dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We wish to thank  G. Mazzamuto ,  F. Utel  and  J. Bertolotti  for technical assistance and  T. Svensson ,  E. Alerstam ,  S. Gigan  and  P. Wasylczyk  for fruitful discussions. This work is financially supported by the  European Network of Excellence Nanophotonics for Energy Efficiency  and the  ERC  through the  Advanced Grant PhotBots  (proj. ref.
Data Collection Recordings were made with a Sennheiser ME-67 directional microphone (frequency response 40-20000 Hz AE 2. 5 dB; Sennheiser Electronic GmbH & Co. KG, Wedemark, Germany) powered by a LR6 battery, connected to a Zoom H4N digital recorder (48 kHz sampling frequency and 16-bit quantization; Zoom Corporation, Tokyo, Japan); these recordings were stored as uncompressed WAV files. For shock and wind-noise reduction, the microphone was mounted on a Rycote Modular Windshield WS 7 Kit for Shotgun Microphones, which includes a windshield with suspension system and a synthetic fur windjammer (Rycote Microphone Windshields Ltd, Stroud, UK). At EP2, recording sessions were made from locations around the edges of the enclosures, mostly at dawn and dusk. At the much larger EP1, recordings were made day and night, whenever individuals showed up to the feeder, mostly from inside the first author's car parked 15-25 m away from the feeder, with the microphone protruding from the window and directed toward the group.
This resulted in the initial identification of five acoustically based categories of calls: grunts, squeals, grunt-squeals, barks, and trumpets-see all but barks in Fig. 1  (barks were eventually removed from the dataset in this study-see below for further details). Basic call categories  Klingholz et al. (1979)  divided wild boar vocalizations into ten call types distributed over three categories (see Introduction). While relying on these three categories to assess the calls recorded in this study, we deemed their subdivision into ten call types too subjective to be followed consistently. For example, what they defined as 'voiced' and 'unvoiced' sounds (or, respectively, grunts and squeals/screams) are perceptually very distinct, while the perceptual distinction between adult 'defense', 'fight', and 'hunger' calls (all considered as different subtypes of the 'unvoiced' category) was ambiguous.
Call duration (DUR) and spectral energy quartiles (Q25, Q50 and Q75) were extracted using a custom-built Praat script written by David Reby. The remainder of the acoustical analysis was conducted using the MIR Toolbox 1. 4. 1  (Lartillot et al. 2008)  in MATLAB (V. 7. 11. 0. 284, R2010b, The MathWorks, Inc. , Natick, MA, USA), using an analysis window of 1024 samples (~21 ms at a sampling rate of 48000 Hz) with a 50% overlap between adjacent analysis windows. Each consecutive portion of the acoustic signal was multiplied by a Hamming window before calculating a 1024-point discrete Fourier transform. The following parameters were extracted for each analysis window: mean dominant frequency (DF), spectral centroid (SC), spectral entropy (ENT), coefficient of variation of the root-mean-square of the amplitude (CVA), spectral flatness (FLAT), spectral flux (SF), and zero crossing rate (ZC).
Statistical Analysis To assess the validity of the initial perceptual classification, made after listening to the recordings and visually inspecting the sound spectrograms, two series of statistical validations were run. First, a multinomial logistic regression was performed to identify the set of parameters that best fit the perceptual classification. Based on the selected parameters, a hierarchical cluster analysis was conducted, the output of which was compared with the perceptual classification. Statistical analyses were conducted in SPSS Statistics (V. 21. 0) and in MATLAB. Two-tailed p-values are reported and significance levels were set at 0. 05. The statistical distribution for each of the 19 extracted parameters was tested for normality using Shapiro-Wilk tests in conjunction with inspection of Q-Q plots.
Acknowledgements We confirm that none of the authors has a conflict of interest to declare. Animal capture and housing at Soci et e Eric Pradat (Ets. Pradat) were in accordance with the legislative requirements of the  French prefecture de l'Indre. As the present study consisted solely of behavioral observations and non-invasive acoustic recordings, there were no negative animal welfare implications. During the study, we adhered to the 'Guidelines for the treatment of animals in behavioral research and teaching ' (Anim. Behav. , 2006, 71,  245-253 ). The described protocol was reviewed and approved by the  Ethical Review Committee of the University of Vienna  (Ethics approval number  2015-004 ). We are grateful to the  Ets. Pradat  for providing full access to their facilities. We thank the  European Science Foundation  for funding MG throughout the field season (' Advances in Farm Animal Genomic Resources '  Research Networking Programme, Exchange Grant   4547 ).
Cervical samples, i. e. normal cervical tissues (N = 2) and cervical tumors (N = 5), were collected from patients treated at the Department of Obstetrics and Gynecology, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague in 2012. All patients enrolled in the study signed the informed consent form. The study received official institutional and ethical approval from the Motol University Hospital and Institute of Hematology and Blood Transfusion. For patients from whom FF tonsillar tumor samples were obtained, data on demographics and clinical pathological characteristics were completed for each patient and collected by a questionnaire. All data are summarized in Additional file 1: Table  S1. All patients were prospectively followed up. The sampling and tissue handling for tonsillar tumor samples in the ongoing study has been described before  [25].
Data analysis For data processing and evaluation, we used the SDS 2. 4 and the ExpressionSuite v1. 0. 1. software (Life Technologies, USA). Ct values for miRNAs identified by automated thresholding were exported separately for cards A and B. From the detected Ct values, the RQ (relative quantity) was calculated using the 2^-Ct formula. The data were further processed by the GeneSpring GX11 software (Agilent). Based on the per-sample measurement counts (hundreds), we selected the 50th percentile shift to perform withinsample normalization and applied it globally across the data sets. For comparison of two or three groups in the censored analyses, T-test or ANOVA, in combination with the Pavlidis Template Matching tool (TIGR TM4 suite) was used. The P-value (P < 0. 05) and fold-change (FC> 1.
Prognostic miRNAs The expression of 738 miRNAs was evaluated in ten tonsillar tumor samples. In order to evaluate if the analyzed miRNAs correspond with the clinical characteristics or patient prognosis, we performed the factor analysis. At first we compute the principal components and for them we made varimax rotation to obtain best view to data. The loading of obtained factor is displayed in Additional file 13: Figure  S7 , the components of the particular rotated factor are listed in Additional file 14: Table  S7. For the analysis we used IBM SPSS v. 23. One hundred percent of the variance can be explained by eight groups of factors and prognosis was the most strongly associated with miRNAs from group 5, which in total explains about 10. 3 % of the variability. Twelve miRNAs in group 5 (miR-196b, miR-485-3p, miR-589, miR-324-3p, miR-342-3p, miR-92a1#, miR-155, miR-146b, miR-142-3p, miR-1260, miR-143, and miR-142-5p) have strong loading (absolute value ≥ 0.
Acknowledgements The authors thank  Blanka Langova  and  Stephanie Villar  for technical assistance,  Prof. Lukas Rob  for collection of cervical samples. Thanks are also due to the  Institute of Pathological Physiology , 1st  Faculty of Medicine, Charles University, Prague , for the opportunity to use their equipment for TLDA card analysis and to the  Genetics Services Platform  of the  International Agency for Research on Cancer  for help with additional TLDA card processing. We also would like to thank  Dr. Al Klingelhutz  of  The University of Iowa, Carver College of Medicine  for the hTERT-immortalized human keratinocytes derived in his laboratory.
(Continued) (A) Fas death domain structural model demonstrating six antiparallel α-helices (PDB: 1DDF)  (19)  with the side chains of Y232 and Y291 indicated (left) and amino acid sequences of human Fas and rat Fas encompassing the death domain with positions of each helix indicated (right; α, α-helix). Tyrosine phosphorylation sites in hFas and corresponding residues in rat Fas are highlighted. Amino acid numbering is according to UniProt entries P25445 and Q63199. (B) A diagram depicting different states of Fas, with respect to its ability to transmit apoptotic signal, as affected by its death domain phosphorylation [adapted from Ref. (27) ]. The proapoptotic state is allowed when both DD tyrosines are dephosphorylated. The dominant proapoptotic state takes place when Y232 and/or Y291 is phosphorylated (some examples of possible dominant-negative scenarios are given).
FUnDinG This work was supported by institutional funds from the  Centre National de la Recherche Scientifique (CNRS)  and the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and by grants from the  Institut National du Cancer (INCa ;  PLBIO09-317 ); the  University of Nice , the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1226 ;  ANR-11-LABX-0028-01 ).
Introduction In the last 10 years, the prevalence of alternative splicing has been completely re-evaluated. Recent reports claim that more than 90% of multiexon genes produce at least two splicing variants  [Pan et al. , 2008 , Wang et al. , 2008]. The depth at which we can sample transcriptomes with next generation sequencing techniques opens the possibility not only to annotate splicing variants in physiological conditions, but also to detect which transcripts are differentially spliced across conditions. This growing interest in splicing both as a fundamental process and because of its implication in pathologies  [Scotti and Swanson, 2016 , Edery et al. , 2011 , David and Manley, 2010]  has been accompanied by an increasing number of methods aiming at analyzing RNAseq datasets  [Trapnell et al.
, 2012 , Wang et al. , 2010 , Robertson et al. , 2010]. The ultimate goal of these methods is to identify and quantify full-length transcripts from short sequencing reads. This task is particularly challenging and recent benchmarks show that all methods still make a lot of mistakes  [Steijger et al. , 2013]. The difficulty of reconstructing full-length transcripts (isoform-centric approaches) also prompted a number of authors to focus on identifying exons that are differentially included within transcripts (exon-centric approaches)  [Reyes et al. , 2013 , Katz et al. , 2010 , Shen et al. , 2012 , Sacomoto et al. , 2012]. Whether they are exon-centric or isoform-centric, methods to study splicing from RNAseq data can further be divided in two main categories  [Martin and Wang, 2011]. The mapping-first approaches first map the reads to the reference genome and the mapped reads are then assembled into exons and eventually transcripts.
, 2014 , Freyermuth et al. , 2016]. The generalization of de novo assembly approaches for studying splicing in human seems to be mostly impeded by the lack of a clear evaluation of its potential in comparison to more traditional mapping-based approaches. This is the gap we aim at filling with the work presented here. To achieve this goal, we performed a systematic evaluation of an assemblyfirst and a mapping-first approach on the same publicly available RNAseq dataset. As a first step, we chose to compare pipelines that we developed in parallel in two teams, namely KisSplice and FaRLine, because we could easily control their parameters. Any difference between the predictions that is solely due to a parameter setting could be fixed easily, which enabled us to obtain a precise understanding of the irreducible differences between the two approaches.
The majority of frequent isoforms are found by both approaches Applying KisSplice and FaRLine to the same RNAseq dataset (SK-N-SH cell lines treated or not with retinoic acid) generated by the ENCODE consortium, we noticed that 68% of the alternatively skipped exons (ASE) identified by KisSplice were also identified by FaRLine and that 24% of ASEs identified by FaRLine were also identified by KisSplice (Figure  2 A ). This observation highlights that the mapping-first approach predicts a much larger number of events. This difference in sensitivity is due to the fact that while mapping-first approaches require that each exon junction is covered by at least one read, assembly-first approaches require overlapping reads across the full skipped exon. Therefore, it can be anticipated that low abundant isoforms, that are covered by few reads, will be reported by mapping, but not by the assembly-first approach.
Either the exon is unannotated (28%), one of its flanking exon is unannotated (8%) or both exons are annotated but no transcript combining them was annotated (45%). The assembly-first approach, KisSplice, does not consider annotations, and an interesting resulting advantage is that novel junctions have the same chance to be assembled as known junctions. Mapping assembled novel junctions to the genome is indeed less challenging than read mapping because the assembled sequences are longer. The downstream annotation of the events is then permissive, in the sense that annotations are used as an evidence, not as a guide. Alternative splicing events involving novel splice sites are clearly identified as such, and can be individually tested and experimentally validated. HIRA gene contain a novel exon, whose inclusion is supported by at least 20 reads on each junction (Figure  3B ).
Overlap with other methods In a first step, we picked FaRLine and KisSplice as examples of a mappingfirst and an assembly-first approach respectively. Clearly, there are other published methods in both categories. MISO is probably the most widely used to annotate AS events. We therefore ran it on the same dataset to check how its predictions overlapped with ours. As shown in Figure  5 , 72% of predictions made by MISO were common to both FaRLine and Kis-Splice, 23% were only common with FaRLine, 2% were only common to KisSplice and the remaining 3% were specific to MISO. Overall, almost all candidates predicted by MISO were also predicted by FaRLine. This large overlap with FaRLine was expected, because both are mapping-first approaches. This also shows that the differences between mapping-and assembly-first approaches reported above are not limited to one mappingfirst approach.
Beside exon-centric approaches, which aim at finding the differentially spliced exons, there is also a number of published methods which are isoformcentric and have the more ambitious goal to reconstruct full-length tran-12 scripts. The most widely used mapping-first and isoform-centric approach is Cufflinks  [Trapnell et al. , 2012]  that we compared to FaRLine using the same dataset. As shown in Figure  5D , we found that the vast majority of ASE were predicted by both approaches. Finally, we compared KisSplice to one of the most widely used de-novo transcriptome assembler, Trinity  [Grabherr et al. , 2011]. As shown in Figure  5B , most ASE found by Trinity were also found by KisSplice. However, KisSplice was significantly more sensitive. The goal of Trinity is to assemble the major isoforms for each gene, it therefore largely under-estimates alternative splicing, especially inclusion/exclusion of short sequences.
KisSplice KisSplice is a local transcriptome assembler. As most short reads transcriptome assemblers  [Grabherr et al. , 2011 , Schulz et al. , 2012 , Robertson et al. , 2010] , it relies on a De Bruijn graph (DBG). Its originality lies in the fact that it does not try to assemble full-length transcripts. Instead, it assembles the parts of the transcripts where there is a variation in the exon content. By aiming at a simpler goal, it can afford to be more exhaustive and identify more splicing events. The key concept on which KisSplice is built is that variations in the nucleotide content of the transcripts will cor-16 respond to specific patterns in the DBG called bubbles. KisSplice's main algorithmic step therefore consists in enumerating all the bubbles in the graph built from the reads.
Annotation and quantification of alternative splicing events We wrote custom perl scripts, based on the FasterDB-based EnsEMBL r75 annotation database. For each gene, all the reads with at least one base overlapping the gene from the start to the end coordinates are retrieved. CIGAR strings are then used to retrieve the alignments blocks. Junction reads are identified by the presence of at least one 'N' letter in the CIGAR. Junction reads were filtered if: • More than 10% of soft-clipping was detected in the alignment (it should not be the case with TopHat) • An indel was close to the junction site, as it would make the junction position uncertain Junction read alignments are then processed block by block sequentially from left to right. Alignment blocks under 4bp on read extremities are removed from the reads as we considered it is not sufficient to identify cor-rectly the mapping localization.
This rule led to miss a lot of ASE events. So to define the flanking exons, we use the information contained in the transcripts and in the reads. We list each junction skipping an exon covered at least one read. If this junction is annotated in the transcripts, we extract all annotated events containing this junction. Else, we annotate the event with the longest covered inclusion isoform. It allows FaRLine to be more robust to the incompleteness of the annotation compared to other methods, like MISO. Panel B of supplementary figure  S2  gives an example of an ASE reported by FaRLine but not by MISO because the inclusion isoform is not annotated in the transcripts.
SKNSH dataset We downloaded a total of 959M reads from http://genome. crg. es/encode_ RNA_dashboard/hg19/. They correspond to long polyA+ RNAs generated by the Gingeras lab, and are also accessible with the following accession numbers (ENCSR000CPN -SRA: SRR315315, SRR315316 and ENCSR000CTT -SRA : SRR534309, SRR534310). For cell lines treated by retinoic acid, the reads were 76nt long, while they were 100nt long for the non treated cells. Hence we trimmed all reads to 76nt.
Acknowledgments This work was funded by the  ANR-12-BS02-0008  (Colib'read) by the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ), Action n3. 6 Plan Cancer 2009-2013,  Fondation ARC  ( Programme Labellisé Fondation   ARC 2014 ,  PGA120140200853 ) and  INCa  ( 2014-154 ). Doctoral fellowships from  ARC 1 -Région Rhône-Alpes  (C. B. P),  Science Without Borders -CNPq -Brazil  (L. L. -grant process number  203362/2014-4 ),  ARS Rhône-Alpes  (A. R. ) and post-doctoral fellowships from  Fondation ARC  (M. P. L). This work was performed on the computing facilities of the computing center  LBBE/PRABI  and the  PSMN (Pole Scientifique de Modelisation Numerique) computing center of ENS de Lyon.
KRT8 was predominantly expressed in ovarian surface epithelia of controls (Figure  3I  and 3J ). However, a focal distribution of KRT8 in TGFBR1-CA Acre ovaries was evident (Figure  3M  and 3N ). Double immunofluorescence revealed low expression or absence of INHA in KRT8-positive cells within tumor lobules (Figure  3O ), suggesting distinct identity of KRT-positive cells.
Hormone profile of mice harboring a constitutively active TGFBR1 in the ovary Clinically, granulosa cell tumors often secrete excessive inhibin and estrogen  [32, 33]. Serum inhibin is a reliable marker for granulosa cell tumors  [34]. To determine the relevance of TGFBR1-CA Acre mice as a potential model for granulosa cell tumors, we measured serum estradiol (E2), inhibin B, inhibin A, and folliclestimulating hormone (FSH) levels in control and TGFBR1-CA Acre mice. The results showed that serum levels of E2, inhibin B, and inhibin A were highly elevated in TGFBR1-CA Acre mice versus controls (Figure  3P-3R ). Consistent with the high levels of inhibin, serum FSH levels were below detection limit in TGFBR1 overactivated mice (Figure  3S ). These data demonstrate that constitutive activation of TGFBR1 promotes the development of granulosa cell neoplasms which phenocopy certain molecular and hormonal characteristics of human granulosa cell tumors.
Immunofluorescence microscopy and immunohistochemistry Immunofluorescence and immunohistochemistry were performed using serial paraffin sections (5 μm)  [18] , with primary antibodies listed in Table  S1. Secondary antibodies for immunofluorescence were conjugated with Alexa Fluor 488 or 594. The sections were mounted using ProLong Gold Slowfade media containing 4',6-diamidino-2-phenylindole (DAPI; Invitrogen). Images were captured using IX73 microscope (Olympus) interfaced with an XM10 CCD camera and cellSens Software. Immunohistochemistry was performed using avidin-biotin complex (ABC) method as described  [18]. Signals were developed using VECTOR NovaRED. Negative controls where primary antibodies were replaced by isotypematched IgGs from the same species were included.
Statistical analyses All statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS; Version 23) except the survival rate analysis by Mantel-Cox test using GraphPad Prism 6 software. Two sample independent t-test (unpaired) was performed to determine difference between two means. The homogeneity of variance was assessed using Levene's test. Two-way analysis of variance (ANOVA) was applied to determine the effect of gonadotropin on granulosa cell differentiation. Data are presented as mean ± standard error of the mean (s. e. m) unless otherwise specified. Statistical significance was defined at P < 0. 05. Results are marked as * P < 0. 05, ** P < 0. 01, and *** P < 0. 001.
ACKNOWLEDGMENTS We thank  Drs. Robert Burghardt  and  David Threadgill  for insightful comments and critical reading of the manuscript. We thank  Dr. Aline Rodrigues Hoffmann  for helpful comments and discussions. We thank  Drs. JoAnne Richards  and  Jan Gossen  for the Cyp19-Cre mice and  Dr. Richard Behringer  for the Amhr2-Cre mice.
, 2001). PCR products were visualized after electrophoresis using a 2% agarose gel, staining with ethidium bromide (at 5 mg/L for 15 min) and UV light exposure. PCR products were sequenced by the Biofidal (Villeurbanne, France) sequencing platform. Their DNA sequences were analyzed using the BioEdit software. Similarity and identity searches of the DNA/amino acid sequences were performed using the NCBI website (http://www. ncbi. nlm. nih. gov/). Multiple alignments of DNA and protein sequences were computed using CLUSTALX v2. 1. These alignments were visualized using Seaview  (Gouy et al. , 2010). BLAST analyses were run at NCBI and insertion sequences were characterized using the IS biotoul website (http://www-is. biotoul. fr/). The phylogenetic relationships between DNA or amino acid sequences were computed by distance matrix methods according to  Galtier et al.
(1996). Neighbor-joining trees and 1000 bootstrap replicates were generated. The adjusted Wallace coefficients enabling comparisons of partitions between genetic markers (lec and MLST types) were computed according to  Severiano et al. (2011)  using UMMI web site at Universidade de Lisboa (http://www. comparingpartitions. info/). Homoplasy test was performed using Splitstree4  (Huson and Bryant, 2006). The DNA and amino acid sequences generated in this work have been assigned GenBank accession numbers KT182468 to KT182473.