dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-sample transcriptomic consensus classifier construction The network of consensus classes also revealed a core set of consensus samples (see Supplementary material, Methods), that is, tumor samples representative of each consensus class on the basis of their initial subtyping by the six classification systems. We used these core samples (n = 1084) to build a single-sample transcriptomic classifier, as detailed in the Supplementary material (Methods). The classifier was trained on approximately one-third of these samples (n = 403) and achieved 97% mean balanced accuracy on the remaining two-thirds of the core samples (n = 681). This classification tool was implemented as an R package that is documented and freely available at https:// github. com/cit-bioinfo/consensusMIBC. We also offer a stand-alone web application that allows users to classify new samples using the single-sample classifier.
LumP tumors were characterized by high expression of a noninvasive Ta pathway signature  [25]  (p < 0. 001) and were strongly associated with FGFR3 transcriptional activity, as estimated by an FGFR3 coexpressed gene signature  [4]  (p < 0. 001). LumNS tumors displayed elevated stromal infiltration signatures, mainly fibroblastic, compared with the other luminal tumors (p < 0. 001). LumU tumors had a higher cell cycle activity than the other luminal tumors (p < 0. 001). Stroma-rich samples displayed intermediate levels of urothelial differentiation. They were mainly characterized by stromal infiltration as summarized by ESTIMATE stromal   3. (A) We performed a gene set analysis (GSA; see the Supplementary material, Methods) in each dataset to test the significance of differential expression of specific bladder cancer-related signatures in each consensus class compared with the others.
The heatmaps show Stouffer combined GSA p values over all datasets. The upper panel refers to bladder cancer gene sets extracted from the ICA components described in the study by Biton et al. [25]  (see the Supplementary material, Methods). The lower panel displays other bladder cancerspecific signatures retrieved from the literature: urothelial differentiation, keratinization, and late cell-cycle signatures from the study of Eriksson et al. [24] , and an FGFR3 coexpressed signature from the study of Sjödahl et al. [4]. (B) We used two mRNA-based computational tools to characterize tumor microenvironments : ESTIMATE (R package, v1. 1. 0) infers the presence of stromal cells (stromal infiltration) and the infiltration of immune cells (immune infiltration) in a tumor sample using two curated gene signatures described by Yoshihara et al.
[26] ; MCPcounter (R package, v1. 0. 13) uses biologically validated transcriptomic markers of specific immune and stromal cell subpopulations to quantify the presence of these populations in a tumor sample  [27]. We ran MCPcounter and ESTIMATE independently on each dataset, and used t tests to compare scores for each consensus class relative to the others. The heatmaps show Stouffer combined t test p values over all datasets. (C) We computed discrete regulon status (1 for active regulon status, 0 for undefined status, and -1 for inactive regulon status) in each dataset, as described in the Supplementary material (Methods) and in the work of Robertson et al. [9]. We evaluated the association between each regulon status and each consensus class using Fisher exact tests; the heatmap illustrates the resulting p values.
Conclusions We emphasize that we report biological rather than clinical classes. We offer a single-sample mRNA classifier (available in an R package and web application) as a research tool for the retrospective and prospective work required to establish how such classes can best be used clinically. The consensus presented here provides a common foundation for the molecular classification of MIBC. Future substratifications may allow defining a system that is more predictive of a response to treatments; in such work, the clinical/strategic issue will be to decide the subtype granularity or resolution  [42]  that is appropriate for addressing a specific problem. We expect that this consensus classification will help the development of MIBC precision medicine by providing a robust framework to connect clinical findings to molecular contexts and to identify clinically relevant biomarkers for patient management.
7 7 Fig. 5 -Clinical characteristics and prognostic associations. (A) Association of consensus classes with gender (n = 1554), clinical stage (n = 1641), and age category (n = 1383). (B) Five-year overall survival stratified by consensus class (see also Supplementary Fig. 5). Kaplan-Meier curves were generated from 872 patients with available follow-up data. Patients who had received neoadjuvant chemotherapy were excluded from the survival analysis. Detailed statistics of the multivariable survival analyses is given in Supplementary Table6. (C) We selected a set of clinically relevant gene signatures (see Supplementary Table7) and performed a gene set analysis (see the Supplementary material, Methods) in each dataset to test the significance of their differential expression in each consensus class relative to the others.
We used one-sided t tests to assess the differential expression of single genes (PD-1 and PD-L1). The heatmaps show Stouffer combined p values over all datasets. Plus/minus annotation of gene sets indicates association of high gene expression levels with response/resistance to the corresponding therapy. Ba/Sq = basal/squamous; EGFR = epithelial growth factor receptor; FGFR = fibroblast growth factor receptor; IFN = interferon; LumNS = luminal nonspecified; LumP = luminal papillary; LumU = luminal unstable; NE = neuroendocrine; TGF = transforming growth factor.
Instead, this paper describes our methods and choices, and we provide an easy-to-use python module and a set of example calculations for the reproduction of these opacities and variants of them, as well as for handling some of the optical depth effects discussed in this paper. The structure of this paper is as follows: In Section 2 we discuss our choices for computing the dust opacities, and present our Python tools that are publicly available. In Section 3 we apply these opacities to simple size distribution models, starting with a standard power-law model, and ending in Section 4 with the analytic steadystate dust coagulation/fragmentation size distribution of  Birnstiel et al. (2011, henceforth B11) , again including the corresponding Python scripts. Finally, in Section 5 we present a very simple model to link the observed thermal emission to the observed extinction of back-side CO line emission  (Isella et al.
GRAIN-SIZE AVERAGED OPACITIES It is known that dust grains in protoplanetary disks are not "mono-disperse", i. e. , at a given radius in the disk the dust does not consist of only a single size, or a narrow size distribution. Perhaps the most spectacular evidence of this is found in the source IM Lup. When observed at near-infrared wavelengths, this disk shows a strongly flaring geometry  (Avenhaus et al. 2018). Clearly, a substantial amount of fine-grained dust is suspended several scale heights above the mid-plane, and 2 ftp://ftp. astro. princeton. edu/draine/scat/bhmie/bhmie. f 3 https://github. com/birnstiel/dsharp opac  is continuously replenished by turbulent stirring. When observed at millimeter wavelengths, however, we see a disk with small-scale ring and spiral substructure that can only be explained if the geometry of this dust layer is vertically geometrically thin (e.
We compare those results to the ones obtained if the analytic coagulation model fits of B11 are used. The Python script for creating the resulting sizeaveraged opacities is publicly available in the module repository. The total absorption opacity κ abs,tot The top panel in Figure  4  shows the total absorption and scattering opacities at a wavelength of 1 mm for a particle size distribution with a min = 10 -5 cm as function of a max. The bottom panel shows the spectral index β = ∂ ln κ abs,tot ν /∂ ln ν. Similar trends as in  Ricci et al. (2010)  are observed: changes in the size distribution index q mainly affect the asymptotic behavior at long wavelengths and the strength of the Mie interference at a max ∼ λ 2π. Figure  4  also shows that for size distributions that extend up to a max 100 µm, the scattering opacity κ sca,tot ν exceeds the absorption opacity κ abs,tot ν.
Together with the DSHARP dust model, and based on the measurements of  Isella et al. (2018) , we find that the particles in the rings of HD163296 should be at least 0. 2 cm of size. Along with this paper, we present publicly available Python scripts that contain the optical data of many literature materials. In addition to that, functions are available for mixing optical constants with effective medium theory, for calculating opacities using Mie theory, and for averaging opacities over particle size distributions. Implementations of the steady state particle size distributions discussed in this paper are also included. The online material includes these python modules, scripts for generating the results and figures of this paper, and the opacity tables. These materials will likely be extended in the future but the version used in this paper is available at  Birnstiel (2018).
We like to thank  Ryo Tazaki ,  Akimasa Kataoka  and  Satoshi Okuzumi  for helpful discussions and  Thomas Henning  for providing the optical constants data used in this work. T. B. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  714769. C. P. D. acknowledges support by the  German Science Foundation (DFG)  Research Unit FOR 2634, grants  DU 414/22-1  and  DU 414/23-1. Z. Z. and S. Z. acknowledges support from the  National Aeronautics and Space Administration  through the  Astrophysics Theory Program  with Grant No. NNX17AK40G  and  Sloan Research Fellowship. Simulations are carried out with the support from the  Texas Advanced Computing Center (TACC)  at  The University of Texas at Austin  through  XSEDE grant   TG-AST130002.
S. A. and J. H. acknowledge funding support from the  National Aeronautics and Space Administration  under grant No. 17-XRP17 2-0012  issued through the  Exoplanets Research Program. J. H. acknowledges support from the  National Science Foundation Graduate Research Fellowship  under Grant No. DGE-1144152. A. I. acknowledges support from the  National Aeronautics and Space Administration  under grant No. NNX15AB06G  issued through the  Origins of Solar Systems program , and from the  National Science Foundation  under grant No. AST-1715719. L. P. acknowledges support from  CONICYT  project  Basal   AFB-170002  and from  FCFM/U. de Chile Fondo de Instalación Académica.
Interactions between people and ecological systems, through leisure or tourism activities, form a complex socio-ecological spatial network. The analysis of the benefits people derive from their interactions with nature -also referred to as cultural ecosystem services (CES) -enables a better understanding of these socio-ecological systems. In the age of information, the increasing availability of large social media databases enables a better understanding of complex socio-ecological interactions at an unprecedented spatio-temporal resolution. Within this context, we model and analyze these interactions based on information extracted from geotagged photographs embedded into a multiscale socio-ecological network. We apply this approach to 16 case study sites in Europe using a social media database (Flickr) containing more than 150,000 validated and classified photographs.
These studies represent a crucial step towards a better understanding of interactions between people and eco-logical systems, through leisure or tourism activities, but they usually focus only on the presence of individuals on a site, and do not explicitly take into account the spatial relation between humans and nature that underlies beneficial socio-ecological interactions in situ, nor information about the individuals that visit a site. The aim of this paper is to explore the potential of Flickr data for the study of socio-ecological interactions. The guiding idea is that interactions between individuals and ecological systems can be visualized and modeled using geotagged photographs from the Flickr photo-sharing platform embedded in a multiscale socio-ecological network. Based on more than 150, 000 photos taken in 16 study sites across Europe, this study examines the potential of the digital traces that we leave while visiting natural sites to efficiently represent socio-ecological interactions at different scales.
Extracting a multiscale socio-ecological network from social media data Socio-ecological interactions have been extracted from a database containing more than 150, 000 photographs taken between 2000 and 2017 in 16 sites in Europe (Figures  1  and 2 ) and posted on the Flickr social media platform. Each photo is geo-localized (latitude/longitude coordinates), time-stamped and associated with a unique Flickr user ID. In order to ensure that only photographs representing an interaction between an individual and a natural site are considered, each photo has been manually validated and classified according to the landscape and the activity identified on the picture. These validated photographs have been taken and posted on Flickr by 2, 193 reliable users whose place of residence have been identified based on their Flickr timeline using 100×100 km 2 world grid cells.
Evaluation of the network's representativeness New data sources such as Flickr data have the great advantage of being global, in contrast with surveys and census data involving usually only one country or at most only a few countries. In return, they come with several biases associated with the lack of information regarding the users sociodemographic characteristics. In order to collect more information about our sample we automatically sent a questionnaire to the 2, 193 reliable users of our cleaned database through the creation of a Flickr group. We obtained a response rate of 11%. Figure  3  shows some descriptive statistics about the respondents according to their sociodemographic characteristics. We note that men represent about two thirds of the respondents. There are also very few young people, the respondents were predominently professionals.
DISCUSSION Central to the measure of human perception and interest in natural environments is the concept of CES, but it is challenging to relate the supply of these nonmaterial services to specific spatial units. Moreover, the attractiveness of a site and the way we explore it may be influenced by our origins  [31, 34]. Indeed, beyond the analysis of peoples activities in natural sites, an important question remains: how does our origin impact the nature of our relationships with natural ecosystems? Taking advantage of a social media database, we proposed in this work a methodological approach to extract and analyze multiscale socio-ecological networks from volunteered, publicly available data generated from social media. We extracted and analyzed from a Flickr database 7, 354 socio-ecological interactions made in 16 case study sites in Europe by individuals living all around the world.
Limitations of the study In this work, we explore the possibility of making use of social media data to provide information about the way people interact with ecological systems. In particular, we developed a methodology to connect Flickr users' place of residence to places where the interaction took place in the 16 study sites. This allowed us to study how the distribution of interactions varies among sites and according to the distance traveled. Although it would not have been possible to conduct this research at global scale using conventional data sources such as surveys, it is necessary to recognize the potential limits and biases of our approach. First, we cannot ensure the reliability of the data both in terms of space and time resolution. In order to limit the potential biases we considered the photos with the most precise spatio-temporal Flickr ac-curacy level (according to the Flickr API which gives access to the accuracy with which Flickr knows the date and location to be registered).
It is also worth noting that most of our analysis is based on data aggregated both in space (100 × 100 km 2 world grid cells and 500 × 500 m 2 case study site grid cells) and time (month granularity). The spatial aggregation allowed to overcome the spatial accuracy error linked to the used GPS-enabled devices, or the map scale used to specify the photo location. By using a manual photograph validation and classification process, we were able to avoid potential errors in classification that arise when using automatic image processing tools. However, even though the interpretation of the photographs was performed by between 1 and 6 local experts following a rigorous protocol, interpretation of the images may still be subjective to some extent. Another important limitation lies in the lack of information regarding the characteristics of individuals using Flickr.
1a  and Supplementary Data 1). Quality controls confirmed that all samples for the two narrow marks showed relative and normalized strand cross-correlations greater than 0. 8 and 1. 05 (Supplementary Fig. 1b ), respectively, according to expectations  14. Relative to genes (Fig. 1a, c ), reads obtained for H3K27ac, H3K4me3 and H3K4me1 were strongly enriched around Transcription Start Sites (TSS), while H3K27me3 and H3K36me3 showed antagonistic distributions, consistent with patterns seen in other tissues  10. Samples clustered by histone mark, with a strong distinction between activating and repressive marks (Fig. 1b ). To investigate the tissue specificity of our dataset, we compared it with data from other brain regions and blood tissue (Supplementary Fig. 2 ). For each modification, we observed higher correlations among amygdalar samples (r = 0.
3e , f)  19. Compared with known genomic features (Fig. 1f ), this map showed expected enrichments of promoter chromatin states (Act, Wk, or Flk-Prom) at transcription start sites and CpG islands, and of transcription states (Str-Trans and Wk-Trans) within genes. Finally, the chromatin states exhibit expected correlations with gene expression (Supplementary Fig. 4 ). As detailed below, these maps allowed us to characterize cross-talks between chromatin and DNA methylation, and differences between groups. CG and non-CG methylation patterns. We used WGBS to characterize the amygdala methylome (C, n = 17; ELA, n = 21). Rates of bisulfite conversion, sequencing depth, and library diversity met IHEC standards and were similar across groups (Supplementary Fig. 5a-d ). In this large dataset, >13 million CGs showed an average coverage ≥5 in the cohort (Supplementary Fig.
We first compared mCG and mCAC. While CG sites were highly methylated, CAC (Fig. 2b, c ) or other non-CG (Supplementary Fig. 6c ) sites were mostly unmethylated, with a minority of them showing methylation levels between 10 and 20%, consistent with mouse data  29. Regarding distinct genomic features and chromosomal location, we confirmed that (i) while mCG is lower within promoters, this effect is much less pronounced for mCAC (Supplementary Fig. 7a )  11  ; (ii) compared with CGs  30  , depletion of methylation from pericentromeric regions is even stronger at CACs, and (iii) as expected, methylation levels were very low in both contexts in the Fig. 2  Characterization of non-CG methylation in the human brain lateral amygdala. a Average genome-wide levels of DNA methylation were measured among the sixteen three-letter cytosine contexts (CNN, where N stands for any base) in the human brain lateral amygdala, using whole-genome bisulfite sequencing.
Altogether, these combined analyses defined major epigenetic and transcriptomic pathways affected by ELA in the lateral amygdala. Finally, we sought to determine whether molecular changes associated with ELA in the lateral amygdala might also affect other brain structures implicated in emotional regulation. To do Fig. 7  Differential gene expression in subjects with a history of early-life adversity (ELA). a Volcano plot of RNA-Seq data showing the 261 and 474 genes that were up-(green circles) or downregulated (red circles) in the ELA group compared with the control (C) group (GLM model using gender, age, pH, PMI, and RIN as covariates; nominal P value < 0. 05). b Gene Ontology analysis of the 735 differentially expressed genes in the ELA group. Terms showing evidence of enrichment for differential methylation, histone profile or chromatin state are shown in yellow (small GTPase) or blue (immune processes; see also Supplementary Fig.
16f ). c, d Gene Set Enrichment Analysis (GSEA) of gene expression changes in ELA subjects. Genes were ranked based on log 2 fold changes from the C versus ELA differential expression analysis ("Ranked list metric", in grey in the lower portion of each panel). Genes with the highest positive fold changes (in red, upregulated in the ELA group) are at the extreme left of the distribution, and those with the lowest negative fold changes (in blue, downregulated in the ELA group) are at the extreme right. A running enrichment score (green line, the upper portion of each panel) was computed for gene sets from the MSigDB curated molecular signatures database and used to identify enriched gene sets  42. Among the numerous gene sets related to the immune function that showed evidence of genome-wide significant negative correlation with ELA (see main text, and Supplementary Table  14 ), two representative gene sets are shown (with the middle portion of each panel showing vertical black lines where members of the gene set appear in the ranked list of genes): c "Interferon-gamma", and d "Leukocyte migration".
Of note, an oligodendrocyte-specific gene collection, which we recently found downregulated in the anterior cingulate cortex of subjects with a history of ELA  40  , positively correlated with ELA in the amygdala (see Supplementary Fig. 16d ), suggesting opposite adaptations in this glial population between cortical and subcortical structures. Source data are provided as a Source Data file. so, we took advantage of gene expression RNA-Seq data recently published by our group using anterior cingulate cortex (ACC) tissue from a cohort of C and ELA individuals (n = 50) that, importantly, included all subjects from the present amygdala study. To identify patterns of shared transcriptional adaptations associated with ELA in both regions, we used Rank-Rank Hypergeometric Overlay (RRHO2  47  ). Results uncovered strongly significant overlapping groups of genes that were either commonly downregulated (P-adj = 10 -487 , Benjamini-Yekutieli), or commonly upregulated (P-adj = 10 -388 ), in both the lateral amygdala and ACC (Supplementary Fig.
16b ). Strikingly, enrichment analyses (see Supplementary Fig. 16c  and full results in Supplementary Data 11) showed that a large majority of GO terms previously identified during the multi-epigenetic investigation of the single amygdala dataset were also recovered by this combined analysis of transcriptomes from two distinct brain regions. Future studies will be necessary to better understand whether similar or divergent epigenetic processes underlie such common transcriptional effects across various brain regions as a function of ELA.