs in the Brassicaceae In order to assess rigorously the conservation of H3K27me3 marking between gene orthologs, we first established a robust comparative framework. Initial work based on the genome sequence of Ath, Aly, Capsella rubella, Brassica rapa, and Thellungiella parvula led to the reconstruction of the ancestral Brassicaceae karyotype with 30,968 protogenes  [16]. Since then, the genome of two additional Brassicaceae has been sequenced (Aal and Tarenaya hassleriana  [18, 19] ). Using this extended dataset as well as the genome sequence of two outgroup species, we now define 27,343 Brassicaceae protogenes with descendants in Ath, Aly, or Aal (see Methods; Fig. 1a ). This set comprises 26,027 protogenes for which the descendants were not subjected to recent duplication in any of the three species.
To explore further the importance of H3K27me3 marking in relation to the 3D organization of the genome, we analyzed another Hi-C dataset that provides information on the strength and distance of chromosomal interactions, both in wild-type (WT) and H3K27me3depleted clf swn double mutant plants  [26]. Using a broad classification (see Methods), interactions were ranked as weak, moderate, or strong (Additional file 1: Table  S9 ). When comparing the distribution of contact distance in the WT for synteny blocks enriched in gene orthologs with plastic or constrained H3K27me3 marking, we found that the latter tend to be associated with a higher number of strong contacts in the 18-23 Mbp distance range (Fig. 5b ). This distance range is similar to the size of Ath chromosomes, which therefore implies a specific contribution of orthologs with constrained H3K27me3 marking to strong intra-chromosomal interactions involving one or both ends of chromosomes.