dataesr/scientific-paragraphs-categorization huggingface dataset:
Micro-array based gene expression profiling RNA samples (see also Online Supplementary Methods) were profiled on a custom designed Agilent micro-array covering all protein coding genes and 12,000 lncRNAs (23,042 unique lncRNA probes) as described by Volders et al. 48 The data-analysis workflow can be found in the Online Supplementary Methods. The data discussed in this publication have been deposited in the NCBI Gene Expression Omnibus  49  and are accessible through GEO Series accession number GSE62006. Complete details of study methods can be found in the Online Supplementary Appendix.
g. DTX1, NRARP, NOTCH3) upon GSI treatment (Figure  1A ). A decrease in ICN1 protein levels was shown by Western blot analysis and downregulation of the canonical Notch1 target gene DTX1 upon GSI treatment was further validated by RT-qPCR (Online Supplementary Figure  S1 ). Amongst previously annotated lncRNAs  48  we could detect significant differential expression (adjusted P-value <0. 05) for 83 lncRNAs, using a base mean cut off of 100 (Figure  1B ). In total, 50 out of the 83 differentially expressed lncRNAs were down-regulated after GSI treatment. Besides previously annotated lncRNAs, we also detected differential expression of non-coding transcripts that had not been previously annotated in other databases (Gencode, lncRNAdb, Broad Institute and Ensembl release 64). 48 Differentially expressed lncRNA loci with a base mean higher than 100 and identified as "unknown, intergenic transcript" or "transfrag falling entirely within a reference intron" by Cuffcompare were retained for further analysis.
Validation of Notch regulated lncRNAs in an extended panel of T-ALL cell lines, normal T-cell subsets and primary T-ALLs To further validate our data, we used a custom designed Agilent micro-array  48  developed in house that contains probes for 15 of the 27 previously annotated lncRNAs and the recently identified LUNAR1 lncRNA that were shown to be regulated by Notch in the above described RNA-seq data from the T-ALL and normal thymocyte models. First, we treated the T-ALL cell lines ALL-SIL, TALL-1, HPB-ALL and DND-41 with GSI (Online Supplementary Figure  S4A  and B ) and carried out gene expression profiling after 12 h and 48 h. Inclusion of the GSI-treated CUTLL1 cell-line samples and the samples of 4 donors of CD34 + thymocytes cultured on OP9 stromal cells, revealed that there was a significant overlap between the RNA-sequencing data and the micro-array data as validated by overlapping   Isolate human thymic CD34 + progenitor T-cells the protein-coding signatures derived from both datasets by Gene Set Enrichment Analysis (GSEA) (Online Supplementary Figure  S4C  and D ).
Nevertheless, few lncRNAs were significantly Notch dependent over all samples of the extended panel of T-ALL cell lines (ALL-SIL, HPB-ALL, DND-41 and TALL-1), probably related to the difference in the T-ALL genetic subgroup and concomitant differences in maturation arrest of the different cell lines evaluated (Figure  3A ). From our selection, only lnc-PLEKHB2-1 and lnc-UBXN4-1 were differentially haematologica | 2014; 99(12)   S6 ) and the data are representative for 4 independent donors. (C) Similar analysis as in (B) for the other lncRNAs that are significantly correlated with the expression of DTX1 (see also Online Supplementary Table  S6 ); data are representative for 4 independent donors. (D) Expression of LUNAR1, lnc-FAM120AOS-1 and lnc-UBXN4-1 in NOTCH1 wild-type (WT) versus NOTCH1 mutant (MUT) primary T-ALL samples.
Remarkably, 9 out of 15 lncRNAs (and LUNAR1) from this selection significantly correlated with DTX1 expression (Spearman rho correlation), supporting their regulation by Notch during early stages of normal T-cell development (Figure  3C  and Online Supplementary Table  S9 ). Moreover, we also had access to 15 primary T-ALL samples of which 7 harbored activating NOTCH1 mutations while 8 were wild type (all cases are FBXW7 wild type). There was a significant difference in expression of LUNAR1 and lnc-FAM120AOS-1 between NOTCH1 wild type and mutant cases (Figure  3D  and Online Supplementary Tables  S10  and S11 ). By implying an addi-tional dataset of 64 primary T-ALL patient samples, we could correlate the expression of lncRNAs lnc-PGBD5-2, lnc-FAM120AOS-1, lnc-c2orf55-1 and LUNAR1 with the Notch1 positively regulated gene set Vilimas_NOTCH1_targets_up 52 by GSEA (Figure  3E ).
Genome-wide analysis reveals direct Notch1 binding to selected lncRNAs To further validate the direct regulation of selected lncRNAs by Notch, publicly available ChIP sequencing (ChIP-seq) data from the CUTLL1 cell line were analyzed for Notch1 binding at specific loci. 34 From the Notch-driven annotated lncRNAs that overlapped between normal and malignant thymocytes (Figure  2D ), 13 out of the 27 lncRNAs were bound by ICN1 (Online Supplementary Table  S12 ) as illustrated for lnc-UBXN4-1 and lnc-PLEKHB2-1 (Figure  4 ). Remarkably, 12 out of the 13 lncRNAs with a Notch1 binding peak also show Brd4 and Med1 binding. Notably, from the putative Notch regulated lncRNAs that showed correlated expression with DTX1 in CD34 + and CD4 + CD8 + normal thymocytes, 6 out of 9 (Figure  4  and Online Supplementary Figure  S5 ) showed binding of ICN1, suggesting that the majority are a direct Notch target.
LRRC28 In addition, we evaluated the presence of H3K27 acetylation (H3K27ac) ChIP-seq signal at these lncRNA loci, a histone mark indicative for putative enhancer regions. For 18 out the 27 selected lncRNAs, H3K27ac ChIP-seq signal was present in close proximity of the promoter region (Online Supplementary Table  S12 ), suggesting the presence of enhancer sequences. Moreover, this public ChIP-seq data also showed LUNAR1 to be directly bound by ICN1, Brd4, Med1 and H3K27ac (Figure  5 ). We also evaluated ICN1 binding at annotated lncRNA loci up-regulated upon GSI treatment of CUTLL1 T-ALL cells and down-regulated in CD34 + T-cell progenitors upon DLL1 exposure in the OP9 in vitro co-culture system. Only 4 out of the 18 overlapping annotated lncRNAs that are negatively regulated by Notch (Online Supplementary Figure  S2A ) showed direct binding by ICN1.
Attributing functional annotation to Notch-regulated lncRNAs through guilt-by-association analysis As described above, we defined a core set of 27 Notchdriven and previously annotated lncRNAs by considering only those differentially expressed and positively regulated by Notch signaling in the GSI perturbation model in CUTLL1 cells and the in vitro OP9-DLL1 co-culture system. As a next step, we aimed to assign potential functionalities to each of these candidates. To this end, we used the socalled 'guilt-by-association' approach (see also Online Supplementary Methods). As previously mentioned, 15 out of these 27 lncRNAs (together with LUNAR1) were detectable by a probe on our custom designed micro-array platform. In a first step, we calculated the Spearman correlation coefficients between the lncRNAs-of-interest and all protein coding genes using the expression data of a primary T-ALL cohort of 64 patients from which we profiled all samples on the custom designed Agilent array.
A B C these lncRNAs in Notch-regulated T-cell biology. This could be related to various functions of Notch signaling, including T-cell lineage specification and commitment, proliferation and Importantly, the recently identified lncRNA LUNAR1 51 was present amongst the most robustly Notch regulated long non-coding RNAs in our data sets. LUNAR1 was shown to be required for efficient T-ALL growth as a consequence of its role in enhancing IGF1R mRNA expression to sustain IGF1 signaling. 51 s a prelude to assigning functional annotation to the newly assigned Notch-regulated lncRNAs in this study, we applied the so-called 'guilt-by-association' approach in which functions are predicted based upon correlation with known protein coding genes and subsequent gene set enrichment analysis. For the selected lncRNAs, various functions were predicted, several of which are linked to Tcell biology or processes that are perturbed in cancer.
(C) Similar analysis as in (B) for the other lncRNAs that are significantly correlated with the expression of DTX1 (see also Online Supplementary TableS6); data are representative for 4 independent donors. (D) Expression of LUNAR1, lnc-FAM120AOS-1 and lnc-UBXN4-1 in NOTCH1 wild-type (WT) versus NOTCH1 mutant (MUT) primary T-ALL samples. (E) Gene set enrichment analysis (GSEA) using the public gene set 'VILIMAS_NOTCH1_TARGETS_UP' 52  and the Spearman correlations between all protein coding genes and the set of 15 selected annotated candidate Notch lncRNAs was performed. This NOTCH1 signature was significantly enriched within the set of protein coding genes positively correlated to the expression of lnc-PGBD5-2, lnc-FAM120AOS-1 and lnc-c2orf55-1. This enrichment was also found for lncRNA LUNAR1.
1995b). In this study, we focused our analysis on the coding sequences. We found a total of 41 polymorphic sites between the het-c alleles in the ORF, only six of which correspond to synonymous substitutions. Four of these synonymous substitutions occur in het-c7 (supplementary data S1, Supplementary Material online). These observations suggest that the het-c locus may be under positive diversifying selection. We have thus analyzed het-c divergence using the HyPhy software package. The codon-based substitution models developed by  Nielsen and Yang (1998)  and  Yang et al. (2000)  were implemented within the HyPhy software package (Kosakovsky Pond and Muse 2005). These models, based on the ratios between nonsynonymous and synonymous substitutions ! = dN/dS, allow for varying rates of substitutions between sites to account for varying selection forces.
Other codons are under purifying selection. This indicates that the main force driving evolution of het-c alleles is positive diversifying selection acting on a limited number of codons. The positions under selection are indicated on the structure of the HET-C protein (fig. 2 ). Positively selected positions are located on the two opposite sides of the "sandwich-motif. " The three positions that show the strongest statistical support for positive selection (123, 133, and 137) group on the HET-C structure (fig. 2B ). Among them, position 133 is proven experimentally to be involved in allele specificity. We next asked whether positive selection is a general feature of GLTP encoding genes in fungi. Sequences from different fungal GLTPs were recovered after blastx searches of the fungal genome sequences in the NCBI Fungal Genome Central database using the P.
anserina het-c2 sequence as a query. The accession numbers for 37 sequences retrieved are listed in supplementary figure  S5  (Supplementary Material online). The chosen sequences cover the subphylum of the Pezizomycotina. A protein-guided alignment of the ORF sequences was generated with ClustalW, and a Neighborjoining tree constructed using the MEGA4 package  (Tamura et al. 2007 ; supplementary fig. S5 , Supplementary Material online). All models found overall low dN/dS ratios of 0. 15 to 0. 34 (table  2 ). We thus conclude from this analysis that GLTP encoding genes in filamentous fungi are evolving under purifying selection and that positive selection appears specific to P. anserina. This implies that in P. anserina the GLTP het-c has gained a function in non-self-recognition in addition to its function as a GLTP.
Primers used were as follows: 3 0 -CG AAGGTGAAAACGAGACGA-5 0 and 3 0 -ACCAAGGCTGGACC TGATA-5 0. Based on the sequence of het-c1 (GenBank: L36207), these primers are expected to give a product of 1,792 bp. PCR was done with following cycle conditions: 5 min at 95 C followed by 35 cycles of 30 s at 94 C, 30 s at 50 C, and 2 min at 72 C, followed by one cycle of 5 min at 72 C and a final hold step at 4 C. PCR products were cleaned using the Sigma GenElute PCR clean-Up Kit. Cleaned PCR products were sequenced using the value read service of MWG biotech. Sequencing was done in two directions using primers binding just outside the coding part of het-c. Primers used for sequencing were as follows: 3 0 -CAACCAACC TTCAACCAACC-5 0 and 3 0 -CAGATGCGTATGCTTTTTGC-5 0. These primers bind just outside the ORF of the het-c gene.
Sequence Analyses Alignments of the DNA and amino acid sequences were done with ClustalW  (Larkin et al. 2007 ). Phylogenetic trees were constructed using the Neighbor-Joining method with the software package MEGA4  (Tamura et al. 2007). Bootstrap supports, expressed as a percentage, were calculated over 1,000 replicates. Additionally, the sequences were analyzed for possible positive diversifying selection on het-c codons. To detect selection, we used the methods developed by  Yang et al. (2000) , which are implemented in the HyPhy software  (Kosakovsky Pond and Muse 2005). Besides testing the het-c alleles, a set of 37 GLTP encoding genes from filamentous fungi found by a Blast search of the Fungal Genome Database held at the NCBI, including one P. anserina het-c allele, was tested for positive diversifying selection.
The sequence accession numbers are listed in supplementary figure  S8 , Supplementary Material online. Transformation and Testing Functionality of New het-c Alleles Protoplasts were prepared and transformed as previously described (Berges and Barreau 1989). The recipient Dhet-c P. anserina strain lacks the entire het-c ORF  (Saupe et al. 1994). The PCR products made for sequencing of the new het-c variants were cloned in the pGEM-t easy vector (Promega). 7. 5 mg of the cloned het-c DNA was used to cotransform protoplasts of the Dhet-c strain with 1. 5 mg of the pPaBle plasmid conferring resistance to phleomycin  (Coppin and Debuchy 2000). The transformed protoplasts were spread on synthetic medium with a high sucrose concentration and phleomycin for selection of transformants. After 4 to 5 days, transformants were transferred to a standard synthetic medium.
Each one of these houses is equipped with solar panels on the roof, external storage batteries and plug-in devices. They have a HEMS sending data to the EDMS which returns information about the district consumption and sends demand-response (DR) alerts when needed. All this is available on the EDMS portal where a lot of data are visible (energy consumption of the community, amount of electricity generated by the solar power generators, amount of emitted CO 2 ). A special page calculates results for each household and compares these to other ones in the community to give each family its own ranking. Information is visible on the HEMS screen, on the computer but also on an electronic photo frame usually loaded with family photos. The frame becomes blue if consumption is reaching a peak and red if DR action is needed, indicating the elements of the dynamic pricing system.
Studied parameters Seventy-nine variables of interest were extracted from the database and analyzed. These included clinical data (familial pedigree, cancer site, clinical presentation at diagnosis, treatment modalities, survival) and tumor characteristics (complete pathological data, presence of synchronous adenoma in the resected specimen, MMR status, presence of genetic and epigenetic alterations). Clinical follow-up of patients was performed according to published national guidelines  [11]. Survival curves were established from the date of diagnosis according to Kaplan-Meier calculations. Details of studied variables are provided in Table  S1.
RNA extraction and microarray analysis Total RNA was isolated using Trizol (Invitrogen, Carlsbad, CA) and checked for purity, integrity and quantity. Samples were then amplified, labeled and hybridized on an Affymetrix Human Genome U133 plus2 GeneChip, following the manufacturer's one-cycle target labeling protocol (Affymetrix, Santa Clara, CA). The chips were scanned with an Affymetrix GeneChip Scanner 3000 and raw intensities were extracted from subsequent images using GCOS 1. 4 software (Affymetrix). Data were normalized using the Robust Multiarray Averaging (RMA) method, implemented in the R package affy  [15]. Data are available at the NCBI Gene Expression Omnibus repository (accession number: GSE39084).
Supervised analysis Associations between clinical and molecular annotations and sample groups were tested for statistical significance using the Chisquared test or Fisher exact test where appropriate. Genes differentially expressed between tumors from young and old MSS patients were selected based on Limma moderated t-test pvalues adjusted for multiple testing by the Benjamini & Hochberg method (,0. 05)  [17]. Pathways from the Biocarta database were tested for enrichment of deregulated genes by combining the following methods: Globaltest (R package globaltest), SAM-GS, and Tuckey  [18, 19]. The p-values of each method were converted into ranks and the mean rank was computed to order pathways.
Transcriptome analysis Microarray data were obtained for 70 patients. Unsupervised analyses revealed that, irrespective of the number of clusters and the percentage of variant genes used, the underlying partitions were consistently and strongly associated with MMR status and groups. Other variables significantly associated with the unsupervised partitions were tumor location, CIMP status and mutated BRAF. These variables were themselves closely associated with MMR status. Age was slightly associated with some unsupervised partitions (obtained using 5% and 20% of the most varying genes). The consensus partition in the 5 clusters was most closely associated with the four groups defined on MMR status and age (Figure  3 ). Cluster 1 (C1) was almost exclusively composed of deficient MMR tumors.
Background: Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. Results: We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes.
Tumor classification based on DNA methylation Genome-wide DNA methylation analysis was performed on a total of 239 breast tumor samples and 46 normal samples. The tumor samples included 22 pure DCIS, 31 mixed DCIS-IBC and 186 pure IBC. A gene region collapsed data set was constructed to reduce the dimensions of the data and to study the methylation in the functional regions of the genes. Each gene is represented by up to six methylation values representing the respective functional regions as described in Materials and methods). Hierarchical clustering was performed to explore the structure of the DNA methylation data, using methylation levels of the 500 most variable gene regions (Figure  1 ). The samples were divided into two main clusters, where one contained all normal samples as well as tumor samples, and the other contained only tumor samples.
Only the significant CpGs or regions that were differentially methylated in both datasets are reported. Comparing normal tissue and DCIS revealed that 16,949 CpGs were differentially methylated, representing 5,659 genes. Comparing DCIS and IBC revealed that 2,000 CpGs were differentially methylated. These CpGs represented 1,076 genes, and 1,745 of the CpGs were hypermethylated while 255 of the CpGs were hypomethylated (Table  1 ). All differentially methylated CpGs are shown in Additional files 5 and 6. SAM was also applied to the gene region collapsed data, and comparing normal tissue and DCIS in the two independent patient cohorts revealed 1,249 differentially methylated gene regions representing 1,011 genes. In comparison, 166 gene regions representing 154 genes were differentially methylated between DCIS and IBC (Table  1 ).
Gene expression analysis mRNA expression data were available for a subset of 104 of the IBC samples studied here. An Agilent whole genome 4x44K oligo array was used for the mRNA analysis as previously described  [48]. The mRNA expression data are available in GEO with accession number GSE19783. Molecular subtypes of breast cancer (luminal A, luminal B, HER2-enriched, basal-like and normal-like) were determined using the PAM50 gene list.
Methylation data processing Statistical and bioinformatical analyses of the methylation level of the 285 samples were performed on two individual datasets, one including methylation levels of all 468,424 CpGs, and one including only 'gene region collapsed' data. The 'gene region collapsed' methylation data were constructed to reduce the dimensions of the methylation data and to focus the analysis on regions that are most relevant for gene function. A CpG that is mapped to a gene is located in one of six subregions: TSS1500, TSS200, 5' UTR, first exon, body and 3' UTR. These subregions represent: 1) CpGs that are between 1,500 and 200 bp upstream of the TSS; 2) CpGs that are between 200 bp upstream of the TSS and the TSS itself; 3) CpGs in the 5' UTR; 4) CpGs in the first exon; 5) CpGs in other exons or in introns (body); and 6) CpGs in the 3' UTR.
Correlation between DNA methylation and gene expression Correlation between DNA methylation level and gene expression was investigated by two approaches. First, if a CpG was within 100 kb of the TSS of a gene, the methylation level of the CpG and expression of the gene were tested for non-zero correlation using Pearson correlation (eMap1 function in the R package eMap)  [50]. For both analyses an association was considered significant if the Bonferroni corrected P-value was smaller than 0. 05. Genome-wide correlation between methylation and expression was visualized using the R package quantsmooth  [51]. Second, the median methylation level of CpGs in the 'gene region collapsed' data and gene expression of the corresponding gene was tested for non-zero correlation using Pearson correlation (R function corr.
Survival analysis Lasso regularization  [52, 53]  was applied to identify CpGs for which methylation level predicted survival (cv. glmnet function in the R package glmnet)  [54]. This approach gives a signature of targets that together capture the variation that is associated with survival of patients. Pre-selection was performed before regression in order to reduce the number of possible CpG sites and to focus on the ones correlated with expression. Univariate absolute correlation between methylation level and expression with nominal P-value lower than 0. 05 were used to preselect data, and 182,653 CpGs were included in the analysis. The analysis was run 100 times and the probes that were present in 80% of the resulting lists were used in the final signature. The coefficients were calculated as the mean of the coefficients in all lists where the probe was present.
Patients were divided into high-and low-risk groups according to the following index for patient i: index i ¼ X n g¼1 β g ⋅X g i where g is the target (CpG or gene), n is the number of targets, β g is the Lasso coefficient for target g and X gi is the methylation value for target g in patient i. Kaplan-Meier estimator and log-rank tests were performed using the functions Surv, survfit and survdiff (R package survival)  [55]. Breast cancer-specific survival was used in all analyses. Multivariate Cox proportional hazard survival analysis was performed using the function coxph (R package survival) to adjust for ER status, TP53 mutation status, T status and lymph node status. Each parameter in the multivariate model was investigated for violations of the assumption of proportional hazards using the function cox.