dataesr/scientific-paragraphs-categorization huggingface dataset:
Assortativity analysis Origin Assortativity (OriAs) and Assortativity of Origin Efficiency (OriEfAs) were calculated using the previously described measure of Chromatin Assortativity  (Pancaldi et al, 2016). Briefly, assortativity is defined as the Pearson correlation coefficient of the presence of an origin (OriAs) or of the value of origin efficiency (OriEfAs) across all pairs of nodes that are connected with each other. This value was calculated with the assortativity function in the igraph package for R. Chromatin assortativity (ChAs) for a particular feature is analyzed in relation to the abundance of said feature. For example, if a particular mark is found in the majority of the fragments in the network, its localization in specific areas of the network cannot be observed and the value of ChAs will be low.
C. Enrichment of WT (grey), APH (red) and CDC6 (blue) origins at the indicated genomic features (left) or chromatin states (right), relative to the randomized controls. CGI, CpG island; TSS, transcription start site; TTS, transcription termination site; interg, intergenic region; enhan, enhancer; activ pro, active promoter; bival pro, bivalent promoter; heter, heterochromatin; elong, transcriptional elongation; insul, insulator; repr, repressed chromatin; l. s. , low signal. All enrichments are significant at p<0. 001. D. Venn diagram of origin subsets, determined by intersections of WT, APH and CDC6 datasets. E. Genome browser examples of common and stress-responsive origins.
C. Assortativity of origin presence (OriAs) of the indicated origin datasets was calculated on the P-P subnetwork and plotted against the total number of origin-containing promoter fragments in each dataset. Filled circles represent OriAs values for each dataset and empty circles show the values of OriAs obtained after 100 origin label permutations ( Supp Figure4A). The solid black circles (WT Rand) represent 20 sets of TSS distance-preserving randomizations applied to the WT dataset.
ACKNOWLEDGEMENTS JM and MG thank all members of our laboratories for useful discussions. We are grateful to  Ana Cuadrado ,  Daniel Giménez  and  Ana Losada  ( CNIO, Madrid ),  Francisco Antequera  ( IBFG, Salamanca ),  Biola Javierre  ( Josep Carreras Leukaemia Research Institute, Barcelona ) and  Raphael Mourad  ( Paul Sabatier University, Toulouse ) for comments on the manuscript. We thank  Francisco Sanchez-Rivera  ( MSKCC, New York ) for discussions about phase-separated assemblies, and  Luis Quintales  ( IBFG, Salamanca, Spain ) for computational help and advice in the initial stages of the project. The work was supported by grants  BFU2013-49153-P  and  BFU2016-80402-R  to JM and  BFU2016-78849-P  to MG ( Spanish Ministry of Science, Innovation  and.
Empirical p-values were obtained as ( r +1)/( n +1), where n is the number of randomizations and r is the number of randomizations that produce a test statistic greater (or smaller) than or equal to that calculated for the real data. All statistical analyses for the computational part were performed using R. Scripts and R notebooks used to analyze origins in their 3D context can be found on https://github. com/VeraPancaldiLab/RepOri3D.
Processing of Mass Cytometry Data Data were acquired using EQ TM Four-Element Calibration Beads, normalized using Rachel Finck's MATLAB normalizer, and concatenated using the FCS file concatenation tool (Cytobank, Mountain View, CA). Cytometric profiles were manually gated to exclude the EQ TM Four-Element Calibration Beads, select singlets, and gate out non-specific background generated by metal-conjugated Ab binding to eosinophils  (22) , as represented in Supplementary Figure  1. The arcsinh (cofactor = 5) transformation was applied on marker expressions.
For the non-common markers (i. e. , markers that do not belong to the backbone composition), the phenotype of this cell corresponds to the specific marker expression. CytoBackBone is implemented in R and the source code can be found at: https:// github. com/tchitchek-lab/CytoBackBone. Each merge realized by CytoBackBone was calculated based on the set of common markers between the cytometric profiles to be merged (CD1c, CD3, CD11c, CD14, CD16, CD19, CD32, CD64, CD66, CD86, CD123, CD141, Granzyme B, and HLADR). A distance threshold of 3 was used for all merging.
Analysis of Merged Mass Cytometric Profiles SPADE analyses were generated using the public version of the SPADE R package. viSNE representations were generated based on the Barnes-Hut implementation of the t-SNE algorithm. Dot-plot visualizations were performed using Cytobank software (Mountain View, CA). SPADE analyses were set up and carried out with a down-sampling parameter of 5%. A random predownsampling was used to select 67,000 cells from each sample (67,000 corresponded to the number of cells contained in the smallest sample) before the SPADE analysis. Full upsampling was eventually performed after the cell cluster identification by SPADE. Categorical marker heatmaps were generated using the SPADEVizR package  (23). Marker expression categories represented in the heatmap were generated based on the intensity expression range of each marker.
Marker expression ranges were calculated based on the 5 and 95th percentiles of marker expression. Then, each range was divided into five uniform categories. The categorization of marker expression was computed based on the means of the individual SPADEexpression medians of each marker. These categories represented negative, low, medium, high, and very high marker expression using a color scale from white to dark red, which was used to produce the heatmap. Cell clusters with <50 cells (for the whole dataset) were annotated as unassigned and were colored in gray in the categorical heatmap representation. Indeed, the phenotypes of these clusters cannot be evaluated based on the mean expression of marker expressions. Hierarchical clustering of markers and clusters was performed using the Euclidian distance and was based on the complete linkage method.
HIV-Infection Induces Deep Phenotypic Modifications in Leukocytes The SPADE algorithm was then used to identify 500 cell populations in the whole dataset of the six merged cytometric profiles. The 72 markers were used as clustering markers to capture the maximal cell diversity (Supplementary Figure  2A ). For each cluster, the expression of CD3, CD11c, CD14, CD16, CD19, CD64, CD66, CD123, Granzyme B, and HLADR was represented (Supplementary Figure  2B ). We then generated a categorical heatmap representing the relative marker expression for each cluster to interpret the cellcluster phenotypes (Figure  1 ). T cell, B cell, PMN, NK cell, monocyte, and dendritic cell clusters were directly annotated on the heatmap according to the expression of CD3, CD11c, CD14, CD16, CD19, CD66, CD123, Granzyme B, and HLADR (Figure  1A ).
Clusters annotated as unassigned in the heatmap correspond to cell clusters having <50 cells from the whole dataset. In these situations, the number of associated cells is not enough to properly define an MSI for each marker and to associate a population to these cell clusters. The PMN population had the highest diversity and represented more than 50% of the cell clusters. In contrast, the pDC population represented only 0. 4% of the cell clusters. The 500 identified clusters were classified into two categories according to their cell abundance. The first category, named Enrichment Trend Clusters (ETCs), corresponded to clusters for which the cell abundance was specific to either HIV patients or healthy donors (cell abundance fold-change >4). The second category corresponded to other cell clusters (clusters without a specific enrichment trend).
However, these markers were located on different panels. CD32, CD64, and CD86 were present in the backbone, whereas TLR2 was present in panel #A, CD11b in panel #B, and CD83 in panel #C (Table  2 ). Therefore, the monocyte population represented a good opportunity to validate our results, as these cells expressed these six markers. Thus, we created a single mass cytometry panel of 25 markers that contained these six specific markers (Supplementary Table  2 ). Leukocytes from the original three HIV-infected patients (named PAT-1 to PAT-3), three new HIV-infected patients (named PAT-4 to PAT-6), and six new healthy donors (named HEA-4 to HEA-9) (Table  1 ) were stained using this panel (Supplementary Table  2 ). After the acquisition of the whole dataset, SPADE analysis was performed. The SPADE analysis was parameterized to obtain 100 clusters using 5% down-sampling, and the clustering based on the levels of CD1c, CD3, CD4, CD8, CD11c, CD14, CD16, CD19, CD32, CD64, CD66, CD86, CD123, CD141, HLADR, and Granzyme B.
HD-ZIP III and ZPR Phylogenetic Analysis Potential HD-ZIP III genes in soybean (G. max), were identified and their sequences determined using peptide sequences of A. thaliana HD-ZIPIII genes (PHB, PHV, REV, ATHB8 and ATHB15; www. arabidopsis. org) as query in BLASTp searches (using BLOSUM62 matrix and expected threshold value of -1) on the soybean genome ((v. 189); www. phytozome. net). The sequences were evaluated for the presence of leucine zipper and homeobox domains. Peptide sequences were aligned using a gap extension penalty of 0. 2 and the comparison matrix BLOSUM62 in Mega7 software  [40]  and a phylogenetic tree was constructed. To identify the soybean orthologs of Arabidopsis ZPR genes BLASTp search was performed as follows. Peptide sequences of AtZPR1, AtZPR2, AtZPR3 and AtZPR4 were obtained from www.
Staining, Microscopy and Image Analysis To evaluate xylem structure, roots were hand-sectioned transversally just above the youngest lateral root, and stained with a saturated solution of phloroglucinol prepared in 20% HCl. The stained sections were mounted immediately 20% HCl and imaged within 10 min of staining to avoid tissue damage. All the images were taken with a resolution of 4080 × 3072 pixels using an AX70 upright microscope, DP70 digital camera and cellSens software (Version 2. 1, Olympus, Shinjuku, Tokyo, Japan), and saved as tiff files. One section each from 12 independent transgenic roots were analyzed for each construct from at least 7 plants. No alterations or image enhancements were performed on the images before analysis. In each cross section imaged, xylem cell boundaries were manually drawn using ImageJ, and the same software was used to determine the xylem cell area and also to count the number of xylem cells  [45].
Statistical analysis was performed in Microsoft excel Student's t-test or using R version 3. 0 for the Shapiro-Wilk or Mann-Whitney-Wilcoxon tests. To assess nodule structure, mature nodules were hand-sectioned transversally to the nodule axis, and the sections were stained with a saturated solution of phloroglucinol prepared in 20% HCl. The stained sections were immediately mounted and imaged as described above for root sections. One section each from 10 independent transgenic nodules obtained from at least 7 different plants were analyzed for each construct. No alterations or image enhancements were performed on the images before analysis. The number of nodule vasculature branches were counted manually using the microscopic images. The cross-section area of the nodule and central infection zone were measured using imageJ software.
For statistical analysis, normality of data distribution was first evaluated using Shapario-wilk test in R (version 3. 0, R-Foundation for statistical computing, Vienna, Austria). Statistical significance of differences was evaluated using Student's t-test in Microsoft excel (normally distributed data) or Mann-Whitney-Wilcox test in R (not normally distributed). For epifluorescence microscopy to visualize promoter:tdTomato expression patterns, whole roots or root segments were mounted in 10% glycerol and imaged using AX70 upright microscope (Olympus, Shinjuku, Tokyo, Japan) using the TRITC/PI filter (exposure time: 25 ms). Images were taken at a resolution of 2040 × 1536 pixels and saved as tiff files. Mature nodules were sectioned transversally, sections were mounted in 10% glycerol, and imaged in an Olympus FV1200 scanning confocal microscope (Olympus, Shinjuku, Tokyo, Japan).
The 488 nm Ar laser (8% power) and default settings for GFP in FV10-ASW software were used for GFP imaging. The 543 nm HeNe laser (8% power) and default settings for DsRed in FV10-ASW software were used for tdTomato imaging, Images were taken at a resolution of 663 × 663 pixels, and a maximum intensity projection stack of 5 slices (5 µm total depth) were exported as a tiff file for each nodule section.
Gene Expression Analysis by RT-qPCR ZPR3dox and vector control (pCAMGFP-CsVMV:GUS), plants were generated using hairy root transformation and whole transgenic roots were harvested from uninoculated roots. The root tissues were immediately frozen in liquid nitrogen, and stored at -80 • C until further analysis. RNA isolation, reverse transcription, and qPCR assays for gene expression were performed as previously described  [25]. Gene expression levels were normalized to those of housekeeping genes GmACTIN, GmCONS7, and GmCONS15  [46]. Data shown are relative expression levels to that of GmACTIN, averaged from three biological replicates with three technical replicates each. Statistical significance of differences in gene expression was evaluated using Student's t-test, after verifying the data distribution using Shapiro-Wilk test in R software version 3.
Acknowledgments We thank  Denis Headon  for providing Edar dlJ mutant mice and  Irma Thesleff  for providing the Edar probe. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/ Inserm ,  ENS de Lyon, UCBL)  facilities, PLATIM and PBES, and warmly thank their respective staff for their constant help. We thank  Nicolas Gadot  and the  ANIPATH facility  for related work. We thank  Eric Tannier  for pointing out the analogy with palimpsests and Neal Anthwal for helpful comments on the manuscript.
In accordance with our observations in mouse models, analysis of human CRC samples showed no difference in UCP2 mRNA expression between stage II and stage III colon tumors and their normal mucosa counterparts (Figure  1G ). Analysis using the public Oncomine clinical cohort database (https://www. oncomine. com) also revealed no clear correlation in UCP2 mRNA expression between normal and tumor colorectal tissues (Table  S1 ). However, UCP2 protein expression was significantly increased in colon tumors from human CRC patients compared to their matched normal mucosa (Figure  1H ). These results show that UCP2 protein, not RNA, is significantly upregulated within the local intestinal tumor microenvironment, suggesting a potential role of the mitochondrial transporter UCP2 in the pathogenesis of CRC.
UCP2 Loss in Colon Tumors Affects Glycolysis with No Effect on Oxidative Metabolism and Mitochondrial Respiration Based on the transcriptomic analysis and the mitochondrial subcellular localization of UCP2, we determined whether the increased oxidative stress levels were associated with an enhanced mitochondrial oxidative metabolism. We previously (C) Pie chart of the biological processes associated with the upregulated genes after UCP2 loss in colon tumors. Biological categories were obtained using GO annotations from the PANTHER classification system. (D) GO analysis of the total number of differentially expressed genes involved in metabolic processes. (E) Gene function analysis using the software Ingenuity Pathway Analysis (IPA). Graph shows top molecular and cellular functions altered in Ucp2 À/À tumors compared to Ucp2 +/+ tumors.
Controversial studies about both the protecting and promoting roles of UCP2 in cancer have their bases in the ability of UCP2 to (A) Mitochondrial respiration rate was determined in the presence of 10 mM pyruvate, 5 mM malate, and 1. 4 mM ADP. The leak was measured after adding 1 mg/mL oligomycin (Ucp2 +/+ T n = 10, Ucp2 À/À T n = 9). (B) CS enzyme activity normalized to protein content. Data represent means ± SEMs (Ucp2 +/+ T n = 11, Ucp2 À/À T n = 7). (C) Scheme showing the 14 CO 2 -producing reactions from [U-14 C]-glucose (red) and [2-14 C]pyruvate (blue). (D) [U-14 C]-glucose (Ucp2 +/+ n = 11, Ucp2 À/À n = 12) and [2-14 C]-pyruvate (Ucp2 +/+ n = 7, Ucp2 À/À T n = 9) oxidation into 14 CO 2 of Ucp2 +/+ and Ucp2 À/À colon tumors (T). Data indicate means ± SEMs. (E) Immunoblots and quantifications of HK2 and LDHA expression using whole-cell extracts from colon tumors.
Following overnight hybridization, chips were washed on the fluidic station FS450 following specific protocols (Affymetrix) and scanned using the GCS 3000 7G scanner. The images were then analyzed with the Expression Console software (Affymetrix) to obtain raw data (CEL files) and metrics for Quality Controls. The observations of some of these metrics and the study of the distribution of raw data showed no outlier sample. CEL files were then normalized and processed to signal intensities using the RMA algorithm from the Bioconductor library and the cdf file V19 from BrainArray. All data for subsequent analysis were then log transformed (base 2) in Partek Genomics Suite. Unsupervised analysis and Anova were used to detect eventual outlier samples and to identify differentially expressed genes.
GO PANTHER classification system (http://pantherdb. org/) was used to categorize differentially expressed genes by biological processes. Gene networks and canonical pathways representing key genes were also identified using the curated Ingenuity Pathways Analysisâ (IPA) database. Fisher's exact test was performed to calculate a P value determining the probability that each biological function and/or disease assigned to the dataset was due to chance alone. The data discussed in this work have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE. Microarray analysis supporting these studies can be found at GEO: GSE104403.
ACKNOWLEDGMENTS We gratefully thank all of the members of the  Frederic Bouillaud and Anne Lombes team (Institut Cochin ) for helpful discussion and support of this project, and  Catherine Postic  for critical evaluation of the work. The authors gratefully acknowledge members from the  Institut Cochin Animal Facility , especially  Matthieu Be  ´nard, for excellent mouse care. We thank  Bertrand Duvillie  ´from  Cochin Institute (Scharfmann team ) for the acquisition and analysis of IF data and Ophelie Renoult for a-KG levels measurement ( CRCINA -INSERM U1232 ,  Universitede Nantes). We thank  Franck Letourneur  and  Jacques Sebastien  from the  Institut Cochin Genomic Platform  for microarray experiments and analysis, and  Maryline Favier  from the  Institut Cochin Histology Facility. E. A.
Design & stimuli We constructed 200 sentences in French, each containing 7 words without diacritics (see Table  1  for an example). Sentences were constructed so as to minimize semantic predictability within the sentence (i. e. , neutral sentences) and to have a simple grammatical structure (i. e. , no passive constructions, no relative clauses). The words had an average length of 5. 64 letters, and an average frequency of 6. 63 Zipf  (van Heuven, Mandera, Keuleers, & Brysbaert, 2014). Word frequencies were obtained using the film subtitle frequencies of the Lexique2 database  (New, Pallier, Brysbaert, & Ferrand, 2004). Between-word spacing (spaced vs. unspaced) and the order of words in the sentences (normal vs. shuffled order) were manipulated in a 2 × 2 factorial design. A Latin-Square was used with participants divided into four groups such that all sentences were tested in all four conditions across the four groups, but were seen only once per participant, with 50 sentences assigned to each condition per participant.
Analyses We used Linear Mixed Effects models (LMEs) to analyze our data, with items and participants as crossed random effects (including by-item and by-participant random intercepts  (Baayen, Davidson, & Bates, 2008) ). Items in these analyses were sentences or their shuffled version in the sentence-level analyses, and individual words (7 per sentence) for the local analyses. Generalized (logistic) LMEs were used to analyze error rates and fixation type probabilities (skips, regressions, refixations). The models were fitted with the lmer (for LMEs) and glmer (for GLMEs) functions from the lme4 package  (Bates, Maechler, Bolker, & Walker, 2014)  in the R statistical computing environment (R Core Team, 2014). We report regression coefficients (b), standard errors (SE) and t-values (for LMEs) or z-values (for GLMEs) for all factors.
Fixed effects were deemed reliable if |t| (or |z|) > 1. 96  (Baayen et al. , 2008). We used the spaced and correctly ordered sentence condition as reference. All durations were inverse-transformed (-1000/duration) prior to analysis. Data and script (R) are available on the Open Science Framework (OSF): https://osf. io/zufuje/?view_only= 86b20d7f02684bef9fd84981ed2d1345.
Acknowledgements This study was supported by grants  ANR-11-LABX-0036  and  ANR-15-CE33-0002-01  from the  French Agence Nationale de la Recherche (ANR) , and  European Research Council (ERC)  advanced grant  742141. We thank  Claire Dimier ,  Chloé Noblet  and  Fanny Broqua  for their help in running the experiment.
22G RNAs are thus triphosphorylated on their 5 0 ends  [20]. Another class of nematode RdRP products, the "26G RNAs", appears to bear a 5 0 monophosphate, and it is not clear whether they are matured from triphosphorylated precursors, or whether they are directly produced as monophosphorylated RNAs  [25] [26] [27]. The enzymatic activity of RNA-dependent RNA polymerization can be mediated by several unrelated protein families  [28]. Most of these families are specific to viruses (e. g. , PFAM ID #PF00680, PF04196 and PF00978). Viral RdRPs are involved in genome replication and transcription in RNA viruses, and they share common structural motifs  [29]. On the other hand, RdRPs involved in RNAi in plants, fungi and nematodes belong to a family named "eukaryotic RdRPs" (PFAM ID #PF05183). While viral RdRPs are conceivably frequently acquired by virus-mediated horizontal transfer, members of the eukaryotic RdRP family are thought to be inherited vertically only  [30].
Bioinformatic analyses of protein sequences Predicted animal proteome sequences were downloaded from the following databases: NCBI (ftp://ftp. ncbi. nlm. nih. gov/genomes/), VectorBase (https://www. vectorbase. org/download/), FlyBase (ftp://ftp. flybase. net/releases/FB2015_03/), JGI (ftp://ftp. jgi-psf. org/pub/JGI_data/), Ensembl (ftp://ftp. ensembl. org/pub/release-81/fasta/), WormBase (ftp://ftp. wormbase. org/ pub/wormbase/species/) and Uniprot (http://www. uniprot. org/). The predicted Branchiostoma lanceolatum proteome was obtained from the B. lanceolatum genome consortium. RdRP HMMer profiles were downloaded from PFAM v. 31. 0 (http://pfam. xfam. org/): 19 viral RdRP family profiles (PF00602, PF00603, PF00604, PF00680, PF00946, PF00972, PF00978, PF00998, PF02123, PF03035, PF03431, PF04196, PF04197, PF05788, PF05919, PF07925, PF08467, PF12426, PF17501) and 1 eukaryotic RdRP family profile (PF05183).
Phylogenetic tree reconstruction Amino acid sequences of the eukaryotic RdRP domain (Pfam #PF05183) were retrieved from PFAM  [35] , and supplemented with the RdRP domains of the proteins identified in the 538 animal proteomes (cf above). Sequences were aligned using hmmalign  [36]  using the HMM profile of the PF05183 RdRP domain. Sequences for which the domain was incomplete were deteled from the alignment. Sites used to reconstruct the phylogenetic tree were selected using trimAl  [37]  on the Phylemon 2. 0 webserver  [38]. Bayesian inference (BI) tree was inferred using MrBayes 3. 2. 6  [39] , with the model recommended by ProtTest 1. 4  [40]  under the Akaike information criterion (LG+Γ), at the CIPRES Science Gateway portal  [41]. Two independent runs were performed, each with 4 chains and one million generations.