dataesr/scientific-paragraphs-categorization huggingface dataset:
FigureS5: IPA assignment variants. Final TP fraction obtained versus N increment for the standard HK-RR dataset, starting from random within-species HK-RR pairs. For both the MI-IPA and the DCA-IPA, two variants of the protein pair assignment strategy are presented. Once pairing scores are computed, pairs are assigned within each species either using the Hungarian algorithm, which maximizes the sum of scores in the species, or using a greedy algorithm, that favors individual pairs with top scores (see Methods). In all the rest of the paper, the Hungarian algorithm is used for the MI-IPA, and the greedy one is used for the DCA-IPA, as in [21]. All results are averaged over 50 replicates employing different initial random pairings, and the dashed line represents the average TP fraction obtained for random within-species HK-RR pairings.
High-risk APLs harbor an altered DNA methylation signature Next, we assessed whether hrAPL and APLs presented differential global CpG methylation patterns. To extend our dataset, we included DNA methylation data of a cohort of 13 APL patients (450 K, from TCGA;  [26] ), of which 3 had an exceptionally high white blood cell count (>10,000/µL) and survival below 1 month that could be integrated as hrAPL cases (Figure  5A, 5B ). Supervised clustering to identify differential CpG methylation sites between hrAPLs and APLs revealed a set of 1,817 CpGs that could classify APLs into the highor low/intermediate-risk category (Figure  5C ). These included 399 hypomethylated CpG regions (CpG coverage >4 and p-value < 0. 005) which displayed enriched H3K4me3 and H3K27ac levels in hrAPLs (Figure  5D ), and 1,418 hypermethylated CpG regions.
Two rounds of bisulfite conversion were performed to assure a high conversion rate. An enrichment for adaptor-ligated DNA was carried out through 7 PCR cycles using the PfuTurboCx Hotstart DNA polymerase (Stratagene). Library quality was monitored using the Agilent 2100 BioAnalyzer (Agilent), and the concentration of viable sequencing fragments (molecules carrying adaptors at both extremities) estimated using quantitative PCR with the library quantification kit from KAPA Biosystem. Paired-end DNA sequencing (2 × 100 nucleotides) was then performed using the Illumina Hi-Seq 2000. The data are publicly available and can be accessed via the Blueprint DCC: http://dcc. blueprint-epigenome. eu/#/experiments.
Peak calling and identification of promoter and enhancer regions For peak calling the BAM files were first filtered to remove the reads with mapping quality less than 15, followed by fragment size modeling (http://code. google. com/p/phantompeakqual-tools/). The peakcalling algorithm MACS2 (http://github. com/taoliu/ MACS/) was used to detect the binding sites for the three studied histone marks at default q-value (5. 00e-02). H3K4me1 peaks were called using the broad setting of MACS2 while peaks for marks H3K27ac, H3K4me3 and transcription factor PML-RARA were called using the default (narrow) setting. In order to identify promoter and enhancer regions, non-overlapping H3K4me3 and H3K4me1 bound regions were taken into account, respectively. The common promoter and enhancer regions across the hrAPL samples were computed using the in-house script intersectbed.
pl. Further, normalized tag enrichment for H3K27ac and H3K27me3 was used to mark active and inactive promoter and enhancer regions. Also, normalized tag enrichment for H3K27ac and H3K9/14ac in common enhancer regions was used to compute differentially active regions across different samples (pt#7, pt#8 and pt#9) and cell types (CD34 + , promyelocytes, metamyelocytes, band neutrophils and segmented neutrophils). The intensity graphs were generated using the in-house script makeColorprofiles. pl. Discriminating hyperacetylated regions were identified using DESeq package (http://bioconductor. org/packages/ release/bioc/html/DESeq. html) and selection for regions with an FDR < 0. 01 and p-value < 0. 0002 between hrAPL and all other samples (APL and AML). Motif analysis was performed as before  [30].
SNP calling and identification of potential functional SNPs SNP calls were identified using the CNAG in-house pipeline based on WGBS data. The list of variant calls was reduced by applying a number of filters, starting from the number of tags (≥20) and Phred score (≥40) on the SNP calls from WGBS. The reduced list was then given as an input to the CHASM web server (http://www. cravat. us/ version 3. 1) for functional annotation and only the missense variants present in driver genes (as identified by Vogelstein)  [39]  at MAF ≤ 5% (1000 G) were selected. Further, only the variants reported in ChIP-seq and RNA-seq tracks by applying computational tools samtools (mpileup option) and GATK along with the variations confirmed through visual inspection of the tracks using IGV were selected. The data are publicly available and can be accessed via the Blueprint DCC: http://dcc.
ACKNOWLEDGMENTS AND FUNDING A special thanks to the generosity of the patients whose samples allowed the present study and discoveries. This work was supported by  BLUEPRINT  ( 282510 ), the  Italian-Flag Project-EPIGEN ,  PRIN-20152TE5PK , the Italian association against cancer ( AIRC-17217 ), the  Dutch Cancer Foundation  (KWF kun  2011-4937 ),  KIKA  (project  311 ) and  Netherlands organization for Scientific Research (NWO-VIDI  to JM).
Analysis of the Primary and Secondary Structures of VpPLD-WT and Its N-and C-Terminal Sequences The full-length sequence of VpPLD from Vibrio parahaemolyticus has been deposited in the NCBI-Protein databases under the accession number of EXJ48329. 1. The full-length sequence of VpPLD is composed of 505 amino acids (aa). By using SignalP 4. 1 server, the first 18 amino acids (MLHTLSKFIFAFMFSVLS) were predicted to be a signal peptide and indicated that the enzyme is an extracellular protein. The molecular mass and isoelectric point of the deduced mature VpPLD protein (VpPLD-WT) were predicted to be 55,239. 06 Da and 5. 35, respectively, by using the Compute pI/Mw tool at the ExPASy molecular biology web server of the Swiss Institute of Bioinformatics. Two highly conserved sequence motifs (HxK(x) 4 D(x) 6 G) comprising the active site residues were found in the VpPLD-WT enzyme, with histidine 152 and histidine 386 as the putative catalytic residues (Figure  1 ).
Analysis of the Primary and Secondary Structures of VpPLD-WT and Its N-and C-Terminal Sequences The full-length sequence of VpPLD from Vibrio parahaemolyticus has been deposited in the NCBI-Protein databases under the accession number of EXJ48329. 1. The full-length sequence of VpPLD is composed of 505 amino acids (aa). By using SignalP 4. 1 server, the first 18 amino acids (MLHTLSKFIFAFMFSVLS) were predicted to be a signal peptide and indicated that the enzyme is an extracellular protein. The molecular mass and isoelectric point of the deduced mature VpPLD protein (VpPLD-WT) were predicted to be 55,239. 06 Da and 5. 35, respectively, by using the Compute pI/Mw tool at the ExPASy molecular biology web server of the Swiss Institute of Bioinformatics. Two highly conserved sequence motifs (HxK(x)4D(x)6G) comprising the active site residues were found in the VpPLD-WT enzyme, with histidine 152 and histidine 386 as the putative catalytic residues (Figure  1 ).
Total Gene Synthesis of VpPLD and Construction of the Expression Plasmid The target PLD protein sequence from VpPLD researched in the present study was deposited in the NCBI Protein database under the accession number EXJ48329. 1. The deduced signal peptide was predicted by using SignalP 4. 1 server  [27]. The PLD gene encoding the mature peptide (with deletion of the first 18 amino acids (MLHTLSKFIFAFMFSVLS), named VpPLD-WT) was artificially synthesized according to the code usage of E. coli by Sangon Biotech, Inc. (Shanghai, China). The gene was ligated into the pET21a vector between the Nde I and Xho I restriction sites to yield the expression vector pET21a-VpPLD-WT-(His) 6 (C-terminal 6× His tag). Meanwhile, the gene was also ligated into the pET28a vector between the BamH I and Xho I restriction sites to yield the expression vector pET28a-(His) 6 -VpPLD-WT (N-terminal 6× His tag).
Modified continuous flow cells designed for photometrical biofilmdensity monitoring and DNA sequencing The standard continuous-flow setup (above) was modified to monitor the dynamics of biofilm dispersal photometrically as previously described  [22]. Briefly, the standard flow cells were replaced with similar-sized glass tubes (inner-diameter 2. 1 mm, 10. 0 cm in length) for the growth of the biofilms and an LED (580 ± 10 nm) with a photosensor (peak sensitivity at 600 nm) was installed to monitor the turbidity (OD 580 nm)/opacity through the glass tube (Fig. 1b ). The photometer signals were computerized using a data logger (Labjack U12, Meilhaus, Germany) and recorded every 5 min using DAQFactoryexpress software (Azeotech, Ashland, OR, USA). Each glass tube was inoculated with 0. 5 ml of overnight culture of mixed RO community adjusted to OD 600 0.
Acknowledgments The authors would like to acknowledge financial support from the  Environment & Water Industry Programme Office  (Project Ref:  1301-IRIS-57 ), and the  Singapore Centre for Environmental Life Sciences Engineering (SCELSE) , whose research is supported by the  National Research Foundation Singapore, Ministry of Education, Nanyang Technological University and National University of Singapore , under its  Research Centre of Excellence Programme  ( M103380001 ). Sequencing of DNA was carried out with the help of  Daniela Moses  and  Stephan Schuster  using the sequencing facilities at SCELSE. Microtiter plate experiment and RO system operation were carried out with the help of  Indah Mutiara Ningtyas Razaad ,  Ravindi Karunaratne , and  Rajan Chakravarthy. The surface zeta-potential and contact angle of membranes were measured with the help of  Jaewoo Lee.
Adrenocortical carcinoma (ACC) and pheochromocytoma and paraganglioma (PPGL) are defined by clinicopathological criteria and can be further sub-divided based on different molecular features. Whether differences between these molecular subgroups are significant enough to re-challenge their current clinicopathological classification is currently unknown. It is also not fully understood to which other cancers ACC and PPGL show similarity to. To address these questions, we included recent RNA-Seq data from the Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets. Two bioinformatics pipelines were used for unsupervised clustering and principal components analysis. Results were validated using consensus clustering model and interpreted according to previous pan-cancer experiments.
Two datasets consisting of 3319 tumors from 35 disease categories were studied. Consistent with the current classification, ACCs clustered as a homogenous group in a pan-cancer context. It also clustered close to neural crest derived tumors, including gliomas, neuroblastomas, pancreatic neuroendocrine tumors, and PPGLs. Contrary, some PPGLs mixed with pancreatic neuroendocrine tumors or neuroblastomas. Thus, our unbiased gene-expression analysis of PPGL did not overlap with their current clinicopathological classification. These results emphasize some importances of the shared embryological origin of these tumors, all either related or close to neural crest tumors, and opens for investigation of a complementary categorization based on gene-expression features.
Similarly, PPGLs are separated into 4 groups named after their molecular characteristics: pseudohypoxia related to succinate dehydrogenase or VHL/EPAS1 disturbances, wnt-altered and kinase-signaling pathways  [7] [8] [9]. New approaches and methods for analysis of molecular pan-cancer datasets may obtain novel insights into the characteristics of a wide range of neoplasms in a single experiment. Their results can be used to test whether the current clinicopathological classification of a particular tumor remains relevant on a molecular level  [10, 11]. Current state of the art and views suggest that a majority of tumor types categorize accordingly to their established clinicopathological classifications in such pan-cancer analyses  [11]. However, an alternative scenario where new molecular analyses proposed a new disease categorization has been shown for some cancers  [12].
Aim 1: To Determine if ACC and PPGL Show Integrity in a Transcriptomic Pan-Cancer Context To address whether the current clinicopathological classification of ACC and PPGL remains relevant in a transcriptomic pan-cancer context, we performed unsupervised clustering and principal component analyses. RNA-seq data from 3319 tumor samples of 35 different categories from the Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) (Figure  1A , Table  1 ) were included. Cases were grouped by TCGA tumor category and genes with high variability between tumor categories were extracted. Dendrograms of unsupervised clustering showed integrity of both ACC and PPGL which formed two separate clusters (Supplementary Figures  S1A-C  and S2 ). In the second series of experiments we analyzed the dataset on a per sample basis.
9, 4th lowest among ACCs (Figure  2 ). Analysis of all ACCs and all SARCs available in TCGA that showed that OR-A5J8 clustered to the SARC group (Supplementary Figure  S5A-C ). The second sarcomatoid ACC available in the TCGA dataset clustered among ACCs. Detailed Analysis of ACC and PPGL Outliers ACC and PPGL samples that clustered outside their disease group in both bioinformatics pipelines were carefully examined. There was one ACC, OR-A5J8, of sarcomatoid type with 100% purity that clustered among sarcomas (SARC). It showed a cortical differentiation score of 7. 9, 4th lowest among ACCs (Figure  2 ). Analysis of all ACCs and all SARCs available in TCGA that showed that OR-A5J8 clustered to the SARC group (Supplementary Figure  S5A-C ). The second sarcomatoid ACC available in the TCGA dataset clustered among ACCs.
Discussion In this study we used a pan-cancer model to investigate the degree of overlap between unbiased gene-expression clustering to the current clinicopathological classifications of ACC and PPGL. A second aim was to investigate which other cancer types these two diseases show similarity to. We found that ACC was a homogenous transcriptomic group that showed a surprising association with neural crest derived tumors. PPGLs mixed with either pancreatic NETs or NBL. In addition, it also clustered together with GBM and LGG as well as ACC. The unique aspect of this study is the combination of two datasets that together has a high number of samples from many different tumor types. The included data provides a very comprehensive characterization of gene expression that has the highest standard for quality control.
Specifically, we used the publically available TCGA and TARGET resources to include 3319 tumor samples originating from 35 different tumor types. The most important and novel aspect came from merging a large number of NBLs (TARGET cohort) with other neural crest tumors, including GBM, LGG, PNET, and PPGL (TCGA cohort). Our first aim was to determine whether findings from an unsupervised clustering of a pan-cancer gene expression dataset overlapped current clinocopathological classifications of ACC and PPGL. Through eight different unsupervised clustering experiments as well as principal component analysis and consensus clustering, we found that both ACCs and PPGLs were relatively homogenous groups of diseases, but that PPGLs mixed with either NBLs or PNETs. As such, our unbiased clustering based on gene expression features did not fully overlap with the current clinicopathological classification of PPGL.
Methods TCGA and TARGET datasets are well suited for comparative studies due to (1) the high number of samples across different disease categories, and (2) the extraordinary standardization in data generation including tissue collection, genome analysis as well as bioinformatics processing. All cases were annotated accordingly to established clinicopathological classifications used in the TCGA and TARGET cohorts (Table  1 ). ACCs and PPGLs were further annotated accordingly to the molecular classifications proposed in the respective TCGA projects  [5, 9]. We selected mRNA expression as a proxy of tissue biology due to the gene-specific correlation between mRNA and protein levels  [48]. This study falls under an approval from the regional Ethics Committee in Uppsala (544/2015).
Sample Selection and Annotation Publicly available level 3 data; RNA-seq V2 and clinical annotations available through TCGA and TARGET consortiums were downloaded 2017-08-23 from Genomics Data Commons https://gdc. cancer. gov (Table  1 ). In the pan-cancer experiments each cohort was restricted to a maximum of 100 samples in order to limit the size for the dataset and to balance the relative weight in-between subgroups. All samples were included for two cohorts; PPGLs (n = 179) and NBL (n = 156). A total of 8 samples with histopathology of neuroendocrine tumors were included from the pancreatic ductal adenocarcinoma project and annotated as PNET  [31]. Samples from primary tumors (01A) were prioritized. For RNA-seq analysis FPKM normalized files were selected and annotated on a per transcript basis for further analysis.
Unsupervised Clustering Files were imported into the Subio Platform version v1. 21. 5074 (Subio Inc, Kagoshima, Japan, https://www. subioplatform. com). FPKM transcript counts were subjected to Log2 normalization and genes with low expression values (mean FPKM < 1) were discarded. Genes with high standard deviation of expression in-between (1) TCGA categories, or (2) individual samples were selected for further analysis, three different datasets with different standard deviation thresholds was used for each unsupervised clustering experiment. Unsupervised clustering was performed using a Spearman test as the distance metric. Subio Platform raw data figures were imported into Microsoft PowerPoint (Microsoft Inc, Redmond, WA, USA) and edited for improved readability.
Consensus Clustering The dataset comprising 2262 genes identified through extraction of mRNAs with a high variance in expression in-between samples through bioinformatics pipeline 1 was used. Consensus clustering was performed using the ConsensusClusterPlus R package  [52]. One thousand iterations were performed with a sample inclusion probability of 0. 8 and an item inclusion probability of 1. The number of clusters was selected based on inspection of the Delta CDF plot.
Adrenal Medulla and Cortex Differentiation Scores A dataset of 2262 genes identified through extraction of mRNAs with a high deviance in expression through bioinformatics pipeline 1 was used to select transcripts that were previously identified as preferentially expressed in either adrenal cortex or medulla from the proteinatlas. org [53]. Twenty-two transcripts were selected, 11 from adrenal medulla and 11 from adrenal cortex. The dataset was analyzed using Gene Set Enrichment Analysis version 9. 09 on the gene pattern platform (https: //genepattern. broadinstitute. org) [54, 55]. Samples were normalized to log-scale and analyzed with default settings.
Identification of Transcripts Shared between Cancer Types The dataset comprising 2262 genes identified through extraction of mRNAs with a high deviance in expression through bioinformatics pipeline 1 was used. Genes with area under the Receiver Operating Characteristic (ROC) curve (Harrell's C-statistic) of >0. 9 for ACC, GBM, LGG, PPGL, NBL, and PNET versus remaining tumor types were selected. Difference between median gene expression value in ACC compared to (1) GBM, LGG, NBL, PNET and PPGL, and (2) remaining tumors were determined. Genes with fold change in ACC of <0. 1 (compared to GBM, LGG, NBL, PNET and PPGL) and <2 (compared to remaining tumor types) were excluded. Remaining genes were annotated for overlapping gene ontology annotations version 2018-05-07 (https://www. ebi. ac. uk/QuickGO/).
Principal component analysis of 3319 samples with selected tumors annotated for TCGA or TARGET tumor category, Figure S5: Bioinformatics pipeline 1. Unsupervised hierarchal clustering of all ACC and SARC samples available in the TCGA database, Figure S6: Bioinformatics pipeline 1. Unsupervised clustering of 35 TCGA tumor categories as well as 8 PAAD samples annotated as PNET, Figure S7: Bioinformatics pipeline 2. 1975 transcripts selected. Unsupervised clustering of 35 TCGA tumor categories as well as 8 PAAD samples annotated as PNET, Figure S8: Bioinformatics pipeline 1. Unsupervised hierarchal clustering based on transcriptome data from 35 TCGA tumor categories with adrenal tumors annotated accordingly to molecular subtype. All samples in the PPGL cohort labeled as pheochromocytoma were removed, Figure S9: Consensus matrices of unsupervised cluster of cluster classification of 3319 TCGA and TARGET samples, Figure S10: Bioinformatics pipeline 1.
Page 4 Figure 1. 41 Figure 1. Pan-cancer dataset and transcriptomic classification. (A) Pan-cancer analysis dataset and pipeline. Results from bioinformatics pipeline 1. (B) Unsupervised hierarchal clustering of RNA-seq data from 3319 TCGA and TARGET samples annotated for cancer type processed by bioinformatics pipeline 1. Abbreviations; ACC, Adrenocortical Carcinoma; GBM, Glioblastoma Multiforme; LGG, Brain Lower Grade Glioma Neuroblastoma; PNET, Pancreatic Neuroendocrine Tumor; PPGL, Pheochromocytoma and Paraganglioma; Cortical, Cortical Admixture PPGL; Hypoxia, Pseudohypoxic PPGL; Kinase; Kinase signaling PPGL and Wnt, wntaltered PPGL.
This approach, recently applied to ecology, has generated numerous novel insights into the understanding of the structure, function, and dynamics of systems  21, 22. Here, we applied network analyses to describe and analyze association patterns between biodiversity threats and extinct or threatened insular species. Species and threats are considered to be nodes, and connections between these two types of entities are established if a threat is responsible for the population decline of a given species. Using a comprehensive dataset of insular biodiversity and threats from the IUCN Red List  23  , we considered 11 categories of threat identified for more than 4,350 species of eight higher taxa distributed across 15 insular regions of the entire world. Identifying patterns of association between threats and species at macro-ecological scales provides unique insights into the layout of global threats across distinct taxa and specific regions, an important undertaking if we are to design appropriate actions to limit the current biodiversity crisis.
For example, such an interaction was observed between body mass and hunting vulnerability in Chinese birds  41. Also, to explain the differences in threats to extinct versus threatened species, two explanations can be proposed. First, it could be due to differences in information, with historical knowledge lacking for some extinct species (see below). Second, it could also be associated with the vulnerability of species, whereby the species most vulnerable to certain threats would be first to become extinct, thus leaving a greater number of less vulnerable species, as has been shown for birds and biological invasions  19, 20. Although the IUCN Red List is the most comprehensive assessment of species that are at risk of extinction worldwide  42, 43  , this dataset has several shortcomings. Firstly, small species with narrow distribution ranges and low dispersal abilities, which are mostly local endemics, are underassessed  44.