dataesr/scientific-paragraphs-categorization huggingface dataset:
The most pronounced enrichment in TF binding regions was for CpGs in Cluster 2 at ChromHMM predicted enhancers when compared to the distribution of CpGs on the HumanMethylation450 array (Fig. 2b ) or all CpGs (hg19; Supplementary Fig. 2C ). The hg19 locations of the Cluster 2 CpGs, their MCF7 ChromHMM segmentation annotations, and TF binding regions are provided in Supplementary Data 2. H A L L M A R K _ A L L O G R A F T _ R E J E C T IO N [2 0 0 ] IM M U N E _ S Y S T E M _ P R O C E S S [3 3 2 ] IM M U N E _ R E S P O N S E [2 3 5 ] P L A S M A _ M E M B R A N E [1 4 2 6 ] M E M B R A N E [1 9 9 4 ] S IG N A L _ T R A N S D U C T IO N [1 6 3 4 ] D E F E N S E _ R E S P O N S E [2 7 0 ] R E G U L A T IO N _ O F _ B IO L O G IC A L _ P R O C E S S E S [7 0 9 ] R E G U L A T IO N _ O F _ C E L L U L A R _ M E T A B O L IC _ P R O C E O C E S S [7 8 7 ] B IO P O L Y M E R _ M E T A B O L IC _ P R O C E S S [1 6 8 4 ] T _ C E L L _ A C T IV A T IO N [4 4 ] H A L L M A R K _ IN F L A M M A T O R Y _ R E S P O N S E [2 0 0 ] E S T R O G E N _ R E S P O N S E _ E A R L Y [2 0 0 ] E S T R O G E N _ R E S P O N S E _ L A T E [2 0 0 ] N U C L E U S [1 4 3 0 ] R N A _ B IO S Y N T H E T IC _ P R O C E S S [6 3 8 ] C Y T O P L A S M [2 1 3 1 ] T R A N S C R IP T IO N [7 5 3 ] R E C E P T O R _ A C T IV IT Y [5 8 3 ] T R A N S C R IP T IO N _ F R O M _ R N A _ P O L Y M E R A S E _ II _ P I_ P R O M O T E R [4 5 All together, these results clearly show that CpGs in Cluster 2 are mainly found at enhancers containing FOXA1, GATA3, and ERα-binding regions.
3A-C ). To investigate whether low methylation of CpGs in TF binding regions was a cancer specific feature of ER positive breast cancer, we compared the tumor methylation levels to those of non-cancerous tissues. We found the level of DNA methylation at TF binding regions systematically lower in ER positive tumors when compared to adjacent normal tissue (Fig. 5a-c ). As adjacent normal tissue to tumors does not always reflect the physiologically normal breast  32  we compared the methylation levels between tumors and healthy breast tissue from reduction mammoplasty (GSE60185  5  ). We found that 82. 5, 84. 7, and 87. 9% of the CpGs in Cluster 2 and in binding regions of ERα, FOXA1, and GATA3, respectively, were significantly demethylated in ER positive disease when compared to normal tissue (difference in median methylation at least 0.
Therefore, DNA methylation signals measured in tumors with high infiltration (low purity) is more susceptible to be influenced by a mixture of cancer cells, adjacent normal tissue, stroma, and infiltrating immune cells. Cluster1-CpG DNA methylation of breast cancer cell lines (GSE94943) resembled the methylation of tumors with low infiltration. Tumors with higher lymphocyte infiltration showed intermediate methylation levels between breast cancer cell lines and immune cells. This holds true for both Cluster 1A and Cluster 1B and supports that the observed methylation level in tumors with higher infiltration may be attributed to higher numbers of immune cells. In fact, T cells (GSE79144  36  ), B cells (GSE68456  37  ), leukocytes (GSE69270  38  ), and monocytes (GSE68456 37 ) all show low methylation of CpGs in Cluster 1A and high DNA methylation of CpGs in Cluster 1B, oppositely of to the methylation level of breast cancer cell lines (Supplementary Fig.
Methods Patient material. Molecular data from two patient cohorts were publicly available and used in this study. The MicMa breast cancer cohort was collected in Oslo, Norway and has been previously described  47. Informed consent has been obtained from all participants and the study was approved by the local ethical committee (S-97103). The DNA methylation data from this cohort was generated using the Illumina HumanMethylation450 as previously described  5  , and is available in GEO with accession number GSE60185. The gene expression data was generated using Agilent whole genome 4 × 44 K oligo array as previously described  48  , and is available in GEO with accession number GSE19783. The breast cancer cohort of TCGA has been described previously  49  , and the DNA methylation (level 3) and gene expression data (level 3) were downloaded from the TCGA Data Portal (https://tcga-data.
Following this procedure, RNA and DNA originate from the same tissue composition. DNA from tumor tissue was isolated using the Maxwell ® 16 (Promega) instrument and the Maxwell® 16 tissue DNA Purification Kit (Promega). The DNA was eluted in 200-600 µl TE buffer (pH 8. 5) and stored at -20 C. The mRNA expression data and PAM50 classification from the OSL2 cohort are available in GEO with accession number GSE58215  19. DNA methylation analysis. The DNA methylation levels of more than 450,000 CpG sites were interrogated for 330 patient tumors from the OSL2 cohort using Illumina Infinium HumanMethylation450 microarray as previously described  5, 50. Preprocessing and normalization involved steps of probe filtering, color bias correction, background subtraction and subset quantile normalization as previously described.
The DNA methylation data (both raw data and preprocessed data) is available in the GEO database as a Series record with accession number GSE84207. Statistical and bioinformatic analyses. All analyses were performed in the R software  51  unless otherwise specified. Genome-wide correlation analysis. Correlation between the level of DNA methylation of single CpGs and gene expressions was tested assuming as null hypothesis zero correlation against non-zero correlation, using the Pearson correlation statistics (function eMap1, R package eMap)  52. 189,026 CpGs with interquartile range of methylation values above 0. 1, were included in the analysis. In the discovery cohort (MicMa) the correlation of the DNA methylation of these CpGs was tested for all gene expressions (17,558), resulting in more than three billion tests.
cse. ucsc. edu/goldenPath/hg19/encodeDCC/ wgEncodeAwgTfbsUniform/ (as of April 12 th 2013). The enrichment between regions surrounding (± 200 bp) CpGs in Cluster 1 or Cluster 2 and ChIP-seq peak regions for each experiment was computed using the mergePeaks tool from HOMER. For the specific analyses of MCF7 TF ChIP-seq data sets, we retrieved hg19 ENCODE ChIP-seq peak regions in the bed narrowPeak format from the ENCODE data portal (https://www. encodeproject. org) and ChIP-seq peak regions from GEO from the ReMap catalog (http://tagc. univ-mrs. fr/remap/ 30 ). Overlaps between CpGs in emQTL, Cluster 1, and Cluster 2 and ChIP-seq peak data sets were obtained using the bedr R package v. 1. 0. 3 (https://CRAN. R-project. org/ package=bedr). We assessed the enrichment using the hypergeometric tests (phyper R function) with all Illumina Infinium HumanMethylation450 BeadChip CpGs as background (Supplementary Data 1).
Supplementary Fig. 2B  was constructed using data sets showing a hypergeometric log 10 p-value < -100 with the Intervene tool  56  to obtain a SVG figure further edited for colors using inkscape (https://inkscape. org/en/). We retrieved ChIA-PET pol2 loops in the MCF7 cell lines from ENCODE  57  , an emQTL (CpG-Gene pair) was considered to be in a ChIA-PET loop if the CpG and the TSS of the gene were in corresponding genomic interval defining Pol2 loops. Enrichment was calculated using the hypergeometric tests (phyper R function) with all Illumina Infinium HumanMethylation450 BeadChip CpGs as background. ChIP-seq data reprocessing and analysis. ChIP-seq data were acquired from publicly available sources. The accession numbers are as follow: ChIP-seq of ESR1 (GSM798423, GSM798424, GSM798425), FOXA1 (GSM986065 and carroll-lab.
21. Based on ChIP-seq data of key histone modifications (H3K4me1, H3K27ac, H3K4me3, H3K27me3) and regulatory factors (CTCF, RNAPol II), a multivariate hidden Markov model annotate the MCF7 genome into nine distinct chromatin states: heterochromatin, repressed, transcribed, enhancers, enhancers + CTCF, CTCF, promoters + CTCF, promoters and promoter_poised. However, in downstream analysis some annotations were collapsed into one as follow: Enhancer = 'Enhancer' and 'Enhancer + CTCF' and Promoter = 'Promoter', 'Promoter + CTCF' and 'Poised Promoter'. Gene set enrichment analysis. Gene set enrichment analysis was performed using the Molecular Signatures Database v4. 0 (MSigDB; 20 ) H and C5 collections. Enrichment was assessed by hypergeometric testing as implemented in the R stats package. MCF7 culture.
8) followed by quality filtering with FASTX Toolkit (minimum 97% of base pairs should have quality scores higher than 10). Trimmed and quality-filtered data was aligned to human genome hg19 with Bowtie. Differential expression analysis was performed with edgeR, thresholds for analysis were p-value < 0. 05, RPKM > 0. 5 and log fold change >1 or <-1. siRNA analysis. All siRNA experiments were at a final concentration of 50 nM. Transfections were conducted using Lipofectamine 2000 (Invitrogen). For the microarray expression experiment we used the siRNAs directed against FOXA1 against non-targeting siRNA. The microarray data from GATA3 siRNA experiments were available at GSE39623  16. Inhibition of ERα binding at enhancer. pdCas9-PuroR plasmid expressing both dCAS9 and sgRNA was purchased from Addgene (Addgene number #71667).
Vascular invasion, inflammatory cell infiltrate and necrosis, including relation of tumor cells/tumor stroma, were evaluated on slides stained with hematoxylin and eosin as previously described  61. Using a simple microscope, subjective categorization of inflammatory cell infiltrate into the categories of "low", "intermediate-low", "intermediate-high" and "high" was performed based on the frequency of mononuclear inflammatory cell infiltration observed in invasive the tumor. Data availability. The DNA methylation data from the MicMa breast tumor cohort and normal breast tissue from reduction mammoplasty is available in GEO with accession number GSE60185. The gene expression data from the MicMa cohort is available with accession number GSE19783. The newly generated DNA methylation data for the breast cancer cohort OSL2 (n = 330) is available in GEO with accession number GSE84207, and the mRNA expression data from the OSL2 cohort is available in GEO with accession number GSE58215.
DNA methylation several cell lines were used in this study and are available in GEO: breast cancer cell lines (GSE94943), T cells (GSE79144), B cells (GSE68456), leukocytes (GSE69270) and monocytes (GSE68456). The microarray data from GATA3 siRNA experiments are available in GEO with accession number GSE39623. The ChIA-PET Pol2 data is available through the ENCODE project with accession number ENCSR000CAA. MCF7 GRO-seq data are available at GEO with the accession number GSE99508.
Acknowledgements This study was supported by funding from the  KG Jebsen Centre for Breast Cancer Research  ( SKGJ-MED-004 ), and the  South Eastern Norway Health Authority  (grant  2011042  to VN Kristensen). Expression profiling was performed with funding from the  Research Council of Norway  (grant  193387/H10  to  A-L Børresen-Dale  and  VN Kristensen ),  South Eastern Norway Health Authority  (grant  39346  to  A-L Børresen-Dale ) and the  Norwegian Cancer Society. T. F. and X. T. are postdoc fellows funded by the  Norwegian cancer Society  (grant nr 419616111190). R. E. was funded by https://www. forskningsradet. no/( 231217/F20 ) and https://kreftforeningen. no ( 3485238-2013 ). A. M. was supported by funding from the  Norwegian Research Council ,  Helse Sor-Ost , and the  University of Oslo  through the  Centre for Molecular Medicine Norway  (NCMM), which is a part of the  Nordic European Molecular Biology Laboratory partnership for Molecular Medicine.
Methods: there were 9 data collection forms but only forms 1-7 were to be uploaded onto the app. Forms 8A and 9A were to be filled but were to remain at the implementation level for use intra campaign. The forms were coded; Form 1A&B, 2A, 3A, 4A, 5A, 6A, 7A, 8A and 9A The Village form (form 1A&B) captured information by household which included village names, name of head of household, cell phone contact of head of household, number of children aged 9 months to 14years in the household, possible barriers to reaching the children, appropriate vaccination strategy based on barriers identified and estimated or proposed number of teams and type. This was the main form and from this every other form picked the population figures to estimate other supplies and logistics. On advocacy, communication and social mobilization the information collected included mobile network coverage, public amenities such as churches, mosques and key partners at the local level.
On human resource and cold chain supplies the information collected included number of health facilities by type, number of health workers by cadre in facilities within the village, number of vaccine carriers and icepacks by size, refrigerators and freezers. All these forms were to be uploaded onto the phone app. except form 8A, the individual team plan, which was to be used during implementation at the local level. Android phone application, doforms, was used to capture data. Training on micro planning, data entry and doforms app was conducted at National, County, Sub-county and ward levels using standardized guidelines. An interactive case study was used in all the trainings to facilitate understanding. The App was also available on Laptops through its provided web-application. The app allowed multiple users to log in concurrently.
Feedback on all the variables were obtained from the team at the Ward level. The ward level team included education officers or teachers, village elders, community health workers and other community stakeholders. Only the Ward level was allowed to collect information on paper and that information was subsequently transferred to the phone-based app, doforms, by health information officers. The national, county and sub county were able to access their data from the app using a password provided by the administrator. The forms were labelled similar to the hard copy. Of the nine forms on hard copy, only the individual plans and map were not uploaded on the mobile app. as they were operational tools to be used at the implementation level. For the remaining seven forms, it was compulsory for responses to be filled in tandem with the numbering.
The micro planning process Micro planning data collection forms were developed based on forms used in Kenya's 2002 measles catch up campaign and on WHO generic guidelines  [1]. A phone application, doforms was used to capture data. Cascade training on MR campaign microplanning and mobile phone use was conducted starting with National TOTs who trained the County teams. County trainers then oriented the Sub County teams who in turn facilitated the Ward teams. Focal points at all levels facilitated the process. There were 9 data collection forms labelled 1A&B, 2A, 3A, 4A, 5A, 6A, 7A, 8A and 9A, but only forms 1-7 were to be uploaded onto the mobile phone app. The Village form (form 1A&B) captured information by household included villages name, head of household, cell phone contact and number of target population in the household, mobile network coverage, public amenities such as churches, mosques, number of health facilities by type, number of health workers by cadre in facilities within the village, number of vaccine carriers and icepacks by size, refrigerators and freezers.
The app allowed multiple users to log in concurrently. Feedback on all the variables were obtained from the team at the Ward level. The Ward team included staff from the largest health facility in the Ward, key stakeholders and partners at that level such as the village elders, sub chiefs, headmen and community health workers and the Ward political representative. Only the Ward level was allowed to collect information on paper and that information was subsequently transferred to the phone-based app, doforms, by sub county health information officers. The national, county and sub county levels were able to access their data from the app using a password provided by the administrator. Sub County health information clerks transferred the information from hard copies into the relevant format using the mobile phone app.
, the doforms. Data entry was real time such that the County and National teams received the information as it was entered on the phones by the data clerks. A central server database was established and used to aggregate the collected data from all the 47 counties. The National and County teams had no influence over the system or data which were monitored by one independent group administrator. For data changes to be effected, the request would be initiated by the data entry person to the group administrator. He would then initiate a dispatch feature to the specific data person who would then return the form with the correct entries to the system. On the first page of the micro plan forms, counties were required to enter the 9 months-14 years proportion of their population which was to be adopted by the respective sub counties.
This proportion would then reflect on respective forms where population figures were required. Automatic user login trails was performed by the system at regular intervals which boosted the data security and protected the system from manipulation. Results: real time data was received from 46 of 47 counties. One county (Marsabit) did not participate in the micro plan process. Over 97% (283/290) of the sub counties responded and shared various information via the app. Different data forms had different completion rates. There was 100% completion rate for the data on villages and target population. Much valuable information was shared but there was no time for the national and county level to interrogate and harmonize for proper implementation. The information captured during the campaign can be used for routine immunization and other community based interventions.
Acknowledgments The use of mobile app in development and dissemination of micro plans was done in collaboration with the American Red Cross who provided for funds to set up the server used by the phone app,  Ministry of Health, WHO KCO, CHAI  and  UNICEF. We therefore thank the  Ministry of Health  staff at county, sub-county and Health facilities for ensuring the information is available for transmission, WHO KCO ( Dr. Sergon ,  Dr. S. Kiptoon ,  Dr. J. Ogange ,  Dr. J. Mwakisha ,  Dr. Ntoburi ,  Dr. D. Rotich ,  Dr. M. Gedi ) for providing technical support in the areas they were allocated,  UNICEF  ( Dr. P. Okoth ,  C. Kinuthia ) who provided for the operational support to train all levels and also facilitate the  National TOTs, Ministry of Health EPI  ( Dr. Maree ,  Dr. J. Charo ,  Dr.
With the availability of SNP data from the 3000 rice genome project (SNP-seek database, IRRI), an extensive search for SNPs in the EUL genes was performed. This database contains 3024 rice genomes classified into 12 subvarieties. Japonica species are subdivided into tropical japonica (Trop), subtropical japonica (Subtrop), temperate japonica (Temp) and Japx which contains all other japonica varieties. Indica species are divided into Ind1A, Ind1B, Ind2, Ind3 and Indx which contains the remaining indica varieties. Further classes consist of aux and aromatic (Aro) rice and the admix group containing all other unassigned varieties. In total 46 SNPs were identified, of which 13 are located in exons (Fig. 1b ). Of the latter SNPs, seven yielded a silent mutation and 6 SNPs introduced an amino acid change (Additional file 3: Table  S2 ).
Domain architecture and structure of the rice EULs Analysis of the domain structure for the different EUL lectin sequences revealed two putative lectins composed of a single EUL domain (further referred to as S-type EULs) and three proteins consisting of two tandem arrayed EUL domains (further referred to as D-type EULs) (Fig. 2a ). When analyzing the sequence similarity between the different EUL domains (151 amino acids), a high degree of conservation was observed. Sequence identity between the different EUL domains can be as high as 85%, while sequence similarity reaches up to 92% (Fig. 2b , Additional file 4: Table  S3 ). The Nterminal region preceding the EUL domain (ranging from 19 to 117 amino acids) and the linker region connecting the two EUL domains in D-type EULs (ranging from 18 to 76 amino acids) were analyzed using Interproscan and BLAST searches against the NCBI database.
Expression profiles of EUL genes upon plant stress A search in the Transcriptome Encyclopedia of Rice (TENOR) database  (Kawahara et al. 2016 ) was performed and changes in the expression levels were observed for the EUL genes after different abiotic stresses and plant hormone treatments. Figure  5  shows the expression profiles for the different OsEUL genes based on the expression data after application of drought or osmotic stress as well as after exogenous application of absiscic acid (ABA, 100 μM) or jasmonic acid (JA, 100 μM). Transcripts for OsEULD1B and OsEULD2 are highly and significantly upregulated in the shoots in response to drought and osmotic stress (43 and 24 times for OsEULD1B, respectively, and 19 and 11 times for OsEULD2, respectively), especially after 3-12 h of treatment.
In addition, binding sites of five maize and one Arabidopsis WRKY transcription factor and one additional W-box motif were retrieved (Additional file 7: Table  S4 ). Furthermore, binding sites for two bZIP transcription factors from Arabidopsis (bZIP19 and bZIP23) were identified. The other identified motifs in the OsEULS2 regulon are associated with FUS3 and MADS box transcription factors. The presence of a TATABOX1 motif was also confirmed in the OsEULS2 promoter. Most of the significantly overrepresented motifs in the promoter sequence of the OsEULD1B gene belong to the so-called ABRE elements (e. g. ABRELATERD1, ABRETAEM, ABREBNNAPA, ABADESI2) or are associated with the bZIP family of transcription factors according to their annotation in the PLACE database (HY5AT, ACGTTBOX). In total, 59.
japonica, indicating that the rice genome harbours a large set of putative lectin domains each characterized by a specific carbohydrate-recognition domain. Since experimental data points to the involvement of EULs in the plant stress response  (Van Hove et al. 2015; Al Atalah et al. 2014a; Fouquaert et al. 2009) , the focus of this paper is on an in-depth molecular characterization of this lectin family in rice. Five EUL genes were identified in the genome of japonica rice. Orthologs of these 5 genes were also found in the indica genome (Additional file 11: Table  S7 ). Since several databases provide evidence that these EUL genes are expressed, we assume that these five genes are functional. In addition, four pseudogenes have been reported  (Fouquaert et al. 2009 ) for which no evidence of transcription was found.
Interestingly, a high degree of similarity is also observed between the intron-exon structure of OsEULS3 and ArathEULS3, the only EUL gene in the Arabidopsis thaliana genome. Due to the similarity between the structure of ArathEULS3 and OsEULS3/D1A/ D2, it is suggested that the S3 type represents the more ancestral structure. A search in the SNPSeek database comprising the data of the 3000 rice genome project, identified 46 SNPs in the rice EUL genes. Analysis of the SNPs that give rise to an amino acid change revealed clear preferences between indica and japonica subspecies for the SNPs in the D-type EUL genes. Further research is needed to elucidate whether the SNPs contribute to differences in stress tolerance of rice. In PFam (PF14200, Ricin-B lectin 2, 103 amino acids) and SSF (SSF30570, Ricin-B like lectins, 151 amino acids) the EUL domains are annotated as ricin-B like lectins based on the presence of the QxW motif typical for ricin-B domains.