dataesr/scientific-paragraphs-categorization huggingface dataset:
In such cases, modeling the CNV effect according to the distance between pairs of interacting loci could be an interesting extension to our current model. The choice of which normalization method to use in Hi-C data analysis, will depend on the specific context and biological question. As an example, we demonstrate here that although chromosome compartment calling and PCA analysis are not dramatically affected by the copy number changes in MCF7 data, inappropriate normalization of the data can lead to incorrect interpretation of the results of such analysis. This effect can be even greater with other cell lines, as it is dependent on the copy number profile of the tumor (data not shown). This first experiment shows that CAIC, which removes the copy number biases, can enable existing analysis pipelines to be applied to cell lines with abnormal karyotypes.
Conclusions Taken together, the analyses covered here confirm that Hi-C can be a powerful technique to explore strutural variations on tumor samples and highlight the importance of using dedicated methods for the analysis of such data. The two new methods we introduce here (LOIC and CAIC) perform well on both simulated and real cancer Hi-C data sets, each answering different biological questions. As the application of Hi-C techniques to cancer and other samples to explore the 3D architecture of their genomes will continue to grow in the coming years, the importance of using the right techniques and developing more accurate tools to characterize such data sets appropiately is critical.
Simulation of cancer Hi-C data Before we turn to how to appropriately model cancer Hi-C data, let us first review some terminology. In the literature, cis-contact counts refers to the contact counts between two loci of the same chromosome: this includes intrachromosomal contact counts but also inter-chromosomal contact counts of homologous chromosomes. In this paper, we restrict the use of cis-contact counts to contact counts issued from the same DNA fragment, and we denote by "trans-homologous" (transH) interactions, the interactions between homologous chromosomes. Note that cis and transH contact counts are mostly indistinguishable (with the exception of allele-specific Hi-C) in Hi-C data hence the simplification of terminology usually used. We now return to the problem at hand: how to simulate a contact count matrix C sim of a cancer genome with abnormal copy number from a raw diploid contact count matrix C.
Estimation of copy number from the contact count matrix The copy number signal can be directly inferred from the Hi-C data in two steps. We first calculate the onedimensional (1D) signal as the sum of genome-wide contact per bin, assuming that this signal reflects the true contact frequencies including the systematic Hi-C biases and the CNVs signal. We further calculate the GC content, the mappability and the effective fragment length of each bin end as already proposed  [26]. The local genomic features of all chromosome bins are defined as the average of the corresponding features among all overlapping fragment ends. We then apply a Poisson regression model to correct the signal from GC content, mappability and fragment length, using the model proposed by Hu et al. [26]. The corrected profile is obtained by subtracting the fitted values to the observed data, and rescaled to be centered on 1.
Acknowledgments We would like to thank  D. Gentien  and the  Institut Curie genomic platform  for providing the MCF7 and T47D Affymetrix data. Many thanks to  P. Gestraud ,  P. Hupe ,  F. Picard ,  G. Rigaill  for their help in defining the segmentation strategy, and to  J. van Bemmel  and  A. Nevière  for their feedbacks on the manuscript.
Funding This work was supported by the  Labex Deep , the  Ligue Contre le Cancer , the  European Research Coucil  ( SMAC-ERC-280032 ), the  ERC Advanced   Investigator award  ( ERC-250367 ), the  ABS4NGS  project ( ANR-11-BINF-0001 ), the  Gordon and Betty Moore Foundation  (Grant  GBMF3834 ) and the  Alfred P. Sloan Foundation  (Grant  2013-10-27 ). The funding bodies did not play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Data Analysis Nonparametric Spearman's correlation analyses were employed to determine the relationship between cell density of the toxigenic species and toxin abundances, as well as between toxin cell quotas and environmental parameters. Canonical correspondence analysis (CCA) and associated Monte Carlo tests were performed using Canoco 4. 5 software to explore the association between species distribution (after square root transformation) and the following environmental parameters: temperature, salinity, bottom depth, silicate, phosphate, and nitrate + nitrite (the latter expressed as inorganic nitrogen concentration). Temperature, salinity, and nutrient concentrations were estimated as the average values measured from the surface to 20 m depth.
ACKNOWLEDGMENTS The R/V Coriolis II expedition was jointly funded by the  Ministerio de Ciencia, Tecnología e Innovación Productiva (MINCyT) , the  Province of Chubut , the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) (Argentina)  and the  Institut des sciences de la mer de Rimouski (ISMER). The authors thank  Wolfgang Drebing  ( AWI ) for sample extraction and toxin measurements by LC-MS/MS and  Annegret Müller  ( AWI ) for toxin extraction and analyses of PSP toxins. In addition, the friendly reception and support of the 2014 captain and crew of the  R/V Coriolis  are gratefully acknowledged. This work was partially funded by  PIP 0122 (CONICET)  and  PICT 0576  ( ANPCyT ) grants, and supported by the  Helmholtz-Gemeinschaft Deutscher Forschungszentren  through the research program Polar regions And Coasts in the changing Earth System (PACES) of the  Alfred Wegener Institut-Helmholtz Zentrum für Polar-und Meeresforschung.
FCs were deployed into the reactor core using a specially designed positioning system, comprising several hollow Al guide tubes, a drive mechanism and a data acquisition system. An integrated cable connecting the chambers and the data acquisition system was used for inserting and withdrawing the FCs into and out of the reactor core. The FC position was regulated by a pneumatic drive consisting of a series of valves and pistons. The axial positioning was ensured by an incremental system which measures the FC position relative to the reference position at the end of the guide tube and a linear system for absolute position measurements through the use of a magnetized strip. Fission reaction rates were measured at 24 axial locations inside the core with increment steps of approximately 25 mm. A photograph of the positioning system is shown in Figure  2.
TRIGA Monte Carlo computational model The fission chamber experiment was modelled and inserted in the existing Monte Carlo model of TRIGA Mark II reactor  [4] , which has been validated against measurements in the past  [6] ,  [13]. The calculations were performed using the state-of-the-art Monte Carlo transport code MCNP version 6. 1  [1]  and ENDF/B VII. 1 nuclear data library  [14]. The fission reaction rates were calculated and averaged over the 4 mm high sensitive area of the fission chambers in 1 mm increments over the whole height of the reactor core. For measurement to calculation comparison the computation results had to be converted to absolute reaction rate units, i. e. counts per second. This was carried out using a normalisation factor K  [9] : R = K • R mc ; K = P ν w f k ef f , ( 1 ) where R is the absolute reaction rate, R mc is the Monte Carlo calculated reaction rate, P is the reactor thermal power, ν is the average number on neutron released per fission, w f is the average deposited energy per fission event and k ef f is the calculated effective multiplication factor of the TRIGA computational model.
Proteomic and label-free quantitative MS/MS data analyses Proteomic and nLC-MS/MS analyses were performed following previously published protocols  [48]. Briefly, protein samples were subjected to SDS-PAGE and stained with colloidal blue, then stained bands were cut from the gel, destained and digested by trypsin. The resulting peptide mixtures were analyzed using an Ultimate 3000 nanoLC system (C18 PepMapTM trap column, Dionex, Amsterdam, The Netherlands) coupled to an Electrospray Q-Exactive quadrupole Orbitrap benchtop mass spectrometer (Thermo Fisher Scientific, San Jose, CA). The mass spectrometer was operated in positive ion mode at 1. 8 kV needle voltage. Data were acquired using Xcalibur 2. 2 software in a data-dependent mode. MS scans (m/z 350-1600) were recorded at a resolution of R = 70,000 (at m/z 200) with an AGC target of 3E6 ions collected within 100 ms.
Dynamic exclusion was set to 30 s and the top 15 ions were selected from fragmentation in HCD mode. MS/MS scans with a target value of 1E5 ions were collected with a maximum fill time of 100 ms and resolution of R = 17,500. Additionally, only + 2 and + 3 charged ions were selected for fragmentation. Acquired peptide were searched by SEQUEST implemented in Proteome Discoverer 1. 4 (Thermo Fisher Scientific Inc. ) against a Pseudotsuga menziesii v1 transcriptomeproteome database from PineRefSeq (54,595 entries, August 2016, https://treegenesdb. org/FTP/Genomes/Psme/v1. 0/annotation/). Spectra from peptides larger than 5000 Da or smaller than 350 Da were rejected and mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 ppm and 0. 02 Da, respectively. Only band y-ions were considered for mass calculations.
Oxidation of methionine (+ 16 Da) was considered as a variable modification and carbamidomethylation of cysteines (+ 57 Da) as a fixed modification. Two missed trypsin cleavages were allowed. Peptide were using the Percolator algorithm  [49]  and only "high confidence" peptides were retained, corresponding to a < 1% False Discovery Rate (FDR) at peptide level. Raw LC-MS/MS data were imported into Progenesis QI for Proteomics 2. 0 (Nonlinear Dynamics Ltd. , Newcastle, U. K). Data processing included the following steps: (i) features detection, (ii) features alignment across all samples, (iii) volume integration for 2-6 charge-state ions, (iv) raw data normalization based on medians of ratios of all intensities of relevant fragments to references (calculated from LC-MS features), (v) import of sequence information, and calculation of protein abundance (sums of volumes of corresponding peptides).
Functional characterization and gene ontology analysis Changes in expression, relative to appropriate controls, were calculated based on the cumulative intensity of each peptide (classifying proteins with ≥1. 5-fold change ratios as up-or down-regulated). All sequences were mapped against Gene Ontology (GO) terms in the TAIR Arabidopsis thaliana database (https://www. arabidopsis. org/) for functional annotation. The proteins were then classified based on their biological functions using Web Gene Ontology Annotation Plot software at level 2 for biological processes (Panther, http://pantherdb. org/) [51]. A binomial test and Bonferroni's correction were applied with Panther software to identify classes represented significantly more frequently than expected among up-and down-regulated proteins in each type of material.
As the gene ontology is currently extremely poor for Douglas-fir, we also applied another method to assess enrichment of GO terms in our protein sets, using the Bioconductor R package topGO 2. 26. 0  [52] , based on the "weight" method and Fisher's exact test. We compared the sets of significant proteins against the 4813 Douglas protein total dataset, and mapped each protein to the best Arabidopsis homolog by BlastP searches. Then, each GO term from Arabidopsis thaliana was associated with the corresponding Douglas protein for topGO analysis.
Phenolic compounds were apparently deposited in vacuoles as granules or droplets, resulting in brownish cells that were easily recognizable in aggregates with cell walls stained blue and both nuclei and cytoplasm (slightly) stained pink/red (see Fig. 4c ). Surprisingly, phenolics accumulating in vacuoles as amorphous deposits reacted differentially with Alcian Blue, forming complexes of various colors from grey-blue to ginger, suggesting they had variable chemical composition (Additional file 4: Figure  S3B, E, F ). Alcian Blue staining of phenolic compounds was confirmed histochemically by other dyes (Azur II and Safranin; data not shown). The occurrence of NEC aggregates interspersed in EMs was found to vary between lines as described below. However, repetitive somatic embryogenesis globally resulted in reduced frequency of NEC clusters and, concurrently, in reduced phenolic contents in EMs.
The SNEA of significant proteins is based on Arabidopsis thaliana bibliographic database, which implies the network representation with Arabidopsis protein names. The SNEA provided further indications of their functions, interactions and putative targets, as well as regulators involved in metabolic pathways that may be affected during repetitive somatic embryogenesis (Fig. 8a, b ). Overall, 70 significant proteins up-or down-regulated after 2 ry (33 proteins) or 3 ry somatic embryogenesis (51 proteins) were integrated in the constructed networks. They included many proteins associated with metabolism of plant growth signaling compounds, including plant growth regulators such as jasmonic acid, ABA and salicylic acid, together with proteins related to lignin (especially after 3 ry somatic embryogenesis) and flavonoid secondary metabolites (especially after 2 ry somatic embryogenesis).
Proteomic analyses revealed important interactions between proteins and plant growth signaling in "plant development" process during the somatic embryogenesis cycles We compared protein profiles of both 2 ry vs 1 ry lines and 3 ry vs 2 ry lines to identify possible proteomic effects of an additional round of somatic embryogenesis. More proteins were detected during both proteomic analyses (2293-2554, 4813 overall) than in previous analyses based on two-dimensional gel electrophoresis  [40, 44]  confirming that shotgun-iTRAQ technology has greater potential for both identifying and quantifying proteins. This should be generally valid for all plant species, but the availability of the Douglas-fir transcriptome enabled about 30% more successful protein identifications than use of the Picea glauca database.
The latter study highlighted several epigenetic regulation mechanisms involved in stage-to-stage transitions. Biological processes associated with significant proteins were identified by both GO analyses (Table  3 , Additional Table  S4  and Fig. 7 ) and screening against bibliographic data. The resulting networks, showing connections between significant proteins according to their involvement in biological processes and/or interactions with regulatory factors or induction signals, are presented in Fig. 8. Interpretation of proteomic results based on protein networks is a powerful approach, but has several limitations. First, it only highlights connected proteins in the networks. Second, the bibliographic database used is specific to Arabidopsis thaliana, so resulting networks inevitably miss important elements of woody, perennial species' networks.
Gene Ontology (GO) terms enriched in significant proteins after repetitive somatic embryogenesis in Douglas-fir (Continued) Gene Ontology Identifiant; Annot. : number of annotated Douglas proteins with GO terms in the data set; Sign. : number of Douglas proteins in the analysed dataset; Exp. : expected number of interesting proteins mapped to the GO term if randomly distributed over all GO terms; ratio sign. /exp: ratio between significant and expected proteins mapping to the GO term; Expression: proteins assigned to specific GO term are over-expressed in 1 ry (1 ry > 2 ry ), 2 ry (2 ry > 1 ry ) or 3 ry lines (3 ry > 2.
Time-scaled phylogeny The phylogenetic affinities of Lilingostrobus were assessed via a cladistic analysis based on Xue  [8]. A data matrix (S1 Taxa with too many missing characters such as Minostrobus  [23]  or Monilistrobus  [34]  were not included in the matrix. The inclusion of other taxa such as Longostachys  [17] , Changxingia  [18]  and/or Paurodendron  [35]  resulted in poorly resolved phylogenies and it was decided to reject those taxa. Data analysis was performed using PAUP Ã 4. 0 (Phylogenetic Analysis Using Parsimony, and other Methods) software  [36]  (S1 Text). The analysis resulted in 9 equally parsimonious trees (Consistency Index (CI) = 0. 734; Homoplasy Index (HI) = 0. 265; CI excluding uninformative characters = 0. 717; HI excluding uninformative characters = 0. 282; Retention Index = 0.
886; Rescaled consistency index = 0. 650). Subsequently, consensus tree topology (S2 Text) was plotted against the stratigraphy in order to construct a time-scaled phylogeny using strap (Stratigraphic Tree Analysis for Palaeontology) package  [37]  of the R statistical software (version 3. 2. 1, R Developmental Core Team 19 2015)  [38]. The calibration of resulting cladogram was performed using the known temporal distribution (S3 Text) of the involved lycopsids (S2 Table, S4 Text). Default options were used but considering a minimum branch length of 1 million years. strap analysis was implemented according to the Bell and Lloyd's tutorial  [37]  was followed for implementation. See Supporting Information for raw PAUP (S1 Text) and strap files (S2 and S3 Text).
Acknowledgments BCM thanks the support provided by the  Région Hauts-de-France , the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development. We thank  Brigitte Meyer-Berthaud  (AMAP, Montpellier) for helpful comments on an earlier version of the manuscript. PG and PS are F. R. S. - FNRS Senior Research Associates.
A subsequent qRT-PCR was also performed on the 30 selected tumors to address prolactin (PRL), growth hormone (GH), proopiomelanocortin (POMC), and luteinizing hormone LHβ in order to exclude PRL/GH co-producing tumors and normal tissues that coexpress POMC/LHβ. Note that although normal pituitary tissues are not representative of normal lactotroph cells, we used a pool of normal pituitary from men and women as control references (data not shown). Microarray data for transcriptomic analysis were obtained from patients participating to the HYPOPRONOS (Programme Hospitalier de Recherche Clinique National 27-43) study, and genotyping and copy number alteration (CNA) analysis from patients included in the PITUIGENE study PHRC-INCa 2012 (ClinicalTrials. gov Identifier: NCT01903967). These studies were approved by the ethics committee of Lyon, and informed consent was obtained from each patient according to French law.
2%Triton X-100, and dried by centrifugation at 600 rpm. The slides were scanned using a Genepix 4000B scanner (Axon, Union City, USA) and Genepix software, with the laser set at 635 mm, the laser power at 100%, and the photomultiplier tube voltage at 60%. The scanned image files were analyzed using CodeLink expression software, version 4. 0 (GE Healthcare), which produces both a raw and normalized hybridization signal for each spot on the array. Transcriptomic data have been deposited in Gene Expression Omnibus under the accession number GSE120350.
Microarray Data Analysis The relative intensity of the raw hybridization signal on arrays varies in different experiments. CodeLink software was therefore used to normalize the raw hybridization signal on each array to the median of the array (median intensity is 1 after normalization) for better cross-array comparison. The threshold of detection was calculated using the normalized signal intensity of the 100 negative control samples in the array; spots with signal intensities below this threshold are referred to as "absent. " Quality of processing was evaluated by generating scatter plots of positive signal distribution. Signal intensities were then converted to log base 2 values. Differential expression analysis was performed using RStudio (http://www. rstudio. org) to isolate differentially expressed mRNAs between men and women lactotroph tumors.
Arrays were washed using the Affymetrix 450 fluidic array station and scanned using the GeneChip scanner 3000 7G. We generated CEL files using the Affymetrix GeneChip Command Console software (AGCC) 3. 0. The tissues and reference 103 genomic DNA were processed in a same batch and hybridized using the ProfileXpert platform. CGH and genotyping data have been deposited in Gene Expression Omnibus under the accession number GSE 22615.
Copy Number Alterations (CNA) Analysis Affymetrix CEL files were extracted using the Genotyping Console software version 3. 0 (Affymetrix). For SNP genotyping, we used the Birdseed (v2. 2) analysis algorithm. Accuracy of genotyping was checked by performing a concordance test between the processed reference 103 and a pre-processed reference 103 (Affymetrix). The test of concordance showed a 99. 79% homology between the two genotypes indicating a good performance of the platform. Moreover, samples showing a call rate >96% and a median of the absolute values of all pairwise differences (MAPD) metric <4 were considered in further analysis. CNA analysis was performed using Partek Genomics Suite version 6. 4 (Partek, St Louis, MO) following normalization by invariant set normalization procedure and computed signal intensities using perfect match and mismatch (PM/MM) model-based expression.
Raw copy number data was computed using a batch of 270 normal external controls samples from the International HapMap project and used as reference. To remove alterations not associated with tumor phenotype, copy number variation was also analyzed on a pool of normal pituitary samples processed simultaneously in the same batch as tumor samples and compared to the references. Inferred copy numbers were predicted using genomic segmentation algorithm. Only copy number alterations with cut-offs of >2. 7 copies for gain and <1. 3 for loss were considered.
Consistently, knocking down FMRP decreased bTC-3 invasion in vitro (Figure  4E ). Interestingly, while FMRP mRNA showed modest stepwise upregulation during PanNET tumorigenesis according to data from a published dataset  (Sadanandam et al. , 2015)  (Figure  S2E ), the upregulation of FMRP protein in liver metastases when compared with primary tumors was far more impressive  (Figures 4D and S2F). This result is consistent with observations in neurons that upregulation of FMRP by NMDAR signaling is largely post-transcriptional  (Gabel et al. , 2004; Todd et al. , 2003). Intriguingly, previously reported RIP-chip (ribonucleoprotein immunoprecipitation followed by microarray analysis) data showed that FMRP binds to HSF1 mRNA  (Ascano et al. , 2012). Moreover, both HSF1 and FMRP are known to play important roles in translational regulation  (Darnell and Klann, 2013; Santagata et al.
Next, we identified a set of 330 genes that distinguished MK801-treated B6 tumors from untreated B6 tumors (''MK801 treatment signature'', Figure  7C , Tables  S2,  and S3 ). Gene set enrichment analysis (GSEA)  (Subramanian et al. , 2005)  between the ''strain signature'' and the ''MK801 treatment signature'' revealed striking similarity between the two signatures (Figure  7D ), suggesting that C3H tumors were similar to MK801-treated B6 tumors when compared with B6 control tumors, which further supported our hypothesis that differences in NMDAR pathway activity are indeed among the defining characteristics distinguishing B6 and C3H PanNETs. The similarity between these two datasets led us to perform GSEA leading edge analysis  (Subramanian et al. , 2005)  to identify an ''NMDAR-pathway low signature'', which encompassed common driver genes in both strain signature and MK801 signature (i.