dataesr/scientific-paragraphs-categorization huggingface dataset:
Bulk mRNA from three independent wild-type CG1 strain fish was also sequenced and used as a control. Principal component analysis identified six distinct clusters corresponding to whole CG1 syngeneic fish, leukemia, MPNSTs, angiosarcomas, germ cell tumor and ERMS (Figure  3A ). By comparing gene expression among different tumor types, unique tumor-specific expression profiles were identified and each assessed for overlap with gene sets found in the Molecular Signatures Database (MSigDB, Figure  3B ,C, Figure  3 -source datas 1-3). For example, the upregulated leukemia gene set   To assess similarities between tp53 del/del tumors and human, we next assessed if zebrafish tumors express tumor-specific gene signatures identified from human angiosarcoma  (Andersen et al. , 2013) , MPNST  (Kolberg et al.
, 2015)  and ERMS (experimentally determined using GEO: GSE108022) (Figure  3 -figure supplement 1-source data 1). Using Gene set enrichment analysis (GSEA)  (Mootha et al. , 2003; Subramanian et al. , 2005) , we identified significant enrichment of signatures associated with human angiosarcoma (FDR q-value = 0. 001, Figure  3 -figure supplement 1A), MPNST (FDR q-value = 0. 00433526, Figure  3 -figure supplement 1B), and ERMS (FDR q-value = 0, Figure  3 -figure supplement 1C) in the corresponding zebrafish tp53 del/del tumors but not other tumor types (Figure  3 -figure supplement 1-source data 1). Taken together, these data reveal conserved gene expression programs associated with both the predicted cells of origin and the corresponding human cancer counterpart. Given that GSEAsig analysis failed to assign leukemias to a specific lineage, we next assessed if these tumors were enriched for signatures associated with normal blood cell lineages identified previously by our group using single-cell RNA sequencing of the zebrafish marrow  (Tang et al.
RNA sequencing and analysis Paired-end reads from poly(A) + RNA-seq were aligned to the GRCz10 reference zebrafish genome with STAR v2. 4. 0  (Dobin et al. , 2013)  using GRCz10v85 Ensembl annotations. PCR duplicates were removed with Picard v1. 95 [http://broadinstitute. github. io/picard/] and reads aligning to ribosomal RNA were removed with RSeQC  (Wang et al. , 2012)  Gene counts were obtained from reads with an alignment quality of at least 10 using featureCounts  (Liao et al. , 2014)  and transformed to transcript per million (TPM) units. Human orthologues of zebrafish genes were obtained from the Beagle database  (Tang et al. , 2017 ; available at: http://chgr. mgh. harvard. edu/genomesrus/). Differential expression analysis was performed with DESeq2  (Love et al. , 2014) , requiring log2(FC) !2 and an FDR < 0.
Gene set enrichment analysis Human tumor-specific gene signatures were assessed for enrichment in tp53 del/del tumor types using GSEA 3. 0. (Mootha et al. , 2003; Subramanian et al. , 2005). Gene signatures were assessed for anigosarcoma  (Andersen et al. , 2013) , MPNST  (Kolberg et al. , 2015) , and ERMS (GEO:GSE108022). The ERMS signature was defined by genes up-regulated in both human and zebrafish kRAS G12Dinduced tp53 wt/wt ERMS when compared with normal muscle (log2(FC) !2). GSEA was completed in comparing individual tumor types to all other tumors using the default parameters and 1000 permutations of the data. Comparison of tp53 del/del and tp53 M214K/M214K MPNST RNA sequencing data from four tp53 M214K/M214K homozygous mutant MPNST samples were processed as described above. Differential expression analysis was performed as described above, comparing tp53 del/del and tp53 M214K/M214K MPNSTs to whole CG1 controls.
Statistical significance was assessed with a one-sided Fisher's exact test on a background of genes that were expressed in at least 4 out of 15 samples. Molecular signature database (MSigDB) analysis and leukemia similarities with NK cells The top 500 most differentially regulated genes within each tumor type were identified and assigned human gene IDs using the Beagle database. These humanized gene lists were then queried for overlaps with molecular signatures from MSigDB. Only the top 50 enriched gene sets were analyzed and representative examples of enriched data sets are shown in Figure  3C. For blood cell analysis in Figure  3D ,a 30 gene signature was defined for each of the major blood cell lineages and then cumulative gene expression analyzed across tumor types as described by  Tang et al.
The distributions of these variables measured in data are shown in Figs. 1  and 2 , and are compared to the sum of signal and background events from simulation. A total of 662 381 events are measured in data, of which 92. 1% are predicted from the POWHEG+PYTHIA simulation to be tt events. Single top quark production and V+jets production contribute 4. 4% and 2. 1% to the total number of events, respectively, as estimated from simulation. The component of multijet QCD events is estimated from control regions in the data, and comprises approximately 1. 4% of the total number of events. The control regions are designed to obtain data samples that are enriched in QCD multijet events that are kinematically similar to the signal region, but with little contamination from tt, single top quark, and V+jets events.
Unfolding and cross section calculation For each kinematic event variable the yield of tt events in each bin is unfolded to correct for the detector acceptance, efficiency, and bin-to-bin migrations stemming from the detector resolution to obtain the yield of tt events in the visible phase space at the particle level. The bin widths are chosen to give a low level of bin-to-bin migration, and are always greater than the detector resolution. A response matrix, constructed using the POWHEG+PYTHIA sample, relates the kinematic event distributions at reconstruction level to those at particle level. The response matrix also includes efficiency and acceptance corrections. Unfolding is performed by inverting the response matrix, based on a least-squares fit with Tikhonov regularization, implemented in the TUNFOLD software framework  [53].
Summary Normalized and absolute differential tt production cross sections with respect to several kinematic event variables are measured at the particle level in a visible phase space region. The results are based on proton-proton collision data at √ s = 13 TeV, collected by the CMS experiment with an integrated luminosity of 35. 9 fb -1. The total cross section is observed to be consistent with previous results and next-to-next-to-leading-order calculations, and the differential measurements are compared to several tt production models: POWHEG+PYTHIA, POWHEG+HERWIG++, MG5 aMC@NLO-LO, and MG5 aMC@NLO-NLO. The POWHEG+PYTHIA simulation is found to be generally consistent with the data, with residual differences covered by theoretical uncertainties. The jet multiplicity distribution is particularly well-modeled, having been tuned on LHC 8 TeV data.
Data sources/Measurement and definition of outcomes Children attended review clinics at ages 1, 3, 5, 8, 11, and 16 years. Validated questionnaires were interviewer administered to determine parentally reported history of wheeze, eczema, and rhinitis, and treatments received. SPT was used to ascertain atopic sensitisation to common inhalant and food allergens, and lung function measurements were obtained using spirometry at all visits from age 5 years. A blood sample was collected in children who gave assent for venepuncture  [16]. Primary care medical records were examined and data including wheeze episodes, prescriptions of asthma medications and oral corticosteroid, and hospitalisations were extracted. In this study, we performed a cross-sectional analysis using data collected at age 11 years.
CRD We measured sIgE to 112 allergenic molecules using ImmunoCAP ISAC (Thermo Fisher Scientific-Phadia AB, Uppsala, Sweden) at the follow-up at age 11 years. The level of componentspecific IgE antibodies was reported in ISAC Standardised Units (ISU). To ascertain co-occurring sensitisations among participants, we dichotomised IgE data according to the manufacturer's guidelines, using a binary threshold (positive>0. 30 ISU). To evaluate the differential connectivity structure of component-specific IgEs, we used continuous raw values.
Validation To evaluate the robustness of our results and provide external narrow validation  [27] , we repeated the analysis among cohort participants who had ISAC CRD data at age 8 years, excluding the children whose data were used in the primary analysis at age 11 years. For children in the validation step, both CDR data and clinical outcomes were ascertained at age 8 years. All statistical analyses were run in the programming language R  [28]. Distance correlation was computed with the package energy  [29]. JDINAC scripts were made available by the authors  [26]  at https://github. com/jijiadong/JDINAC. We used igraph package for network visualisations  [30] , epitools to estimate the odds ratio (OR)  [31] , clValid to compute internal validity measures for HC  [32] , and caret to infer the penalised logistic regression model  [33].
Participant flow and demographic data Among 1,184 children born into the cohort, 822 attended clinical follow-up at age 11 years. CRD data were obtained for 461 (56. 1%) children. Demographics of these 461 participants are presented in S1 Table ; we have also previously reported that there were no significant differences in demographic characteristics or outcomes between cohort members with and without CRD  [13]. Of 461 children with CRD, 221 (47. 9%) had positive sIgE to at least one of the 112 allergen components  [13] , and 94 (20. 4%) had current asthma. After filtering  [19] , 44/112 allergen components were active; 213 (46. 2%) children had at least one of the active component IgEs >0. 30 ISU, 73 (34. 3%) of whom had asthma. The list of components that were inactive  [19]  and the proportion of children who had positive sIgE to these 'rare' components are presented in S2 Table.
As a result, the soils are extremely acidic by the deposition of acyclics, and this is also a very shady environment  (Frank and Finckh 1997). Beyond this classification, we characterized the landscape structure into three surface samples of 25 km 2 (one per locality), including all home gardens (Fig. 1 ). A study of landscape features and computed descriptive statistics was conducted using ArcGis 10. 1, Spatial Analysis tools and FRAGSTATS software  (McGarigal and Marks 1995). The selection of metrics was based on their ability to characterize various aspects of fragmentation such as total area of fragments per class, mean size of patch (index of fragmentation), number of patches (indicator of several ecological processes and landscape heterogeneity), total edge (indicator of patch complexity), average nearest-neighbor distance and mean proximity index (see  McGarigal et al.
DATA ANALYSIS In order to test the main hypotheses, we explored the relationship between the landscape variables (i. e. , distance to different types of vegetation where medicinal plants can be found) and the richness of native and introduced species found in home gardens. Figure  2  shows a schematic representation of our methods and a conceptual framework of our methodological proposal. Using the multiple minimum distance tools from ArcGis 10. 1, we calculated the shortest distance between home gardens and different land-use classes (i. e. , Ur, Nf, Tc, etc. ). We measured distance between each home garden and different vegetation classes except Wetland, Pasture, and Water bodies because these are poorly represented in the region and we used a new distance variable called Accr (access to national and/or provincial routes and secondary paved roads) to perform an independent analysis.
This was done in order to evaluate the degree of relationship between these latent variables and predictor variables, and thus, we were able to identify the variables that best explain the variation in the richness of native and introduced species of Paraguayans' home gardens. All analyses were performed using the Infostat/P v2010 program  (Di Rienzo et al. 2010). To evaluate the management and the role of home gardens as social ecological spaces where the processes of domestication of native plants may occur, we performed a classification of the species according to the type of acquisition management that they were subjected to  (Casas et al. 1996). Consequently, we divided the plant management into two stages: initial management and further management. In the first stage, we distinguished practices, such as planting, sowing, and direct transplant from other habitats.
Acknowledgements The authors are grateful to the study participants for their generosity in sharing their ethnobotanical knowledge. The authors thank  Gabriela Orofino ,  Lucía Cariola ,  Violeta Furlan ,  Ana Calviño ,  Ramiro Aguilar  and  Carlos De Angelo  for useful references and comments. The project was funded by the  National Science Centre, Poland , on the basis of decision  DEC-2013/09/N/HS3/02226. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 I n t e r n a t i o n a l L i c e n s e ( h t t p : / / creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
A post hoc power calculation shows our sample of 421 (severe smoking/ex-smoking vs severe nonsmoking) and 399 (severe nonsmoking vs mild/moderate) is sufficient to identify a difference in treatable trait prevalence between cohorts with a medium effect size (0. 3) and a power close to 1. 00. Data analysis was supported by IBM SPSS Statistics for Windows, Version 24. 0. Armonk, NY, USA, with significance set at P < 0. 05, unless otherwise stated.
Wagers reports grants from Innovative Medicines Initiative, other from Roche, grants from European respiratory society, during the conduct of the study, other from GSK, other from European Respiratory Society, outside the submitted work; Dr. Chung reports personal fees from Advisory Board membership, grants for research, personal fees from payments for lectures, outside the submitted work; Dr. Sterk reports grants from Innovative Medicines Initiative (IMI), during the conduct of the study; Dr. Fowler has nothing to disclose. The research leading to these results has received support from the Innovative Medicines Initiative (IMI) Joint Undertaking, under grant agreement no. 115010, resources for which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and kind contributions from companies in the European Federation of Pharmaceutical Industries and Associations (EFPIA) (www.
The optimal treatment for patients with low-grade glioma (LGG) WHO grade II remains controversial. Overall survival ranges from 2 to over 15 years depending on molecular and clinical factors. Hence, risk-adjusted treatments are required for optimizing outcome and quality of life. We aim at identifying mechanisms and associated molecular markers predictive for benefit from radiotherapy (RT) or temozolomide (TMZ) in LGG patients treated in the randomized phase III trial EORTC 22033. As candidate biomarkers for these genotoxic treatments, we considered the DNA methylome of 410 DNA damage response (DDR) genes. We first identified 62 functionally relevant CpG sites located in the promoters of 24 DDR genes, using the LGG data from The Cancer Genome Atlas. Then we tested their association with outcome [progression-free survival (PFS)] depending on treatment in 120 LGG patients of EORTC 22033, whose tumors were mutant for isocitrate dehydrogenase 1 or 2 (IDHmt), the molecular hallmark of LGG.
The results suggested that seven CpGs of four DDR genes may be predictive for longer PFS in one of the treatment arms that comprised MGMT, MLH3, RAD21, and SMC4. Most interestingly, the two CpGs identified for MGMT are the same, previously selected for the MGMT-STP27 score that is used to determine the methylation status of the MGMT gene. This score was higher in the LGG with 1p/19q codeletion, in this and other independent LGG datasets. It was predictive for PFS in the TMZ, but not in the RT arm of EORTC 22033. The results support the hypothesis that a high score predicts benefit from TMZ treatment for patients with IDHmt LGG, regardless of the 1p/19q status. This MGMT methylation score may identify patients who benefit from first-line treatment with TMZ, to defer RT for long-term preservation of cognitive function and quality of life.
Patient samples Tumor specimens (formalin fixed paraffin embedded  [FFPE]  or frozen) were collected from patients treated in the phase III trial EORTC 22033 (NCT001828199)  [6]  for high-risk low-grade glioma. Non-tumoral brain tissue (n = 5, FFPE) was available from epilepsy surgery. Patients agreed and signed written informed consent for translational research according to institutional and international guidelines and regulations. The trial design of EORTC 22033 comprised a 2-step process: first, patients were registered and tumor tissue was submitted for central pathology review and determination of the deletion status of chromosome 1p (stratification factor). Second, at the time when at least one high-risk feature was present (age > 40 years, progressive disease, tumor size > 5 cm, tumor crossing the midline, or neurological symptoms) patients were randomized to focal radiotherapy (RT, 50.
External datasets External datasets comprised the LGG dataset from The Cancer Genome Atlas (TCGA; n = 197; 90 WHO grade II, 106 WHO grade III, 1 unspecified WHO grade)  [7]  and a set of anaplastic glioma (AGlioma, WHO grade III, n = 227; GEO accession number GSE58218)  [50]. The TCGA dataset was randomly split into two sub-datasets, called TCGA-1 and TCGA-2 (stratified by age, 1p/19q codeletion status, WHO grade, CIMP status, and overall survival), to obtain a test and a validation cohort for marker selection and construction of a predictive model for purity estimation. (dbGaP accession number phs000178. v9. p8; http://cance rgeno me. nih. gov).
Data processing The normalization  [20]  for Illumina HM-450K arrays included NOOb background correction, dye-correction (chemistry I vs II) and RUV-2 step (removal of unwanted variation) based on control probes using the function preprocessFunnorm from the R package minfi  [3]. DNA methylation was summarized by M values  [15]. The Com-Bat procedure  [25]  was used to limit experimental variation and batch effects across the four datasets.
Probe selection CpG probes with detection p values of more than 0. 01, located on the sex chromosomes, or in SNPs were removed. A three Gaussian mixture model based on M values was used to establish the DNA methylation status of probes in nontumoral brain tissue (NTB) in order to remove methylated and hemi-methylated probes. Then selection of methylation probes was restricted to CpGs located in gene promoters within 1500 nucleotides up-or down-stream of the transcription start site (TSS). The gene locations were based on Homo sapiens data from UCSC build hg19. The list of DDR genes was adopted from Pearl et al. [41]. A list of 167235 CpGs was detected in promoter regions and 101981 CpGs were considered as unmethylated in non-tumoral brain tissue. CpG methylation was defined as "functional" when the correlation between CpG methylation and expression of the corresponding gene was negative in both datasets TCGA-1 and TCGA-2, with a Pearson correlation coefficient inferior or equal to -0.
Expression Gene expression from RNAseq data (Level 3) was quantified for the transcript models using RSEM  [33]  and normalized within-samples to a fixed upper quartile for TCGA. Further details are available at the DCC data portal of TCGA. Genelevel data were restricted to genes expressed in at least 70% of samples. The complete dataset was normalized by the VOOM procedure  [30] , and it was split according to the TCGA-1 and 2 DNA methylation datasets.
Molecular subtype In all datasets the samples were classified into the three molecular glioma subtypes according to the revised WHO classification 2016  [36]  using HM-450K data. The G-CIMP status was determined by unsupervised clustering (Ward's algorithm with Euclidean distance) as previously reported  [5] : the G-CIMP status served as approximation for the IDH mutation status, as this information was not available for all samples in any of the datasets. The IDH mutation status was available for 123/132 patients in EORTC 22033, 194/227 patients in AGlioma, 96/201 patients in TCGA-1, and 99/197 patients in TCGA-2. The 1p/19q codeletion status was assessed using the combined intensities for methylated and unmethylated signals and circular binary segmentation to detect copy number aberration (CNA) events as previously described  [4].
Estimation of tumor purity based on HM-450K The tumor purity of each sample was estimated by a predictive model based on DNA methylation information using the purity estimation by the ABSOLUTE procedure available for the LGG TCGA dataset  [7]. The split LGG TCGA datasets served as training and validation sets, respectively. The prediction of the purity of the samples was provided by a Sparse Partial Least Squares model (SPLS), where unmethylated (β-median < 0. 2) CpG-probes located in intergenic regions were the predictors and ABSOLUTE purity estimation corresponded to the response (after arcsin-square-root transformation). The SPLS regression used the PLS-NIPALS algorithm (maximize covariance between variable of interest and predictors) with lasso regularization to reduce the number of dimensions and to limit multicollinearity  [31].
Statistical analysis Differential DNA methylation and differential gene expression between codeleted and non-codeleted IDHmt tumors were tested by moderated t test (R package limma) on M values  [15]  and normalized expression, respectively. Betavalues, equivalent to methylation fraction, were used to complement M values as a measure of effect size. The set of differentially methylated positions (DMPs) was established with a fold change cut-off for the Beta-value of 0. 1 and for the fdr-adjusted p values of 0. 1. This analysis was performed for the four datasets and the list of candidate DMPs was given by the intersection of the four results. Comparison of DNA methylation patterns were investigated by a simultaneous analysis of several tables based on a principal component analysis algorithm called STATIS  [29].
This method enables the analysis of the relationship among data tables with the same variables (CpG-probes) and combines them into a compromise matrix corresponding to the optimal agreement between the data. Briefly, STATIS can be separated into three steps: the interstructure, the analysis of the compromise and the intrastructure. The interstructure consists of the comparison of the table by RV-coefficient  [44]  and the construction of the compromise. The RVcoefficient measures the similarity between two tables: the values 0 and 1 correspond to unperfect and perfect matching between the two tables, respectively. A permutation test based on the first eigenvalue of the interstructure was developed to evaluate the strength of the relationship among datasets. The analysis of the compromise yields a reference system providing an optimal averaged representation of the columns (CpG-probes).