dataesr/scientific-paragraphs-categorization huggingface dataset:
Methylation profiling DNA was extracted from tumors and genome-wide DNA methylation analysis was performed using either the Illumina HumanMethylation450 BeadChip (450 k) or EPIC arrays. DNA methylation analysis was performed with custom approaches as previously described  [12, 23]. DNA methylation profiles from 50 K27M pHGG were compared to defined supratentorial tumor subgroups, i. e. G34R-H3. 3 mutated (n = 10), MYCN (n = 10) and PDGFRA/pedRTK1 (n = 10) subgroup tumors. For t-SNE analysis (t-Distributed Stochastic Neighbor Embedding, Rtsne package version 0. 11), 428,230 uniquely mapping autosomal probes in common between the 450 k and EPIC arrays were used. The input for the t-SNE calculation is 1-Pearson correlation, weighted by variance. Clustering analyses were performed using the beta values of the top 10,000 most variably methylated probes by standard deviation.
PCA and t-SNE analysis were performed on the 250 genes associated with the highest variance to keep the same proportion of genes selected with the microarray analysis. All samples were projected on the two first principal components computed with rlog transformation of the counts of the 120 genes with the highest standard deviation. Using One hundred and nineteen high-grade glioma samples were divided in four groups according to their location either in the pontine (DIPG), cortical or thalamic area of the brain as well as the non-thalamic midline, i. e. spinal cord, cerebellum or peduncle tumors classified as 'non-thalamic midlines' Rtsne package (v 0. 11), we applied t-SNE on the same data matrix with the Pearson correlation as a distance and the following parameters: theta = 0, perplexity = min(floor((ncol(rlog_VariableGenes)-1)/3), 30), check_duplicates = FALSE, pca = FALSE, max_iter = 10,000, verbose = TRUE, is_distance = TRUE.
v19. chr_patch_-hapl_scaff_annotation. gtf ). PCA for all samples were generated after scaling to unit variance using the PCA function from the FactoMineR package (v1. 41) and plotted using Factoextra (v1. 0. 5). Merging of the 3 biological replicates of H3. 1-or H3. 3-K27M subgroups was performed using bigWigMerge tool (UCSC kent utils, http://hgdownload. soe. ucsc. edu/admin/ exe/macOSX. x86_64/). Heatmaps of H3K27me3 ChIPseq enrichment across genomic loci were calculated using deepTools version 1. 5. 11. ComputeMatrix was used with regions of either +/-5 kb or +/-10 kb around the center of the genomic intervals for 'bound regions' or TSS for differentially expressed genes, respectively. Heatmaps were plotted with or without k-means (k = 5) and their average profiles of ChIP-Seq enrichment in the same -10/+ 10 kb genomic intervals were also generated for each k-means group.
Results Histone H3 K27M midline pHGG and K27M DIPG display similar gene expression profiles and survival but differ significantly from other high-grade gliomas We conducted microarray gene expression profiling of the single center cohort from Necker Enfants Malades hospital of 119 pHGG with histone H3 genotype previously determined either by Whole Genome Sequencing  [25]  or targeted Sanger sequencing  [2]  (Table  1  and Additional file 1: Table  S1 ). In total, 131 distinct gene expression microarrays were hybridized as 12 samples were analyzed twice to control for potential batch effects. The 12 duplicated samples were located in close proximity on PCA plot when projected on the 2 first principal components, confirming the appropriate removal of a batch effect in our dataset (data not shown).
1a ). Considering the mutational status of histone H3 genes, the results clearly showed that H3-K27M tumors, whichever their pontine or thalamic location, could be separated from the wild-type and G34R/V tumors on the first principal component (Fig. 1b ). Indeed, all H3-K27M mutated thalamic and spinal tumors were close to DIPG samples, whereas histone H3 wild-type thalamic tumors are distributed on the right side of the plot among histone H3 wild-type non-thalamic midline and cortical tumors. Interestingly, 5 DIPG without any mutation in H3F3A, HIST1H3B/C and HIST2H3A/C were located within the K27M DIPG subgroup. These tumors all showed H3K27-trimethylation loss by immunohistochemistry (Additional file 3: Figure  S1A ). Unsupervised K-means analysis was performed on the same dataset (using k = 2 which showed the best BIC value) and led to a similar conclusion as the k-mean group 1 corresponded to H3-K27M and H3-wild type samples presenting H3K27-trimethylation loss whereas k-mean group 2 contained all other pHGG tumors (Additional file 3: Figure  S1B ).
Additionally, this GE dataset was analyzed in parallel with another dimension reduction and visualization technique for high-dimensional data, t-SNE, as it was shown to be more robust than PCA with respect to outliers. Moreover, t-SNE has also been frequently used for pediatric brain tumor classification based on DNA methylation profiles  [23]. This analysis of GE profiles led to similar observations, re-iterating the similarity of H3-K27M thalamic midline and DIPG (Additional file 3: Figure  S1C-D ). Histone H3-G34R/V samples were tightly clustered together in particular in this analysis, thus reflecting a strong similarity in their gene expression profiles.
PCA analysis of GE measurements by microarrays did not clearly discriminate H3. 1 and H3. 3 mutated tumor samples (Fig. 1b ). Indeed, even if H3. 1-K27M DIPG were closer to each other in the 2-dimensional PCA plot, they are surrounded by H3. 3-K27M samples. However, as microarray data were generated in several batches, we could not exclude that this could obscure the dataset, despite the use of a batch correction method. Therefore, we took advantage of a RNA-seq study of 21 new H3-K27M DIPGs samples which appeared more suitable as it provides an exhaustive measurement of transcriptome in contrast to microarray analysis. Grouping of tumors based on their RNA-seq expression profiles in either t-SNE or PCA classifications confirmed the discrimination of H3. 1-K27M from H3. 3-K27M tumors observed in our DNA methylation study (Fig.
1-K27M tumors, as expected given that they are both canonical histone H3 with identical role in the cell  [24]. Yet, the similarity of H3. 1 and H3. 2 mutated tumors should be confirmed with additional H3. 2 mutated samples from other cohorts, as only two were reported in the literature  [2, 18]. Histone H3. 1 and H3. 3-K27M tumors were also discriminated by RNAseq transcriptome profiling, supporting their intrinsic divergence. This could support the recently reported superiority of RNAseq over expression microarrays for tumor classification purposes  [28]. MacKay and coll. did not report this distinction between H3. 1 and H3. 3 mutated tumors using DNA methylation profiling. This difference might result from a 10 times smaller proportion of H3. 1 mutated samples analyzed (8 out of 441 samples) hiding out the variability brought by these tumors in their huge dataset.
Acknowledgements DC, CP, TK, JM, EB, JG, MAD acknowledge financial support from  Etoile de Martin  and Carrefour through the campaign " Les Boucles du Coeur ", DC from  Cancéropôle Île-de-France  " Émergence 2018 ", and  SP from Association pour la Recherche en Neurochirurgie Pédiatrique. CJ acknowledges NHS  funding to the  NIHR Biomedical Research Centre at The Royal Marsden Hospital  and the  ICR.
RNA-seq library was prepared from 1. 5 μg of total RNA using TruSeq Stranded mRNA Library Prep Kit in paired end format (Illumina) following the manufacturer's recommendations. RNA sequencing was carried out on Illumina HiSeq2500 platform (Illumina) at IGA technology service (Istituto Genomica Applicata, Udine, Italy). RNA-seq raw and processed data are deposited in the Array Express archive under the accession number E-MTAB-5157.
Bioinformatics analysis Read quality assessment and trimming were obtained by means of fastQC (version 0. 11. 3) and Trimmomatic (v. 0. 33)  [18] , respectively. First 15 bases from the start of the reads were removed by the headcrop function of Trimmomatic tool. Then, raw reads were aligned to the human genome version hg19 with TopHat  [19]  (version 2. 1. 0) changing only the mate inner distance and its S. D. to 250 and 300, respectively, respect to default parameters. Read counts for UCSC annotated genes were calculated using the htseq-count function of HTSeq tool  [20]  (version 0. 6. 0) with the mode option set as union (default) for ambiguously-mapped reads. Normalization was carried out with edgeR package  [21]  (version 2. 12. 0,) in R-3. 2. 2. Raw counts were normalized according to library size to obtain counts per millions (cpm) and only genes with a cpm ≥1 in at least three samples were retained for subsequent analyses.
Differentially expressed genes (DEGs) at each time point between treated and untreated samples were identified using a paired design with the likelihood ratio tests of the glmLRT function in edgeR package and considered significant if the p-value was ≤0. 05 and the absolute Fold Change (FC) ≥1. 2. DEGs were annotated with the Topp-Gene suite (https://toppgene. cchmc. org/) [22]  and prioritized by literature mining using PubMatrix  [23]. Enrichment analysis was performed using the preranked tool of Gene Set Enrichment Analysis  [24]  (GSEA, version 2. 2. 1,) and the gene sets of the Molecular Signature Database Hallmarks collection (i. e. , 50 gene sets representing well-defined biological processes; MSigDB http://software. broadinstitute. org/gsea/msigdb). Specifically, genes were ranked separately for each time point using the fold change value between treated and untreated samples.
Gene sets smaller than 15 or larger than 500 genes were excluded; gene sets passing this filter were considered significant if the False Discovery Rate (FDR) was ≤0. 05 when using 1000 permutations of gene sets. To test DEGs enrichment for diseases signatures we used the ToppGene tool (https://toppgene. cchmc. org/) with the DisGeNET database (http://www. disgenet. org/), considering significant only the diseases with FDR corrected p-value ≤0. 05. Groups of genes with a differential temporal expression profile in exposed cells (as compared to controls) were identified using the next-maSigPro method of the maSigPro  [25, 26]  R package (version 1. 34. 1). Briefly, the p. vector function was used to compute a regression fit for each gene in both matrixes of cpm counts for BEAS-2S exposed to diesel and biomass UFP separately.
The gene network was created in Cytoscape  [27]  (version 3. 3) using the Reactome FI plugin  [28]  (version 5. 0). For diesel UFP, 129 genes selected using the above mentioned criteria were inputted as a list to the Reactome FI, which reconstructed gene-gene interactions using the pathway-based Reactome Functional Interaction (FI) database (version 2015). The Reactome Functional Interaction (FI) database contains both manually annotated and predicted gene-gene relationships  [29]. Since genes modulation could have been induced at protein level only, we used the linker genes option to add putative linker genes in the network. Linker genes are automatically selected by the plugin algorithm among the genes that connect the higher number of genes of the input list according to the Reactome FI database annotation.
The fold change was calculated for each condition using the 2 -ΔΔCt method comparing ΔCt of UFP treated cells to ΔCt of control untreated cells. To validate results at the protein level, the expression of four secreted proteins (i. e. , IL-6, VEGF, EREG, and IL-24) has been quantified with the ELISA assay (IL-6 and VEGF: Life Technologies; EREG and IL-24: Elabscience) according to the manufacturer's guidelines. The absorbance of each sample was measured using a Multiplate Reader Ascent (Thermo Fisher Scientific) at 450 nm and 630 nm and data analyzed with Ascent Software. Data are reported in pg/ml as mean and standard error of mean (SEM) of three independent experiments. Statistical analyses were performed in Sigma Stat 3. 1, using one-way ANOVA with Dunnett's or Dunn's post hoc test and p-value< 0.
Despite some small differences in the three biological replicates, expression data of DEGs were able to clearly partition exposed cells from controls (Fig. 1c ). To validate relevance of identified signatures in lung diseases, we compared DEGs to publicly available datasets. We limited the analysis to genes most constantly modulated during time, e. g. in at least 3 out of 5 time points, for a total of 53 and 3 genes for diesel and biomass, respectively. By ToppGene enrichment analysis with the DisGeNET database, we observed a significant involvement to several cancer-related disorders for DEGs induced by diesel UFP: Squamous Cell Carcinoma (FDR p-value: 4. 791E-7), Chemical Carcinogenesis (FDR p-value: 6. 405E-5), Squamous cell carcinoma of the head and neck (FDR pvalue: 6. 405E-5) and Lung Adenocarcinoma (FDR p-value: 8.
Exposure to diesel and biomass UFP modulates transcriptional dynamics in BEAS-2B cells To capture variations in the transcriptional dynamics after exposure to diesel and biomass UFP, we identified genes showing statistically significant expression changes over time using the next-maSigPro method of the maSig-Pro R package  [24, 25]. maSigPro revealed profound temporal changes in the expression of 120 and 96 genes in BEAS-2B cells exposed to diesel and biomass UFP, respectively, at a 95% confidence level (Additional file 11: Table  S7 ). Using hierarchical clustering and maSigPro default parameters, significant genes were grouped into nine clusters showing distinct expression profiles during the time of the experiment (Fig. 3  and Additional file 12: Figure  S5  and Additional file 11: Table  S7 ).
75 between expression levels from RNA-seq and qPCR; Table  2 ). Alternatively to qPCR validation, to support the evidences of gene expression profiling and bioinformatics analyses, we quantified the release of four soluble factors, involved in the inflammatory signaling, cell proliferation and regulation of angiogenesis, in cells supernatants. In particular, we evaluated with ELISA assay the protein levels of IL-6, IL-24, EREG, and VEGFA in the protein lysate of BEAS-2B cells exposed to diesel and biomass UFP. As compared to cells exposed to the sole medium, cells exposed to UFP from diesel significantly increased the release of interleukins, IL-6 and MDA-7/ IL24, and of EREG and VEGF (Table  3 ). On the contrary, biomass UFP did not induce any significant modulation of the analyzed proteins (data not shown).
Discussion In our study we designed and adopted a time course experiment to investigate, through RNA-seq, the transcriptional changes induced in human bronchial epithelial cells by the exposure to UFP from diesel and biomass combustion. The bioinformatics analysis of transcriptional data reveals that the two different UFP induce, since the earliest time points, different transcriptional   S9 ). Of the 129 resulting DEGs, network analysis identified a biological interaction for 122 genes (rounded shape) and 42 further "linker" genes (diamond shape). Some of the most interesting linker genes are investigated in the Discussion section (red board). Grey lines indicate manually curated interactions according to Reactome FI database. Size of DEGs nodes is directly proportional to the number of differentially expressed time points; the FC at each time point in cells exposed to UFP from diesel vs control is also indicated.
Ion PI chips V3 were sequenced on the Ion Proton, using Ion PI Hi-Q sequencing kit. The sequences were aligned against the human reference sequence (GRCh37/hg19) using Torrent Suite Software 5. 0. 4. Variant calling was then performed using variant caller version 5. 0. 4. 0. Annotation and filtering of the variants were accomplished by submitted them to Ion Reporter Software version 5. 2. Filtering was carried out by applying a series of steps: variants with a minor allele frequency (MAF) < 1% in the 1000 genomes project or in the 5000 exomes european-american (NHLBI ESP) were kept. We focused on predicted missense, frame-shift, stop-gain or stop-loss, and splice-site variants. For remained variants in the final list, we also checked the prediction score in Polyphen 2 (http://genetics. bwh.
Variants of uncertain significance (VUS) We also identified VUS in 7 patients. One male adult patient (N°66) carried a novel hemizygous variant (c. 893G > A; p. Arg298Gln) in the AIFM1 gene, localized on     4 ). Since the age of 60, the patient had CPEO associated with dysphagia but without any other associated symptom (Table  1 ). His mother presented exactly the same clinical signs with same onset but she was not available for testing. At the age of 69, muscle biopsy revealed COX-negative fibers with a complex IV deficiency by spectrophotometry. Blue native PAGE analysis revealed an assembly defect or increased instability of complex IV. The c. 893G > A variant is not frequent in ExAC database (< 0. 01%) (5/87658 tested alleles including 2 in a hemizygous state in male individuals). It affects a highly-conserved residue in the NADH-binding domain and is predicted as probably pathogenic.
1248A > T; p. Lys416Asn, and c. 2354A > G; p. Glu785Gly, respectively, are not found in ExAC databases and in silico analysis predicts them to be probably pathogenic (Table  4 ). The corresponding amino acids are located in the coiled-coil domain and are conserved over species. Mutations in KIF5A are described in a wide clinical spectrum from hereditary spastic paraplegia (HSP) 10 to axonal neuropathy  [10]  and was recently implied in early-onset phenotype with severe myoclonus and evidence of mitochondrial dysfunction  [11]. We also identified a novel heterozygous variant, c. 4682G > A; p. Cys1561Tyr in another kinesin family member (KIF1B) (Table  4 ). The patient (N°34) presented with an early-onset disease including frequent falls and lower limb weakness. At 16 years of age, he developed a tetraparesia during an infectious episode with progressive incomplete recovery.
At 43 years-old, he has lower limb weakness with areflexia, and jaw muscle weakness. Electromyogram study showed evidence of anterior horn cell involvement and we found a complex III deficiency in muscle. The missense variant affects a highly conserved amino acid located in the PH (Pleckstrin Homology) domain, is not frequent in ExAC database (< 0. 01%) and is predicted to be deleterious (Table  4 ). Heterozygous pathogenic variants in DNA2 have been identified in adult-onset mitochondrial myopathy with mtDNA instability  [12]. We found a novel DNA2 heterozygous variant, c. 2862G > C; p. Leu954Phe in a patient presenting with cerebellar ataxia, myoclonic epilepsy, cataract and bilateral hearing loss (Patient N°67) (Table  4 ). Muscle biopsy revealed histological signs of mitochondrial myopathy with low level in complex I and mtDNA multiple deletions.
The variant, localized in the helicase domain, is not found in ExAC database and in silico analysis predicts it as pathogenic (Table  4 ). We identified 2 heterozygous variants in POLG2 in a 38 year-old patient (N°35) presenting with sensory neuropathy and multiple mtDNA deletions (Table  4 ). The c. 1105A > G; p. Arg369Gly variant was firstly being classified as functionally pathogenic  [13]. It was then reclassified by ClinVar as variant of undetermined signification. More recently, this variant has been identified in homozygous state in control individuals and is now considered as likely benign  [14]. The second variant (c. 390-2A > C) is supposed to have an effect on splicing by in silico analysis (http://www. mutationtaster. org/). Mutations that affect splicing and POLG2 expression have previously been described in patients  [15, 16].
Conclusions Our data highlights the great underlying genetic heterogeneity of suspected mitochondrial disease and how hard is to assign a pathogenic role to each variant identified by NGS. International networks are required to create a common genetic database in order to improve the molecular diagnosis of these diseases. Our results also confirm that a panel strategy is not optimal to identify the molecular abnormalities associated with mitochondrial disease even by increasing the number of genes analyzed. Data from the literature suggests that exome may be of greater interest than gene panels that need to be permanently reassessed based on the identification of new responsible genes. Nevertheless, further comparative studies will be needed to determine the best strategy to use for the diagnosis of these pathologies.
Computational results We have implemented our branch-and-cut algorithm in C++, using CPLEX 12. 5 and concert technology  [10]. It was tested on a Xeon Quad-Core E5507 machine with a 2. 27 GHz processor and 8GB RAM, running under Linux. The maximum CPU time has been fixed to 5 h. Each instance is composed of a graph from TSPLIB  [34]  and a set of demands. TSPLIB graphs are complete Euclidean graphs, that is each node is assigned coordinates in the plane, and the weight of each edge is given by the Euclidean distance between its endnodes. The demands used in the instances are randomly generated. Each set of demands is either rooted, that is, of the form {(s, t i ) : i = 1,. , d} (s is the root node of the demands), or arbitrary. The computational results are given in Tables  3, 4 , 5, 6, 7, and 8.
Methodology Crustal deformation pattern during the Mw 7. 4 Sanriku-Oki Tohoku foreshock. To obtain the co-seismic ground displacement field over the Japanese Island (black vectors in Fig. 1a ), we use GPS data from GEONET. Data were analysed with the GIPSY/OASIS-II Version 6. 1. 1 software, developed at the Jet Propulsion Laboratory (JPL). We estimated the station coordinates in precise point positioning (PPP) mode  23. Co-seismic horizontal component offset at the time of earthquake we derived by differencing the mean position 8 days before and 1 day after the foreshock. The observed displacement field of GPS velocity reveals that Japan's northeast coast moved east-southeast up to ~3 cm during this event. This estimated ground displacement correlates well with previous studies  9. To visualize the deformation pattern over the epicentre and rupture area (off-shore), the standard approach of rectangular half-space dislocation modelling of the surface deformation induced by an earthquake is adopted  16.
Acknowledgements The  Geospatial Information Authority of Japan  is duly acknowledged for making available the  GPS Earth Observation Network System (GEONET) GPS data. We thank  Priyesh Kunnummal  and  Ajish P. Saji  for extending their help during the formulation of this paper. DT thanks  Department of Science (DST), Govt. of India  for providing research fellowship. This work is supported by  DST, India. This work is part of the interdisciplinary initiative,  Lithosphere-Atmosphere-Ionosphere-Magnetosphere (LAIM) coupling program  of  Indian Institute of Geomagnetism, Navi Mumbai, India. LR acknowledges support for this research from the  French Space Agency CNES  ( Centre National d'Etudes Spatiales ) and support from the  ANR  project  TO-EOS  for acquiring the 1-Hz GEONET GPS data.
Jets are identified as originating from b-quarks with a multivariate classifier using observables sensitive to lifetimes, production mechanisms, and decay properties of b-hadrons  [39]. The tagging efficiency is determined in simulated t t events to be 60% (85%) for the tight (loose) tagging criterion. Samples of simulated data are used in the design of the measurement, estimation of the background, and the unfolding procedure. P -B  [40]  v1 and v2 were used to simulate tW and t t events, respectively, with P 6. 428  [41] , the five-flavor scheme (5FS) CT10  [42]  parton distribution function (PDF) set, and Perugia 2012  [43]  collection of tuned parameters. An identical configuration except using P 8. 183 and P -B -2 for tW was included for particle-level comparisons. Alternative samples used P -B -2 or M G 5_aMC@NLO (MG5_aMC) 2.