dataesr/scientific-paragraphs-categorization huggingface dataset:
5 • C. For the summer period, the biogenic emission increase raises to 93 % and 92 % for isoprene and terpenes for a temperature increase of 6. 4 • C (Fig. 6 ). The correlation of determination between historic Figure  3. Observation-simulation comparisons for different schemes: the boxplots are presented for the data and the schemes for the annual averages, and the correlation of determination for each scheme is shown above each boxplot. The point on the boxplot shows the average value. Each point in the Taylor diagram shows one station, while each color shows one scheme (black -SOA2p, red -SOAvbs -and green -SOAmod). Table  1  shows statistical information for observed data and the three studied schemes. Table  1. Statistical information for different schemes in regard to measurements. In this table are the average for the data and each scheme, the bias, standard deviation and root-mean-square error (RMSE; all in µg m -3 ).
Acknowledgements. This work was performed using HPC resources from  GENCI-CCRT  (grant  2018-A0030107232 ). R. Vautard  is acknowledged for providing the WRF/IPSL-CM5-MR CORDEX simulations, and  D. Hauglustaine  and  S. Szopa  are acknowledged for providing t he INCA simulations. Z. Klimont is acknowledged for providing ECLIPSE-v4 emission projections. The thesis work of  Arineh Cholakian  is supported by  ADEME ,  INERIS  (with the support of the  French Ministry of Ecology) , and the  ANR SAF- MED  project. Giancarlo Ciarelli  was supported by  ADEME  and the  Swiss National Science Foundation  (grant no. P2EZP2_175166 ). Financial support. This research has received funding from the  French National Research Agency (ANR)  project  SAFMED  (grant  ANR-15 12-BS06-0013 ). This work is part of the  ChArMEx  project supported by  ADEME ,  CEA ,  CNRS-INSU  and  Météo-France  through the  multidisciplinary program MISTRALS (Mediterranean Integrated STudies at Regional And Local Scales ).
Phenotypic Analysis Stem circumference (SC, in cm) at 50 cm above ground level was measured to evaluate the growth of individual trees, where the average per plot was calculated. Growth traits were frequently measured only during the first 6 years, as height and SC are the main selection traits for rubber tree breeding  (Rao and Kole, 2016). Measurements were taken at four different ages and are listed in Supplementary Table  1. Two sets of measurements were taken each year: one set applied to trees under low-water conditions (LW), and the other applied to trees under wellwatered conditions (WW). These conditions were established according to the water distribution of each region in which the experiments were installed (Supplementary Figure  1 , Supplementary Table  2 ). Analyses of the SC traits were carried out via the breedR package  (Munõz and Sanchez, 2017)  in conjunction with the remlf90 function and method = "ai, " and the best linear unbiased predictors (BLUPs) of each genotype used with the following mixed linear model were taken: y Xb Zg e = + + where y is the adjusted mean phenotypic value (best linear unbiased estimated [BLUES]), × and Z are known incidence matrices, b is the vector of fixed effects (environmental effects), and g is the vector of random effects (genetic effects).
Genotypic Data and Single-Nucleotide Polymorphism Calling Genomic DNA was extracted according to the methods of  Souza et al. (2013)  and  Conson et al. (2018). Genotypingby-sequencing (GBS) library preparation and sequencing were performed as described by  Elshire et al. (2011). Genome complexity was reduced by digesting individual genomic DNA samples with EcoT22I, a methylation-sensitive restriction enzyme, and 96 samples were included in each sequencing lane. The resulting fragments from each sample were directly ligated to a pair of enzyme-specific adapters and combined into pools. PCR amplification was carried out to generate the GBS libraries. Library sequencing of GT1 × RRIM701 and GT1 × PB235 was performed on an Illumina GAIIx platform (Illumina Inc. , San Diego, CA, United States), and sequencing of PR255 × PB217 was performed on the Illumina HiSeq platform.
The raw data were processed, and single-nucleotide polymorphism (SNP) calling was performed via TASSEL 5. 0  (Glaubitz et al. , 2014). Initially, the FASTQ files were demultiplexed according to their assigned barcodes. The reads from each sample were trimmed, and the tags were identified by the following parameters: Kmer length of 64 bp, minimum quality score within the barcode and read length of 20, minimum Kmer length of 20 and minimum count of reads for a tag of 6. The retained tags with a minimum count of six reads were aligned to the H. brasiliensis reference genome sequence  (Tang et al. , 2016)  via Bowtie 2 version 2. 1  (Langmead and Salzberg, 2012) , with the very sensitive option enabled. SNP calling was performed via the TASSEL 5 GBSv2 pipeline  (Glaubitz et al. , 2014)  and filtered with snpReady software  (Granato and Fritsche-Neto, 2018).
The following criteria were used: 20% missing data, minor allele frequency (MAF) greater than or equal to 5% (MAF of 0. 05), and removal of individuals with more than 50% (sweep. sample = 0. 5) missing data for the called SNPs. Only biallelic SNPs were maintained, which was performed via VCFtools  (Danecek et al. , 2011). After the data were filtered, the missing data were imputed by the knni method with snpReady software  (Granato and Fritsche-Neto, 2018). The genotypic data are available under NCBI accession PRJNA540286 (ID: 5440286) (GT1 × PB235 and GT1 × RRIM701) and accession PRJNA541308 (ID: 541308) (PR255 × PB217).
Genomic Selection Analysis Phenotypic analysis was carried out jointly for all years of evaluation via the mixed model approach. Prediction based on genomic relationships and predictive ability assessment was performed via a relationship matrix-based approach for genomic prediction  (Habier et al. , 2007) ; the matrix K was the central object denoting the genomic relationship matrix. Two kernel methods were used: the linear kernel method (GB) used by  Jarquin et al. (2014)  and  Lopez-Cruz et al. (2015)  and the nonlinear kernel method (GK) proposed by  Cuevas et al. (2016). The matrix for the GB  (VanRaden, 2008)  and GK  (Gonzalez-Camacho et al. , 2012)  methods was obtained via the function G. matrix in snpReady software  (Granato and Fritsche-Neto, 2018). Statistical models for genomic predictions taking G×E interactions into account  (Jarquin et al.
, 2014) , MDs  (Jarquin et al. , 2014), and MDe (Lopez-Cruz et al. , 2015)  and fitted with the GB and GK methods; this was applied to all the data sets for all the studied traits. These analyses were performed to derive estimates of variance components resulting from the main genetic effect, and genetic environment-specific effects and residual effects of the four models described above for SC in the data sets from the two different conditions (LW and WW) were computed. For all GS models, BLUPs were estimated via the mixed model with breedR software  (Munõz and Sanchez, 2017) , and all models were fitted with G×E interactions via BGGE software  (Granato et al. , 2018) , in which 20,000 iterations were performed (ite = 20,000), 5,000 samples were discarded (burn-in = 5,000), and every fifth iteration was used to estimate the posterior mean (thin = 5).
The random CV 2 design (CV2) is a simulation of genotypes that has been evaluated in some environments but not in others. The CV2 design can be used only for multienvironment modeling methods (MM, MDs, and MDe) and not for single-environment (SM) modeling methods where the random CV is CV1. The parameters of the models, which include the main genetic effects, variance components resulting from residual effects, G×E interaction effects, and environment-specific effects, were reestimated from the TRN data in each TRN-TST partition (50 random), and the models were fitted to the TRN data set. PA was assessed by computing Pearson's productmoment correlations between predictions and phenotypes in the TST data set within environments.
Single-Nucleotide Polymorphisms Calling We started with 435 genotypes, but three genotypes were replicates and thus were merged. We removed 27 individuals that had more than 50% missing SNPs, leaving 411 genotypes. After the data were analyzed, a total of 259. 224 million reads of sequence data were obtained, of which 69. 8% were high-quality barcoded reads. The overall alignment rate of these reads to the rubber tree reference genome  (Tang et al. , 2016)  was 83. 7%, and 23. 1% were aligned exactly one time. A total of 107,294 SNPs were identified. After markers 1) with more than 20% missing data, 2) with an MAF ≤ 0. 05, 3) with more than two alleles were excluded, tags with a minimum depth of six reads were aligned to the H. brasiliensis reference genome sequence  (Tang et al. , 2016). This method was based on that of previous studies of other species, in which the authors argued that, compared with high-depth sequencing, low-depth (approximately 2-4X) sequencing enables more individuals to be genotyped for the same cost, which, according to  Li et al.
Descriptive Statistics Box plots of SC in each environment are depicted in Supplementary Figure  4. The distribution of this trait in the environments was symmetrical (data not shown). The LW environment exhibited relatively high increases in SC, while the WW environment exhibited relatively low increases (Supplementary Figure  4 ). The trees grew better under increased water availability (WW) (data not shown); however, because the phenotypic measurements were taken twice per year for each tree, the phenotypes of the trees under WW were always measured at the beginning of the year, whereas the phenotypes of the trees under the LW were taken half a year later. This method inevitably generates a small difference between the two phenotypes because the trees under LW are older than those under WW when the same measurements are taken.
FUNDING The authors gratefully acknowledge the Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP) for a Ph. D. fellowship to FF (18/18985-7); the Coordenação de Aperfeiçoamento do Pessoal de Nível Superior (CAPES) for financial support (Computational Biology Program and CAPES-Agropolis Program) and postdoctoral fellowships to LS (88887. 334728/2019-00); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for financial support, a postdoctoral fellowship to LS (168028/2017-4), and research fellowships to AS and PG.
The H3S10ph signal on chromosomes was normalized against the DAPI levels after background deduction and analyzed using ImageJ software. All graphs and data statistical analysis were done using GraphPad Prism 5. Each data set was expressed as the mean±s. e. m. and P-values were determined by t-test. P-values are shown by means of asterisks as follows: n. s. , not significant; P>0. 05; *P≤0. 05; **P≤0. 01; ***P≤0. 001; ****P≤0. 0001.
Cytochalasin B was used at 40 µM, Blebbistatin at 50 µM and Leptomicin B at 130 nM. For fluorescence live-cell imaging, INCENP-knockout cells and mutant rescue cells stably expressing H2B-RFP were transfected with a targeting construct in order to insert GFP downstream of the CENP-H gene by homologous recombination as previously described  (Vagnarelli et al. , 2006). The CENP-H-GFP knock-in construct was provided by Tatsuo Fukagawa (Graduate School of Frontier Biosciences, Osaka, Japan). Fluorescence images were collected on an Olympus IX-70 microscope controlled by Delta Vision SoftWorx (Applied Precision) and a 100× objective (NA 1. 4) every 2 min prior to anaphase onset then, threedimensional data sets were collected every minute onwards. Movie frames were processed using Delta Vision SoftWorx software (Applied Precision).
MS data analysis Raw data files were processed using MaxQuant version 1. 6. 2. 6  (Cox and Mann, 2008) , which incorporates the Andromeda search engine  (Cox et al. , 2011). The spectra were searched against a human FASTA database (accessed June 2018) containing all reviewed entries in the reference UniProt Human Proteome. The processed output was then analyzed using R or RStudio software. TMT reporter ion intensities were corrected for mixing using total ion intensities measured for unmodified peptides. The Andromeda score for this match is 126.
Funding This work was funded by  Wellcome Trust , of which W. C. E. is a  Principal Research Fellow  (grant number  073915 ), and by  'la Ligue contre le Cancer, Grand-Ouest ' comity (districts:  29 ,  22 ,  56 ,  35  and  79 ), the  Fondation ARC pour la Recherche sur le Cancer  and  Reǵion Bretagne  of which S. R. was a recipient. T. L. is supported by a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  ( 206211/Z/17/Z ). Deposited in PMC for immediate release.
To have a comprehensive view of the transcriptional and epigenomics effects of the PMA/ionomycin treatment, we performed RNA-seq and ChIP-seq (for H3K4me3 and H3K27ac) experiments on mock (DMSO) and PMA/ionomycin-treated P5424 cells. Analysis of the RNA-seq for de novo discovery of lncRNAs identified 7098 transcripts corresponding to 6487 de novo lncRNA genes (Supplementary Dataset 1). As expected, most de novo lncRNAs were T-cell specifics (Supplementary Fig. 2A ). The PMA/ionomycin treatment led to 799 induced and 433 repressed coding genes, as well as 172 induced and 163 repressed lncRNAs (including 148 and 152 de novo lncRNAs, respectively) (adjusted p-value < 0. 01; fold change > 2; Supplementary Dataset 2; Fig. 1A ). However, we did not observe substantial changes in the level of histone modifications at promoters of differentially regulated genes (data not shown).
Effect of the PMA/ionomycin treatment on P5424 gene expression. (A) Heatmap showing the normalized RNA-seq signal of differentially expressed coding and lncRNA genes in 3 controls (DMSO) and 3 PMA/ionomycin-treated (PMA/Iono) P5424 cells. (B) Scatter plot highlighting upregulated and downregulated coding genes (red) and lncRNAs (blue) in response to PMA/ionomycin stimulation on P5424 cells. (C) The top 10 biological processes identified by functional enrichment analysis (g:Profiler) of coding genes that were affected by stimulation with PMA/ionomycin. defined as lncRNA/mRNA pairs (Supplementary Dataset 3). We observed that pairs of lncRNA/mRNA separated by less than 500 kb were similarly co-regulated (Fig. 2B, C ). Moreover, the lower the distance were between the two genes, the higher was the frequency of concordant changes (Fig.
Validation of correlated lncRNA/mRNA pairs. The above observations pointed us to look more in detail for pairs of coregulated lncRNA/mRNA loci that might be involved in T-cell functions (Supplementary Dataset 3). We used H3K4me3 and H3K27ac ChIP-seq data to better map the 5′ regulatory regions of the lncRNA candidates. Visual inspection of the PMA/ionomycin-regulated loci led to the selection of 5 co-induced and 3 co-repressed lncRNA/coding gene pairs showing correlated expression and histone modification dynamics (Figs 3A, 4A, respectively). RT-PCR assessment of the selected lncRNAs retrieved the expected results (Figs 3B, 4B; Supplementary Fig. 3C  and data not shown). Subsequently, we validated the co-regulation of the lncRNA/mRNA pairs in PMA/ionomycin-and DMSO-treated P5424 cells by RT-qPCR (Fig.
5C ). Among the well correlated pairs, we found that the XLOC_000895/Bcl2 pair displayed a highly similar kinetic of activation, hence strongly supporting a functional cis-regulatory link. Bcl2 is a major regulator of cell death (apoptosis) and is involved in T-cell mediated immune response, as well as in oncogenic transformation of lymphoid lineages  [48] [49] [50] [51]. The XLOC_000895/Bcl2 locus was expressed at low levels in developing thymocytes as observed with our or published RNA-seq datasets (data not shown and Supplementary Fig. 4 ). As Bcl2 is required for normal T-cell differentiation and maturation  52  , we hypothesized that this locus might be expressed at high level in a small population of thymocytes or at later T-cell maturation stages. Consistent with this, Bcl2 has been shown to be up-regulated in DP thymocytes treated with PMA/ionomycin, likely reflecting cell survival during positive selection of DP cells  53.
To explore whether the XLOC_000895/Bcl2 pairs is expressed at later stages of T-cell maturation or during T-cell activation, we reanalyzed a published RNA-seq dataset covering most stages of thymic and peripheral T-cell differentiation  18. Interestingly, we observed that expression of the XLOC_000895 and Bcl2 genes were well correlated throughout T-cell differentiation (Supplementary Fig. 4A, B ), with the highest expression of both genes in differentiated Th2 cell populations (Supplementary Fig. 4C ). This suggests a potential role of this lncRNA locus in the regulation of Bcl2 expression during T-cell maturation. The Bcl2 gene is often up-regulated in T-cell leukemia and lymphoma  [54] [55] [56]. A recent study using a mouse model of T acute lymphoblastic leukemia (T-ALL) identified Bcl2 as a direct target of TLX1 and NUP214-ABL1 oncogenes when both oncogenes were expressed in the same cells and suggested to be a potential therapeutic target in this context  47.
New analyses of this data demonstrated that the XLOC_000895 lncRNA was also induced in TLX1/NUP214-ABL1 expressing thymocytes, similarly to Bcl2 (Supplementary Fig. 5 ). Moreover, we observed an increase of H3K4 methylation at the promoter of XLOC_000895 along with binding of TLX1 and STAT5 (a direct target of NUP214-ABL1 fusion protein) as well as p300 and BRD4 co-factors in TLX1/NUP214-ABL1 positive thymocytes (Supplementary Fig. 5E ). These observations strongly suggested that XLOC_000895 is a direct target in T-ALL and a potential regulator of Bcl2. XLOC_000895 locus regulates the expression of Bcl2 in P5424 cells. We named the XLOC_000895 lncRNA Robnr (Regulator of Bcl2 non-coding RNA). The Robnr lncRNA has 3 predicted isoforms sharing a single transcription start site and 8 exons (Supplementary Dataset 1).
RNA-seq. Poly(A) RNA was isolated from three replicates of P5424 cells treated with DMSO or PMA/ionomycin and was used for the RNA-seq library preparation, using the TruSeq RNA Library Prep Kit v2 (Illumina). Libraries were paired-end sequenced on the Illumina NextSeq 500 sequencer. Reads were aligned using STAR aligner (v2. 4. 2a) with arguments "outFilterMismatchNoverLmax" and "outFilterMultimapNmax" set to 0. 08 and 1, respectively. Transcripts discovery was performed using Cufflinks (v2. 2. 1) with the "library-type" argument set to fr-firstrand, and a GTF file obtained from GENCODE ("Comprehensive gene annotation", vM1) provided as the genomic annotation. The GTF files produced for each sample by Cufflinks were combined using Cuffmerge. The "class code" assigned to each transcript by Cuffmerge was used to defined unknown transcripts (class code "u").
Only de novo transcripts with counts greater than 0 in at least one RNA-seq sample were kept for subsequent analyses (Supplementary Dataset 1). These de novo transcripts were combined with the GENCODE GTF file to produce the final genomic annotation that was provided to FeatureCounts (v1. 4. 6-p4) for quantification. Differential gene expression was performed using DESeq2 (v1. 6. 3). To create bigwig files, reads from Watson and Crick strands were selected using SAMtools (v0. 1. 9) and provided to the bam2wig. py script from the RSeQC program suite (v2. 6. 4) (Supplementary Dataset 2). Public RNA-seq data from Hu et al. 88  and Vanden Bempt et al. 47  were downloaded from the GEO database (accession numbers GSE48138 and GSE102209, respectively) and processed as described above. RNA-seq profiles were visualized using the IGV genome browser  89.
Tissue specificity. To estimate the bias toward tissue-specificity for each transcript, we processed a set of fastq files obtained from the SRA database (accession number SRP012040). This dataset corresponds to 30 mouse tissues sequenced with a paired-end strategy on an Illumina HiSeq2000  90. After read mapping, gene expression levels were quantified using Cuffdiff  91. Based on the log2-transformed FPKM values, we assessed the tissue-specificity for each gene across every tissue using the tau score, as described by Kryuchkova-Mostacci and Robinson-Rechavi  92. The tau score formula is: ∑ = - - = = ≤ ≤   Tau x n x x max x (1 ) 1 ; ( ) i n i i i i n i 0 1 where: n corresponds to the number of samples, X i to the expression level in condition i, and Max(x i ) to the maximum expression level through all tissues.
Genomic pairing of lncRNA and mRNA. For each differentially expressed lncRNA we identified the closest differentially expressed mRNA (hereafter lncRNA/mRNA pairs) and computed the distance between the two TSSs. Supplementary Dataset 3 provides the list of co-regulated lncRNA/mRNA pairs. All plots (bar and scatter) were done using the ggplot2 R package  93. Functional enrichment analysis. Gene ontology (GO) enrichment of biological processes was assessed on PMA/ionomycin-regulated coding genes using the g:Profiler web server  94. The top 10 GO terms enrichments having the lowest P value were used to select the annotations (Fig. 1C ). Functional enrichment of biological processes associated with coding genes surrounding PMA/ionomycin-regulated lncRNAs was performed using the online tool GREAT (Genomic Regions Enrichment of Annotations Tool)  46  and default setting.
The samples were then incubated overnight at 65 °C. The next day, the DNA was purified with the QIAquick PCR Purification Kit (Qiagen) and eluted in 20 µl of water. Quantitative real-time PCR with SYBR green was used to quantify the immunoprecipitated DNA. The primers used are listed in Supplementary Table  1. Data represent the percentage of input normalized to the Actb promoter. ChIP-seq. ChIP-seq libraries for H3K4me3 and H3K27ac in P5424 cells treated with DMSO or PMA/ionomycin were generated with the MicroPlex Library Preparation Kit (Diagenode), according to the manufacturer instructions. The libraries were sequenced in paired-end 75/75nt mode using the NextSeq ® 500/550 (Illumina), according to manufacturer's instructions. Reads were mapped to the mm9 reference genome using standard procedures.
Acknowledgements We thank the  Transcriptomics and Genomics Marseille-Luminy (TGML)  platform for sequencing the RNA and ChIP samples and the Marseille-Luminy cell biology platform for the management of cell culture. We thank students from the  Polytech Marseille (Génie Biologique 4  A; promotion 2015) for their technical help in the context of a practical session funded by  CoReBio PACA. Work in the laboratory of S. S. was supported by recurrent fundings from  INSERM  and  Aix-Marseille University  and by the  Foundation for Cancer Research ARC  ( ARC PJA 20151203149 ) and  A*MIDEX  ( ANR-11-IDEX-0001-02 ), Plan Cancer 2015 ( C15076AS ) and 2018 (THYNCRNA), and  Equipe Labellisée Ligue contre le Cancer. W. S. and Y. K. were supported by the  Franco-Algerian consortium Hubert Curien (PHC) Tassili  ( 15MDU935 ).
The specific integrin clusters were imaged with a 40× oil immersion objective (Leica HCX PL APO, Leica Camera AG, Germany) and a zoom factor of 2 in a confocal microscope (Leica TCS SP5 X, Leica Microsystems, Germany) equipped with a mercury metal halide bulb (Leica Camera AG, Germany). 2. 7. Data Analysis and Statistics. All images were analyzed with ImageJ 51 and the resulting data were processed with Origin (OriginLab). Cell migration trajectories, from a total of 60 cells per experiment from three independent experiments, were manually tracked with an ImageJ plugin, MTrackJ,  52  where the cell migration speed was calculated. Cell morphological parameters were calculated for at least 166 manually marked cells from at least three independent experiments for each experimental condition. The total area of αvβ3 and α5β1 FAs of 30 cells in total from 2 different experiments was automatically analyzed with ImageJ.