dataesr/scientific-paragraphs-categorization huggingface dataset:
The specific mechanisms driving the elevated expression of immune-related genes in Cluster 3 remain unclear, however increased levels of immunogenicity and upregulation of MHC protein and antigen processing complexes have been independently observed in aneuploid tumors  (46, 47) , consistent with the elevated level of chromosomal aberrations in Cluster 3. Cluster 4 CCAs, which are associated with IDH1/2 mutations and FGFR2 and PRKA-related gene rearrangements, might also be tested with recently described IDH inhibitors (ClinicalTrials. gov identifier: NCT02073994) or FGFR-targeting agents. Notably, our study suggests that loss of FGFR2-3′ UTRs may represent an alternative mechanism of FGFR2 activation, beyond FGFR2 in frame gene fusions and activating mutations. This, along with our additional observation of FGFR3 rearrangements, expands the proportion of potential FGFR-targetable cases in CCA  (48).
Sample collection Primary tumor and matched normal samples (non-neoplastic liver or whole blood) were obtained from the SingHealth Tissue Repository (Singapore), Fundeni Clinical Institute (Romania), Khon Kaen University (Thailand), ARC-Net Biobank (Italy), Centre de Ressources Biologiques Paris-Sud (France), Department of Pathology, Yonsei University College of Medicine (South Korea), Hospital do Cancer de Barretos (Brazil), Linkou Chang Gung Memorial Hospital (Taiwan) and Department of Pancreatobiliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University (China) with signed informed consent. WGS data from 10 Japanese cases were also included (National Cancer Center, Japan   1  and Supplementary Table 5 ). A detailed list of clinical data is included in Supplementary Table  1A.
Analysis of somatic promoter mutations with FIREFLY FIREFLY (FInding Regulatory mutations in gEne sets with FunctionaL dYsregulation) is a method for identifying gene sets dysregulated by somatic promoter mutations through modulation of TF binding. We extracted somatic promoter mutations by selecting noncoding sSNVs within +/-2kb of TSSs of GENCODE genes. We identified those mutations predicted to change TF-binding based on PBM data (details below). We then evaluated gene sets for enrichment in promoter binding-change mutations. For each gene set, we calculated the test statistic M = number of genes in the gene set with binding-change mutations, summed across all tumors. To identify gene sets enriched in binding-change mutations, we performed two statistical tests in sequence, where gene sets found significant in each test (q < 0.
1) were then tested in the next: (1) Fisher's exact test; (2) a synthetic mutations test. For gene sets passing these tests, we then performed gene expression tests using the "GSA" v1. 03 R package  (37)  to assess their transcriptional dysregulation. Further details are provided in the Supplementary Methods. We generated synthetic mutations in promoter regions of interest based on the genome-wide frequency of mutations at each trinucleotide observed for each tumor/normal pair. Synthetic mutations were generated separately for each tumor, based on the frequencies and mutation types observed in the actual tumor. Further details are provided in the Supplementary Methods.
Identifying TF binding-change mutations To identify binding-change mutations, we used TF-DNA binding specificity PBM data from the cis-BP database  (34). PBM experiments measure TF binding to all possible 8-mer sequences with a DNA binding enrichment score (E-score)  (35). Typically, E-scores > 0. 35 correspond to specific TF-DNA binding  (57). To call binding sites for a particular TF, we required that such sites contain at least two consecutive 8-mers with E-scores > 0. 4. We also used PBM data to call 'non-binding sites' defined as genomic regions containing only 8mers with E-score < 0. 3. For each somatic mutation and each TF with available PBM data, we analyzed the 15-bp genomic region centered at the mutation. If the region contains a TF binding site in the normal sample but not in the corresponding tumor sample, the mutation was called a 'loss-of-binding' mutation for that TF.
Identification of L1-retrotransposition insertions We searched for sources of somatic L1 insertions by looking for highly recurrent SVs: ≥ 10 SVs in a 1Mb region. We then selected the subset of these region that contained a mobile L1 element in a database of retrotransposon insertion polymorphisms (dbRIP)  (62). Only SVs with ≥ 2 reads with poly-A tails at the putative L1 insertion site were retained for further analysis.
Validation of structural variations For genomic DNA, 100 ng of whole-genome amplified DNA of the tumor and normal matched cases were used as PCR templates. For cDNA, total cDNAs of tumor and normal matched control were synthesized using SuperScript III System according to manufacturer's instructions (Invitrogen) and 40 ng of cDNA were used as PCR template. PCR was performed using fusion-specific primers with Platinum Taq DNA Polymerase system (Invitrogen). PCR products were cleaned up by the Exo/Sap enzyme system (Invitrogen) and bidirectionally sequenced using the BigDye Terminator v. 3. 1 kit (Applied Biosystems) and an ABI PRISM 3730 Genetic Analyzer (Applied Biosystems). Sequencing traces were aligned to reference sequences using Lasergene 10. 1 (DNASTAR) and analysed by visual inspection.
Copy-number analysis Raw SNP array data was processed using Illumina Genome Studio. We used ASCAT v2. 0 (63) to estimate allele-specific copy-number profiles. We determined regions of copynumber alteration based on their relative copy-number using the "copynumber" R package  (64). For tumor-normal pairs without SNP array data, we estimated copy-number profiles based on sequencing data using Control-FREEC (65), Quandico  (66) , and Sequenza  (67). We used GISTIC v2. 0. 22  (68)  to determine regions of significant focal copy-number alterations, using ASCAT/Sequenza's inferred copy-number segments, and associated copynumber values were defined as log 2 of the segment's relative copy-number. Full details are provided in the Supplementary Methods.
Gene expression analysis Gene expression microarray data was pre-processed using the "lumi" R package  (69). Batch effects were removed using ComBat  (70). We used GSEA v2. 2. 2 (71) with a classic weighting scheme to determine pathways upregulated or downregulated in each integrative CCA cluster relative to the others, employing canonical pathways in the MSigDB C2 catalogue of annotated gene sets. Full details are provided in the Supplementary Methods.
DNA methylation analysis DNA methylation profiles were obtained for 138 tumors and 4 normal samples. Data was preprocessed using the "minfi" (73) and "wateRmelon" (74) R packages. We selected 4,520 probes with the highest standard deviations in β-values across the tumors, and mean β < 0. 5 in the normal samples, for clustering using the "RPMM" R package  (75). In the hypermethylated methylation clusters (1 and 4), we considered a CpG site to be hypermethylated if the following conditions held: (1) β < 0. 5 in normal samples; (2) Mvalues were significantly different in the (i) hypermethylated cluster versus (ii) the combined normal samples and the low-methylation tumors-those not in methylation cluster 1 or 4 (q < 0. 05, two-sided t-test); and (3) its mean β in the hypermethylated cluster minus the mean β across the normal samples and low-methylation tumors was > 0.
Integrative clustering The "iClusterPlus" R package (76) was used to perform integrative unsupervised clustering of 94 CCAs based on 4 genomic data types: (i) somatic point mutations in 404 targeted genes (gene by sample matrix of binary values), (ii) sCNAs defined as copy-number segments identified by ASCAT v2. 0, (iii) the most variable expression probes (coefficient of variation > 0. 1), and (iv) the most variable methylation probes (top 1% standard deviation in β-value). We ran iClusterPlus. tune with different numbers of possible clusters (n=2 to 7), choosing the number of clusters at which the percentage of explained variation leveled off (n=4), and the clustering with the lowest Bayesian information criterion (BIC). Further details are provided in the Supplementary Methods.
Acknowledgments This study is performed as part of the  International Cancer Genome Consortium (ICGC). We thank the  Duke-NUS Genome Biology Facility  for methylation and gene expression assays, the  GIS Population Genetics facility  for SNP arrays and the  Cytogenetics Laboratory, Department of Molecular Pathology, Singapore General Hospital  for FISH analysis. We thank  D. Jefferson  ( New England Medical Center, Tufts University ) for H69 cells. We also thank the  SingHealth Tissue Repository ,  Dr. Catherine Guettier  and  Dr. Jean-Charles Duclos-Vallée  ( DHU Hepatinov, Hôpital Paul Brousse, AP-HP, Villejuif, France ) for tissue samples. Financial support: We thank  Sir Lambert Cornelias Bronsveld  and  Lady Bronsveld-Ngo Kim Lian  for philanthropic support (B. T. T. ). This work was supported by the  Singapore National Medical Research Council  ( NMRC/STaR/0006/2009 (B.
This report summarises the conduct and key findings from the fourth and final scientific workshop organised under the Mas²tering project (Multi-Agent Systems and Secured coupling of Telecom and Energy gRIds for Next Generation smart grid services), which is co-funded by the European Union under the FP7 programme. The workshop was co-located with Sustainable Places 2017 hosted by Teesside University in Middlesbrough, UK. Sustainable Places is an annual international conference focused on innovations for Energy-efficient Buildings and the wider smart grids that connect them. This fourth edition of the Mas 2 tering workshop series aimed at validating the project's work to date with respect to its approach, technologies, use cases and in-progress results. For this purpose, a panel of three experts was formed and to them the project presented an Overview & Approach, Physical Testing at the ENGIE Crigen facilities, simulation of the Cardiff Grid, Project Use Cases and the Business Model Approach.
Key Findings and Discussion A series of prepared questions and open discussion shaped the panel session. The following paragraphs capture the main topic areas and points of interest. Decentralised Decision Making: The panel was in agreement with the approach of decentralised decision making (MAS) coupled to a bottom-up prosumer centric approach and leveraging local energy communities. It was noted that participant "buy-in" to pilot programs was often not linked to the concept of flexibility management but benefits involved in the retrofit program (in the cited case, new kitchens with smart appliances as part of a larger home retrofit). Validation of the Simulation Approach: The attendees appreciated the approach used for the simulation activities. In particular the definition of numerous smart grid scenarios based on expected evolution trends appeared much more reliable than the creation from scratch of a scenario where all Mas 2 tering objectives could be achieved.
Acknowledgments:  Mas²tering (Multi-Agent Systems and Secured coupling of Telecom and Energy gRIds for Next Generation smart grid services ) is co-funded by the  European Union  under the  FP7 programme , grant n°  619682. The  Mas²tering  project can be referenced at http://www. mas2tering. eu/. Author Contributions: Paper written by  Thomas Messervey ,  Zia Lennard ,  Juan Manuel Espeche , and  Mario Sisinni. Reviewed by  Marie-France Robbe ,  Meritxell Vinyals ,  Julien Ardois , and  James Sharman.
Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states. LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states.
The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i. e. , the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data.
g. , nutrients, water clarity, and chlorophyll concen-trations) and measures of lake ecological context across regions, the continent, and the globe (e. g. ,  [13] [14] [15]. In practice, measures of water quality are often collected from a relatively small number of lakes within individual regions. In the United States, large investments have been made in water quality monitoring by federal, state, local, and tribal governments; and many, but not all, of the data sets have been placed in government data repositories such as the USGS National Water Information System (NWIS) and the USEPA Storage and Retrieval (STORET) database. Unfortunately, these data repositories do not currently allow us to study lake water quality at broad scales. Despite the large number of water quality records in these systems, a recent analysis of stream nutrient data obtained from NWIS, STORET, and more than 400 other organizations determined that more than half of the data records lacked the most critical metadata necessary to make the data usable (e.
g. , chemical form, parameter name, units)  [16] , and we would expect a similar result with lake data because they are typically treated similarly to stream nutrient data. In addition, STORET and NWIS do not include any measures of lake ecological context. Therefore, to study the controls of eutrophication specifically, and water quality in general, requires development of a comprehensive database for lake water quality that is integrated with measures of lake ecological context and sufficient metadata for robust analysis. We created a database called LAGOS-NE, the "lake multiscaled geospatial and temporal database" for thousands of inland lakes in 17 of the most lake-rich states in the upper Midwest ≥4 ha shown as blue polygons. Some lakes extend beyond state borders and are included in the database if it was possible to delineate their watersheds.
Watershed boundaries rather than state boundaries were used for all analyses of lakes, streams, and wetlands. The map is modified from Soranno et al. [17]. and Northeastern United States (Fig. 1 ). We avoided the problem of lack of metadata for the water quality data by contacting the original data providers for water quality data, asking for metadata, and only including data for which sufficient metadata were available. We addressed the problem of lack of ecological context data by creating our own database of lake ecological context. The detailed methods and approach for building this database have been published previously  [17] ; here we publish and describe the database for the 51 101 lakes and reservoirs ≥4 ha in the study area (1 800 000 km 2 ). We had 3 related motivations for developing this database: (i) to facilitate further development of our basic understanding of lake water quality at broad scales using water quality data on thousands of lakes collected over the last several decades (see  [11, 17]  for details); (ii) to build the capacity to apply this scientific understanding to environmental management and policy of inland waters; and (iii) to foster broad-scale research by designing an open-science database that is extensible for future uses and by making the data and methods publicly accessible.
LAGOS-NE comprises 3 data modules that, although integrated in the same database, were derived using different data sources and data integration methods, and thus must be version-controlled separately. LAGOS-NE LOCUS v1. 01 includes lake location and physical characteristics based on an existing national-scale database of lake and streams in the United States for all lakes. LAGOS-NE GEO v1. 05 includes measures of land, water, and air (ecological context) obtained from existing national-scale GIS (geographic information system) data sets and measured in multiple zones (delineated by different spatial classifications) around all lakes. This module also contains some temporal data for climate, land use/cover, and atmospheric deposition variables. LAGOS-NE LIMNO v1. 087. 1 includes in situ measurements of lake water quality for a subset of the above lakes.
These 87 data sets of lake water quality were obtained from a combination of sources including government, tribal agencies, university researchers, citizen scientists, and non-profit agencies. Samples were taken during any season of the year from the most recent decades, mostly from the late 1980s to 2012. The largest challenge in building LAGOS-NE was the heterogeneity of the data set formats, variable conventions and units, and metadata, none of which were standardized. Many steps of data integration required manual input from experts in diverse fields and close collaboration among specialists in ecoinformatics, database design, freshwater ecology, and geography; all combined, the effort took 6 years and involved ∼15 individuals, spread across numerous institutions. We designed the database using principles of open science so future users could ask new research questions by using the existing database or adding new data modules to the database.
To ensure that users could do this, we documented the major steps of data set integration and carefully integrated metadata directly into the database itself, we emphasized data provenance, and we used a database versioning system. In this data paper, we make the following research products available: (i) data tables with the data that make up LAGOS-NE and an R package for accessing the data and integrating the tables; (ii) for each of the 87 water quality data sets, we provide the ecological metadata language (EML) metadata files that we authored after receiving the data, the data files that we processed to import into LAGOS-NE and the R-script that we wrote to process the data; and (iii) GIS coverages of the underlying freshwater geographic features (lakes, streams, and wetlands) that are linked to the data tables for GIS processing by researchers.
Overview of LAGOS-NE LAGOS-NE includes some data on all lakes in a study area (above the minimum lake area threshold, which was 4 ha), which we call the "census" population of lakes. The census population of lakes is a critical feature of LAGOS-NE because it allows us to characterize the ecological context of every lake in our study population and to identify whether the lakes for which we have water quality data are biased in any way. LAGOS-NE includes 3 main categories of variables: (i) variables that describe the physical characteristics and location of lakes themselves; (ii) variables that describe in situ water quality; and (iii) variables that describe a lake's ecological context at multiple scales and across multiple dimensions (such as hydrology, geology, land use, climate, etc. ) based on the principles of landscape limnology  [12, [18] [19] [20].
Three factors dictated which data were included: past research and theory about the spatial and temporal controls of lake water quality, data availability and quality, and the time and resources necessary to compile, integrate, and process the original data. In other words, data that were especially time-and resource-intensive to collate, integrate, or process were given lowest priority and, in some cases, were not ultimately incorporated into the database. There was a number of constraints for each of the categories of data that had to be considered. For creating the census population of lakes (i. e. , their geospatial location, perimeter, and surface area), we relied on a single source of data (the 1:24 000 National Hydrography Dataset [NHD])  [21]. For the in situ water quality data, we incorporated data only if they were in a digitally accessible format such as a text or spreadsheet file.
Finally, for the ecological context variables, we included only data for which we could obtain a GIS or raster coverage at the national or state scale for all 17 states. We organized these 3 categories of data into database "modules" that had similar data types and sources so that we could develop procedures and set standards for each module (Fig. 2 ). The module structure also facilitates data reuse and extension by accommodating future data modules related to any other lake or ecological context feature. The design of LAGOS-NE and the workflow for its construction have been described previously in detail  [17]. In particular, the database design is based on the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI),   [17] , this module was called LAGOS-lakes), and LAGOS-NELIMNOv1.
087. 1. We include descriptions of the types of data that are included in each module, with the major categories of variables the same as those describing the data tables in Additional file 1. The black connectors among the modules show that the modules are connected to each other through common unique identifiers through the LAGOS-NELOCUS module (through the unique lake ID). P is phosphorus, N is nitrogen, C is carbon, S is sulfur, and atm is atmospheric. This figure is modified from Fig. 1  in Soranno et al. [17]. Community Observations Data Model (ODM; CUAHSI ODM) as described in Soranno et al. [17]. Here, we provide a brief overview. One important guiding principle in creating LAGOS-NE was to ensure data provenance, i. e. , that we could trace the original source data through to the final LAGOS-NE database.
Because each data module had different types of source data, we developed different procedures for data provenance for each module, described in Soranno et al. [17]  and in this paper. The database model is based on ODM because it is a flexible data model (i. e. , allows the incorporation of a wide range of types of data) that allows for the incorporation of controlled vocabulary and, importantly, allows for extensive documentation through a relational database structure of linked tables containing metadata  [17]. The database was created and is maintained in PostgreSQL v9. 1. However, for researchers to use the database for analysis and modeling, it is necessary to export the data into tables that can be processed by statistical packages or computer code. Therefore, we exported the data into a series of tables (of similar data) that are needed to conduct research on either the census population of lakes, the lakes for which there are water quality data, or some combination.