dataesr/scientific-paragraphs-categorization huggingface dataset:
Discordant read pairs are paired read ends that map uniquely to different protein-coding genes with orientations consistent with formation of a sense-sense chimera. Junction-spanning reads are detected by constructing a sequence database that holds all possible exon-exon junctions that match the 3′ end of one gene fused to 5′ end of a second gene. Unmapped reads aligned to the database of all hypothetical exon junctions created by using the Ensembl transcriptome reference. Only reads for which the mate pair maps to either of the two fusion partner genes are considered as fusion transcripts. In this study, we extracted fusions with (1) at least two discordant read pairs, (2) at least one junction spanning read and (3) without high gene homology between each fusion gene partner (BLAST E-value > 0.
001). Next, we applied the concept of mutation allele fraction to RNA sequencing data, and calculated the ratio of junction-spanning reads to the total number of reads crossing over the junction point in the reference transcript. We used the transcript allele fraction (TAF) to exclude artifacts that depend on highly expressed transcripts. We included fusion transcripts showing TAF > 0. 01 of both genes in our fusion list. In addition, we filtered out fusions that are found in normal TCGA samples. Briefly, MapSplice_2_0_beta_7_21 identifies fusion candidates as any two segments of a read alignment that were (1) separated by a gap longer than 200,000 nt, or (2) were on different chromosomes, or (3) were on different strands, or (4) mapped to discordant locations (i. e. the apparent direction of transcription changes between the segments).
From the FPKM matrix for the 80 tumor samples, we extracted 8,167 genes with "lincRNA" and "processed_transcript" Ensembl biotypes. miRNA Sequencing: We generated miRNA sequencing data using methods described previously  (Chu et al. , 2016) , except that 1ug of total RNA (at 250ng/uL) was used as input instead of messenger RNA-depleted RNA. Briefly, we aligned reads to the GRCh37/hg19 reference human genome, assigned read count abundances to miRBase v16 stem-loops and mature strands, and assigned miRBase v20 5p and 3p mature strand names to MIMAT accession IDs. While we used only reads with exact-match alignments in calculating miRNA abundances, BAM files available from the Genomics Data Commons (https:// gdc. cancer. gov/) include all sequence reads. Unsupervised Clustering: We extracted 356 lncRNAs that were robustly expressed (mean FPKM ≥ 1) and highly variable across the n = 80 tumor cohort (≥ 95th FPKM variance percentile) from the matrix of 8,167 lncRNAs (above).
For lncRNAs with larger SAM scores, a q-value of ≤ 0. 01, and a mean FPKM ≥ 5, we set the columns of the FPKM data matrix to the heatmap order, transformed each row of the matrix by log 10 (FPKM + 1), then used the pheatmap R package (v1. 0. 2) to scale and cluster only the rows, using a Pearson distance metric and Ward clustering. For miRNA sequencing data we used unsupervised non-negative matrix factorization (NMF) consensus clustering (v0. 20. 5) in R 3. 1. 2, with default settings  (Gaujoux and Seoighe, 2010). The input was a reads-per-million (RPM) data matrix for the 303 (25% of 1212 miRBase v16) most-variant 5p or 3p mature strands. After running a rank survey for between 2 and 15 clusters with 50 iterations per solution, we identified a clustering solution for more detailed work by assessing profiles of the cophenetic correlation coefficient and the average W cm , KM plots, and clinical covariate associations, then performed a 500-iteration run to generate the final clustering result.
To visualize typical vs. atypical cluster members, we calculated a W cm from the final NMF consensus membership matrix. To generate a clustering heatmap. we used a SAM  (Li and Tibshirani, 2013 ) (samr v2. 0) multiclass analysis with an RPM input matrix and an FDR threshold of 0. 05 to identify mature strands whose abundance varied across the clusters. For mature strands with larger SAM scores and a mean RPM ≥ 25, we set the columns of the RPM data matrix to the heatmap order, transformed each row of the matrix by log 10 (RPKM + 1), then used the pheatmap R package (v0. 7. 7 or v1. 0. 2) to scale and cluster only the rows, using a Pearson distance metric and Ward clustering. The RPM filtering acknowledged that more abundant miRNAs are more likely to be influential  (Mullokandov et al. , 2012).
3) and unmethylated tumor samples (β < 0. 1) should be significant at an FDR < 0. 01. A gene was defined as epigenetically silenced if at least 25% of the promotor CpG sites met all of these criteria. A total of 120 normal samples were used for this analysis, including 10 drawn at random from each of the 12 TCGA projects that include normal samples, such lung adenocarcinoma, breast invasive carcinoma, colon adenocarcinoma, endometrial carcinoma, and others (https://tcga-data. nci. nih. gov/docs/publications/). We estimated leukocyte fraction using an approach described in  (Carter et al. , 2012). As a source of leukocyte DNA methylation levels, we used data for peripheral blood mononuclear cells (PBMC) from six healthy donors  (Reinius et al. , 2012)  (GEO: GSE35069). We identified 36 mRNAs, 65 lncRNAs and 94 miRNAs that were statistically associated with local DNA methylation.
Low-Pass Whole Genome Sequencing Library Construction: Approximately 500-700 ng of genomic DNA from fifty randomly selected tumor and matched normal pair samples were individually sheared into fragments of approximately 300 bp using an E220 Focused-ultrasonicator (Covaris). These fragments were made into paired-end libraries using KAPA Bios kits in a Sciclone NGS Workstation (Caliper/Perkin Elmer) according to manufacturers' protocols. Libraries were sequenced using an Illumina HiSeq 2000, one sample per lane, with a paired-end 2× 51 bp setup. The average depth of coverage was approximately 4. 9X, with a minimum of 1. 56X and maximum at 8. 17X. The average genome coverage was 89. 05%, with a minimum of 71. 87% and maximum of 92. 12%. Raw data was converted to FASTA format, and the Burrows-Wheeler Aligner used to generate BAM files.
Slides were probed with 220 validated primary antibodies (Table  S3 ) followed by corresponding secondary antibodies. Signal was captured using a DakoCytomation-catalyzed system and DAB colorimetric reaction. Slides were scanned in a CanoScan 9000F. Spot intensities were analyzed and quantified using Array-Pro Analyzer (Media Cybernetics, Washington DC) to generate spot signal intensities (Level 1 data). The software SuperCurveGUI  (Hu et al. , 2007) , available at http:// bioinformatics. mdanderson. org/Software/supercurve, was used to estimate the EC50 values of the proteins in each dilution series (in log2 scale). Briefly, a fitted curve ("supercurve") was plotted with the signal intensities on the Y-axis and the relative log2 concentration of each protein on the X-axis using the non-parametric, monotone increasing B-spline model  (Tibes et al.
, 2006). During the process, the raw spot intensity data were adjusted to correct spatial bias before model fitting. A QC score was calculated for each slide to help determine the quality of the slide: if the score was less than 0. 8 on a 0-to-1 scale, the slide was dropped. In most cases, the staining was repeated to obtain a high quality score. If more than one slide was stained for an antibody, the slide with the highest QC score was used for analysis (Level 2 data). Protein measurements were corrected for loading as described  (Gonzalez-Angulo et al. , 2011; Hu et al. , 2007)  using median centering across antibodies (level 3 data, described later). In total, 220 antibodies and 12 UM samples were processed on the RPPA platform. Final selection of antibodies was also driven by the availability of high quality antibodies that consistently pass a strict validation process.
These antibodies are assessed for specificity, quantification and sensitivity (dynamic range) in their application for protein extracts from cultured cells or tumor tissue. Antibodies are labeled as validated and use with caution based on degree of validation  (Gonzalez-Angulo et al. , 2011). RPPAs were quantitated and processed (including normalization and load controlling) as described previously, using ArrayPro Analyzer software (Media Cybernetics, Washington DC) and SuperCurve v1. 3, available at http://bioinformatics. mdanderson. org/OOMPA. Raw data (level 1), SuperCurve nonparameteric model fitting on a single array (level 2), and loading-corrected data (level 3)  (Ju et al. , 2015; Zhang et al. , 2009)  were deposited at the TCGA Data Coordinating Center (DCC).
Regulome Explorer-To gain greater insight into the development and progression of uveal melanoma, we have integrated all of the data types produced by TCGA and described in this paper into a single "feature matrix". From this single heterogeneous dataset, significant pairwise associations have been inferred using statistical analysis and can be visually explored in a genomic context using Regulome Explorer, an interactive web application (http://explorer. cancerregulome. org). In addition to associations that are inferred directly from the TCGA data, additional sources of information and tools are integrated into the visualization for more extensive exploration (e. g. , NCBI Gene, miRBase, the UCSC Genome Browser, etc).
Pathways were obtained in BioPax Level 3 format, and included the NCIPID and BioCarta databases from http://pid. nci. nih. gov and the Reactome database from http://reactome. org. Gene identifiers were unified by UniProt ID then converted to Human Genome Nomenclature Committee's HUGO symbols using mappings provided by HGNC (http:// www. genenames. org). Altogether, 1,524 pathways were obtained. Interactions from all of these sources were then combined into a merged Superimposed Pathway (Super-Pathway). Genes, complexes, and abstract processes (e. g. "cell cycle" and "apoptosis") were retained and are henceforth referred to collectively as pathway "features". The resulting pathway structure contained a total of 19,504 features, representing 7,369 proteins, 9,354 complexes, 2,092 families, 82 RNAs, 15 miRNAs and 592 abstract processes.
Creating a Curated Transcription Factor (TF) Regulome: A compendium of TFs and their targets (TF regulons) were created by combining information from four databases: i. SuperPathway  (Sedgewick et al. , 2013) : This is the same interaction network used in the PARADIGM analysis (above). Only links that correspond to regulation at the transcriptional level were retained for MARINa and hMARINa use. ii. Literome  (Poon et al. , 2014) : The network was filtered to include only transcription links in which the regulator is a known TF. iii. Multinet  (Khurana et al. , 2013) : The network was reduced to links that correspond to regulation on transcriptional level.
iv. ChEA  (Lachmann et al. , 2010) : Data from the Gene Expression Atlas  (Petryszak et al. , 2014)  was used to filter the inferred links in the ChEA database. Specifically, the context likelihood of relatedness (CLR) method  (Faith et al. , 2007)  was used to compute a measure of association between every pair of genes. The top 10% of gene pairs based on the CLR score were intersected with the ChEA network and the overlapping pairs were added to the final combined network. The combined network includes 72,915 transcriptional regulatory links between 6,735 regulators and their targets. Only regulators with at least 15 targets were considered in the final analysis, which resulted in a final network consisting of 419 TFs with 58,363 total targets (covering a set of 12,754 unique targets). Creating a Curated Kinase Regulome: Proteins identified as kinases in Manning  (Manning et al.
, 2002)  or Uniprot (UniProt Consortium, 2014) were aggregated into a list of 546 kinases. Protein substrates were extracted from PhosphositePlus  (Hornbeck et al. , 2014)  on March 7, 2015. Kinase-substrate interactions were retained if the kinase appeared in the Manning-Uniprot kinase list and the kinase was identified as a human protein in the PhosphositePlus database. The final compendium consisted of 5,388 links between 342 kinases and 2,260 unique substrates. MARINa Estimate of TF Activity: MARINa regulator activity scores predict each TF's relative activity as a contrast between two cohorts of interest. The activity score is derived from a combined view of the expression levels of each TF's transcriptional targets (the TF regulon), based on the following steps: i. The TF regulon is split into positively-and negatively-regulated sets by measuring the Spearman correlation between the expression of the TF and that of each of its targets.
Relationship of Fold Change between TCGA SCNA Clusters 3 vs. 4, and Association with Time to Metastasis in TCGA and Laurent Monosomy 3 Cases -We processed Laurent microarray expression data (GEO: GSE22138)  (Laurent et al. , 2011)  to 23,520 expression records, using the probe with the highest cohort variance when a gene symbol had data for more than one microarray probe (e. g. for CD44, given 13 probes we used 229221_at; for MALAT1, given 12 probes we used 224559_at). We then used Ensembl v82 gene symbols and bio-types for 20,425 protein-coding genes and 8,167 lncRNAs or processed transcripts ('lncRNAs') to identify 17,525 expression records for coding genes and 1,227 records for lncRNAs in Laurent data. Of the 63 Laurent cases with clinical data, we retained the 32 monosomy 3 cases. These included 22 with metastasis and 10 without, and had a median event time of 20.
4 months. We identified genes that were variably expressed in these 32 samples, finding 13,142 coding gene records above a mean abundance of 2. 0 and above the 25 th percentile in variance, and 736 lncRNA records with a mean abundance above 1. 5 and a variance above the 40 th percentile. Similarly, for 33 TCGA cases had metastasis data, thresholding 20,531 RSEM genes on the 40 th percentile (50. 3) and 50 th variance percentile retained 12,319 variably expressed mRNAs. For 8,167 Ensembl v82 lncRNAs and processed transcripts ('lncRNA'), FPKMs for which we calculated from the mRNA sequence data, thresholding on the 80 th mean FPKM percentile (0. 087) and 75 th variance percentile retained 1,634 variably expressed noncoding genes. To identify genes that were associated with time to metastasis in M3 cases, we censored time and status to 5 years for the 32 Laurent and 33 TCGA records.
The upper heatmap shows unsupervised consensus clustering for RNA-seq data of mRNA (left) or lncRNA (right) expression. Covariate annotation tracks show selected genomic and clinical features. The lower heatmap displays the expression profiles of 12 genes used in a prognostic test for the risk of developing metastasis  (Harbour, 2014) , with blue text highlighting genes that are on chromosome 3. High-risk primary tumors show low expression of eight of these genes and high expression of four genes (yellow versus green panels at the left). BAP1 structural alterations that include alternative splicing and rearrangements were detected by assembly of RNA-seq and DNA-seq data. Leukocyte fraction was estimated from DNA methylation data. LOH, loss of heterozygosity. S3. S4. S5  and S6.
Acknowledgments We are grateful to all patients and families who contributed to this study. We thank  Dr. Jasmine Francis ,  Dr. Amy Schefler  and  Dr Helen Kalirai  for following through with the requirements for sample submission. This work was supported by the following grants from the  NIH :  U54 HG003273 ,  U54 HG003067 ,  U54 HG003079 ,  U24 CA143799 ,  U24 CA143835 ,  U24 CA143840 ,  U24 CA143843 ,  U24 CA143845 ,  U24 CA143848 ,  U24 CA143858 ,  U24 CA143866 ,  U24 CA143867 ,  U24 CA143882 ,  U24 CA143883 ,  U24 CA144025 ,  P30 CA016672 ,  P50 CA083639 , and  K08 EY022672  (C. M. C. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH. E. A. G. is an employee of  GenomeDX Biosciences. A. D. C. declares research funding from  Bayer AG.
The results showed that mice treated with DMSO needed more time to locate buried food than mice treated with saline solution (372 s vs. 157 s respectively, p= 0. 0078, Mann-Whitney, test Figure  2B ). However, 7 and 21 days after the last treatment, no difference remained between the two groups (Figures  2C  and 2D ), indicating that intranasal infusions of DMSO had only temporary short-term side effects on the sense of smell of the animals. Finally, we assessed olfactory functions of mice engrafted with TG16 that survived at 195 days post graft. We did not find any statistical differences between control and TMZ-treated mice for the time spent to locate buried food (177 s ± 139 s vs. 157 s ± 133 s respectively, p=0. 6905, Figure  2E ). These data indicate therefore that intranasal treatments with TMZ did not induce a long-term impairment of the olfactory function.
Histopathological analysis Brains were post-fixed for 2 h in 4% PFA, cryoprotected in increasing concentration of sucrose (10% to 30% in PBS). Serial coronal cryostat sections of 20 mm (200 mm apart) were made and mounted on polysine slides (Thermo Scientific, Braunschweig, Germany) for Haematoxylin-Eosin staining. Tumour volumes were calculated by multiplying the sum of all sectional areas (square millimeters) by the distance between successive sections as described previously  [19]. Images acquisition were obtained using a UPlanFI 4X dry objective of 0. 13 numerical aperture using a motorized microscope Olympus BX51 equipped with a Sony ExwaveHAD 3CCD DSP camera and the software Histolab 7. 6. 1 (Microvision Instruments, Evry France).
Acknowledgement We are indebted to  C. Joubert ,  V. Neuville  and all of the staff of the animal facilities. O. Etienne  and  A. Gouret  for help in mice weight and to  T. Kortulewski  and  G. Livera  for imaging facility. This study was supported by grants from  Inca (Tetratips ,  PLBIO10-030 ),  Fondation de France  (N° Engt : 2013-00042632 ). J. R. P. had a fellowship from  Fondation de France  and  Ramón y Cajal program  ( RYC-2013-13450 ),  M. J. has a fellowship from  Ligue Nationale Contre le Cancer  and from  IRTELIS-CEA.
Introduction Dissections were performed as in  Lafontaine (2004). Morphological, including genitalia, terminology follows  Kristensen (2003). Not all genitalia were prepared on slides to allow for three-dimensional analysis of the complex male genitalia. Genitalia and abdomens, when not slide mounted, are preserved in glycerol filled microvials. Specimens from the following collections were examined: The symbol ‡ will be used to represent unavailable names in the text  (Fletcher and Nye 1982). Figures were manipulated with Adobe Photoshop CS4 (Adobe 2008). Male genitalia are figured in natural color with CS4 "auto color" used to improve white backgrounds. All geographical coordinates are approximate, and are based on the localities provided on specimen labels. GPS data were acquired with Google Earth.
Acknowledgements We would like to thank the individuals and their respective institutions for allowing access to specimens used in this research, these include:  Alessandro Giusti  (NHMUK),  Patricia Gentili-Poole and Brian Harris (USNM) ,  Carlos G. C. Mielke  (CGCM and DZUP),  Alexey Prozorov (MWM) ,  Harald Sulak  (MWM),  Thomas Witt  (MWM), and  Andrew Warren  (ADW). Jason Dombroskie (CUIC) facilitated access to specimens and camera equipment. We would also like to thank  Ana P. S. Carvalho  ( University of Florida ) and  Santos Braz  ( Brazil ) for facilitating access to private lands on which to photograph living V. mediorosea. Ulf Drechsel  and  P. Thiaucourt  for providing material from  Paraguay. Finally , we thank  Roger Hutchings  (Brazil),  Andrew Mitchell  (Australia), and an anonymous reviewer for their suggestions.
Stimuli Stimuli were 200 color images of various non-face objects (e. g. , animals, plants, manmade structures/objects) and 100 color images of human faces. We resized each image to 256×256 pixels and equalized their mean luminance values in MATLAB (R2012b). As in our previous studies that used a different but comparable stimulus set (e. g. ,  Rossion et al. 2015) , we deliberately did not segment faces/objects from their original naturalistic backgrounds, such that the viewpoint, lighting, and composition images varied widely across the full stimulus set (see Figure  1  for examples). During the stimulation sequence, each image appeared on a uniform grey background and subtended 8. 721˚ of visual angle. A small central fixation cross (subtending 0. 788˚ of visual angle) overlaid the images throughout the sequence.
Procedure Participants sat in a darkened room and viewed a computer monitor at a distance of 80cm. Each stimulation sequence consisted of a two second pre-stimulation period that contained only the central fixation cross, a two second fade-in, a 90 second sequence of rapid images presented at a periodic rate of exactly 12 Hz (12 images per second), and a three second fade-out. We achieved this 12 Hz presentation rate using customized software programmed in Java that sinusoidally modulated the contrast of each image from 0% to 100% to 0% over a period of 83. 33 ms (see Figure  1 ; Movie 1). Since the stimuli are still visible at low contrast, sinusoidal contrast modulation ensured that the stimulation was near-continuous, such that participants did not perceive any interruption between images (see  Retter and Rossion 2016 , for a comparison of sinusoid and squarewave stimulation at 12.
EEG Analysis. We analyzed EEG data using the open source toolbox Letswave5 (http://nocions. webnode. com/letswave) running over MATLAB R2012b (MathWorks, USA), and R (https://www. r-project. org/). We note that all EEG waveform analysis procedures described here have also been extensively documented in recent publications  (Liu-Shuang et al. 2014; Rossion 2014; Rossion et al. 2015; Jacques et al. 2016; Retter and Rossion 2016).
In each of our three ROIs, we compared the conditional mean waveforms by conducting a paired t-test at each of the 513 time points. We inspected these obtained t-values relative to two significance criteria (p <. 01 uncorrected, and p <. 05, corrected for multiple comparisons). We corrected for multiple comparisons using a permutation procedure described by  Blair and Karniski (1993;  see also  Finkbeiner and Friedman 2011)  which maintains experiment-wise error in ERP analysis. The steps for this procedure were as follows. First we generated 100,000 permutations of our data by systematically shuffling the periodic/nonperiodic labels for each participant's conditional mean datasets (consisting of 513 time points). Assuming the null hypothesis is true (i. e. , there is no difference between periodic and nonperiodic waveforms), then the assignment of condition labels will be arbitrary, such that each instance of permutated data is just as likely to have occurred as our actual obtained data.
In this way, the permuted data arrangements represent 100,000 possible outcomes of our experiment. For each permutation, we conduct a paired t-test at every time point (n = 513, for a total of 51. 3 million comparisons) and keep aside the maximum t-value (t|max|) given by that permutation. By rank ordering the t|max| values obtained across all permutations, we form a reference distribution against which the obtained test statistic at each time point can be compared. To maintain experiment-wise α at 0. 05, we set our critical t to be the value in the t|max| reference distribution that cuts off 0. 025 of each tail (see  Blair and Karniski, 1993  for a more detailed explanation of the rationale behind this procedure). We assessed the similarity/difference of the time-domain response to periodic and nonperiodic faces in a second way using multivariate pattern classification (MVPA).