dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of 6 transcripts recapitulating anaplastic meningioma clusters Mapped RNA sequencing reads were normalised using the regularised logarithm (rlog) function implemented by the DESeq2 package  97, 98. PCA was performed using the top 500 most variably expressed transcripts and the prcomp function (R stats package)  111. Given that primary component 1 (PC1) was the vector most clearly distinguishing the closely clustered C2 subgroup from the more diffusely clustered C1 (Figure  3a ), we extracted the top 50 transcripts with the highest absolute PC1 coefficients. We then identified the subset that overlapped with the most significantly differentially expressed genes (absolute log 2 fold change > 4 and adjust p-value < 0. 0001) between i) the C1 and C2 anaplastic meningioma subgroups and ii) the C1 anaplastic meningiomas and the 19 grade I tumors (Supplementary Tables  S10  and S17 ).
Processing of genomic sequencing data Genomic reads were aligned to the reference human genome (GRCh37) using the Burrows-Wheeler Aligner, BWA (v0. 5. 9)  112. CaVEMan (Cancer Variants Through Expectation Maximization: http://cancerit. github. io/CaVEMan/) was used for calling somatic substitutions. Small insertions and deletions (indels) in tumor and normal reads were called using a modified Pindel version 2. 0. (http://cancerit. github. io/cgpPindel/) on the NCBI37 genome build  113, 114. Annotation was according to ENSEMBL version 58. Structural variants were called using a bespoke algorithm, BRASS (BReakpoint AnalySiS) (https://github. com/cancerit/BRASS) as previously described  115. The ascatNGS algorithm was used to estimate tumor purity and ploidy and to construct copy number profiles from whole genome data  116.
Identification of driver mutations in known cancer genes Non-synonymous coding variants detected by Caveman and Pindel algorithms were flagged as putative driver mutations according to set criteria and further curated following manual inspection in the Jbrowse genome browser  118. Variants were screened against lists of somatic mutations identified by a recent study of 11,119 human tumors encompassing 41 cancer types and also against a database of validated somatic drivers identified in cancer sequencing studies at the Wellcome Trust Sanger Institute (Supplementary Tables  S22  and S23 )  119. Copy number data was analysed for homozygous deletions encompassing tumor suppressor genes and for oncogene amplifications exceeding 5 or 9 copies for diploid and tetraploid genomes, respectively. Only focal (<1Mb) copy number variants meeting these criteria were considered potential drivers.
Patient survival analysis The Kaplan-Meier method was used to analyze survival outcomes by the log-rank Mantel-Cox test, with hazard ratio and two-sided 95% confidence intervals calculated using the Mantel_Haenszel test (GraphPad Prism, ver 7. 02). Overall survival data from time of first surgery for each anaplastic meningioma within gene-expression defined subgroups 1 and 2 was collected and used to plot a Kaplan-Meier survival curve.
120 the supervised analysis of the probes, CpG sites were selected by applying an ANOVA test to identify statistically significant CpG positions (FDR adjusted p-value < 0. 01) that were differentially methylated among the compared groups (Δβ > 0. 2). Selected CpG sites were later clustered based on the Manhattan distances aggregated by ward's linkage. Finally, the genes corresponding to the selected CpGs were used to perform a Gene Set Enrichment Analysis (GSEA) with curated gene sets in the Molecular Signatures Database120. The gene sets used were: H: hallmark gene sets, BP: GO biological process, CC: GO cellular component, MF: GO molecular function and C3: motif gene sets (http://software. broadinstitute. org/gsea/msigdb/collections. jsp). The gene clusters resulting from the hypergeometric test with a FDR adjusted p-value < 0.
Acknowledgements This work was supported by the  Wellcome Trust ,  Cancer Research UK, Meningioma UK and Tadhg and Marie-Louise Flood. U. M. was personally supported by a  Cancer Research UK   Clinician Scientist Fellowship ; G. C. by a  Wellcome Trust   Clinical PhD Fellowship  ( WT098051 ); F. M. by  A. I. L. ( Associazione Italiana Contro le Leucemie-Linfomi e Mieloma ONLUS ) and by  S. I. E. S. (Società Italiana di Ematologia Sperimentale ); S. B. was funded by a  Wellcome Trust   Intermediate Clinical Research Fellowship  and a  St. Baldrick's Foundation Robert J. Arceci Innovation Award. J. B. was funded by the  charity Brain Tumour Research. The samples were received from the tissue banks from  Cambridge (UK) ,  Dresden (Germany) ,  Liverpool (UK) ,  Plymouth (UK)  and  Tel Aviv  (Israel).
Then, we excluded from the analysis the positions with background signal levels in methylated and unmethylated channels (p>0. 01). Finally we removed probes with one or more single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) > 1% in the first 10 bp of the interrogated CpG, as well as the probes related to X and Y chromosomes. From the filtered positions, we selected only CpG sites present both in promoter regions (TSS, 5'UTR and 1st exon) and CpG islands (UCSC database, genome version hg19). meningiomas. Bisulfite-converted DNA (bs-DNA) was hybridized on the Ilumina Infinium HumanMethylationEPIC BeadChip array following the manufacturer's instructions. All.
1-97. 5%). Differential expression analysis was performed using Cuffdiff  [21]  for the Cufflinks package  [22]. The bioconductor package cummeRbund  [23]  was used to investigate the output from the Cuffdiff package. Identification of signal peptides in transcripts of interest was carried out by manual curation of relevant NCBI GenBank files (https://www. ncbi. nlm. nih. gov/genbank/) while secreted proteins were identified through comparison with a database downloaded from the Human Protein Atlas (https://www. proteinatlas. org/ search/protein_class:Predicted%20secreted%20proteins).
Microarrays Microarrays were carried out using RNA extracted from whole testes of 20-day old FSHRKO, SCARKO, FSHRKO. SCARKO and control mice. RNA from testes of individual animals was extracted using RNeasy kits (Qiagen Ltd. , Manchester, UK). Samples of total RNA (8 μg) from individual animals (4 per group) were reverse transcribed and then in vitro transcribed and hybridised to mouse MOE430A arrays (Affymetrix, Santa Clara, CA, USA) according to the GeneChip expression technical manual (Affymetrix, Santa Clara, CA, USA) as previously reported  [24]. Gene transcript levels were determined from data image files using algorithms in Gene Chip Operating Software (GCOS1. 2, Affymetrix). Data were normalised to testis volume and Sertoli cell number as described in Results (Additional file 1). Array data and RNAseq data were aligned using a relational database (Access, Microsoft Corp, Washington, USA) using the gene symbols to connect the databases.
Real-time PCR Testis RNA was extracted using Trizol (Life Technologies, Paisley, UK) and levels of specific mRNA species were measured by real-time PCR. Total RNA was reverse transcribed using random hexamers and Moloney murine leukaemia virus reverse transcriptase (Superscript III, Fisher Scientific UK Ltd. , Loughborough, UK) as described previously  [25]. To normalise the data, external standard (luciferase mRNA: Promega UK, Southampton, UK) was added to each testis at the start of the RNA extraction  [26]. For real-time PCR the SYBR green method was used with SYBR mastermix (Agilent Technologies, Wokingham, UK)  [27]. All primers were designed by Primer Express 2. 0 (Applied Biosystems, Warrington, UK) or Primer-BLAST (https:// blast. ncbi. nlm. nih. gov/Blast. cgi) using parameters previously described  [28]  and the primers used are described in Additional file 2.
The only probable exceptions will be transcripts in another cell type which are highly dependent on the germ cells or Sertoli cells and which may be wrongly categorised as germ cell-or Sertoli cell-specific if their expression drops markedly after cell ablation. This is discussed further below but we would not anticipate numbers of such transcripts to be large. Transcripts in the RNAseq database that show a less-marked response to cell ablation (eg 30-70% decrease) are likely to be expressed in more than one cell type and so data in Additional file 4 should be treated with caution and should be cross-referenced to Additional file 3. As a proof of concept that cell ablation will identify cell-specific transcripts, transcript data (from the RNAseq study) for a number of known germ cell-, Leydig cell-and Sertoli cell-specific genes are shown in Fig.
05, FDR = 0. 05) reduction after DTX treatment. This list contains 495 protein-coding genes, 121 non-coding RNAs, 44 predicted pseudogenes and 41 other unclassified transcripts (Additional file 5). The disadvantage to using less stringent criteria is that transcripts decreased 70-80% after DTX are likely to be expressed at low levels in other testicular cells. Conversely, this is a list of transcripts highly enriched in the Sertoli cells which can be used to interrogate other databases and it is likely to include the overwhelming majority of Sertoli cell-specific transcripts. The data in Additional file 3 also allows investigators to apply more or less stringent criteria as necessary. Finally, the complete RNAseq dataset (Additional file 3) can be used to assess whether any gene of interest is likely to show Sertoli cell expression, even if it is expressed in more than one testicular cell type.
This would indicate that in the mouse Sox13 is not expressed in the germ cells, shows some expression in the Sertoli cells but is mostly expressed in another somatic cell type. Differences to other studies which used the rat  [33, 34]  may be due species-dependent variation in transcript localisation. To confirm the data from the RNAseq analysis 10 genes were chosen for further analysis by qPCR. These transcripts represent different levels of fold-change in expression after DTX and transcript levels were measured in testes from iDTR mice (without prior busulfan treatment) at different times after DTX treatment (Fig. 2 ). At 7 days after DTX treatment there was a clear and marked reduction in all transcript levels and the fold change after DTX was similar whether measured by qPCR or RNAseq with the exception of Epha8 which showed a less marked reduction by qPCR.
Functional analysis of Sertoli cell-specific transcripts Potential protein/gene network interactions among the Sertoli cell-specific transcripts identified by the online "Search Tool for the Retrieval of Interacting Genes/Proteins" (STRING, http://www. string-db. org) [37]    Transcript species Etd, Slc9a2, Peg10, Pde4b and Aqp8 showed significant (P < 0. 05) age-dependent differences between controls may be secreted by the cells (Additional file 9). This list of 72 secreted proteins includes 10 which do not have a signal peptide and so must be secreted through non-classical secretory pathways  [38]. Interestingly, the one KEGG pathway significantly enriched is for axon guidance while biological processes associated with neural signalling are also enriched. This is consistent with the reported ability of Sertoli cells to promote neuronal differentiation and survival in cocultures and co-grafts  [39]  although the normal physiological importance of this is not clear beyond the high potential for the Sertoli cells to secrete trophic factors.
Comparing all 4 datasets (Additional file 5, two RiboTag datasets  [41, 42]  and the data in  [43] ) there are 83 transcripts that appear in all (Additional file 15). This may be considered as a baseline list of Sertoli cellspecific transcripts but must be considered to be highly conservative and treated with caution as it does not include some known Sertoli cell-specific transcripts such as Rhox5, Amh, Sox9, and Gdnf.
Identification of FSH-and androgen-regulated Sertoli cellspecific transcripts To identify Sertoli cell-specific genes regulated by FSH and androgen, microarrays were carried out using RNA extracted from 20-day old control, FSHRKO, SCARKO and FSHRKO. SCARKO mice. All data from the arrays is shown in Additional file 16 and data from Sertoli cellspecific transcripts only is shown in Additional file 17. In total, 539 Sertoli cell-specific transcripts, identified by RNAseq as above, were also present on the gene-chips used for the arrays. It has been shown previously that testis volume and Sertoli cell number differ significantly between the control and transgenic mouse groups used in this study  [16]  with the consequence that there is enrichment for Sertoli cell mRNA in total testicular mRNA from all groups relative to control animals (ie Sertoli cell mRNA will make up a greater proportion of total testis mRNA).
In order, therefore, to determine whether transcript expression in the adult shows similar dependence on hormonal stimulation to 20-day old animals, the expression of 10 Sertoli cell-specific genes was measured by qPCR in adult control, FSHRKO, SCARKO and FSHRKO. SCARKO mice. The transcripts chosen for further study represented a cross-section of transcript differences in the FSHRKO and SCARKO testes as measured by the microarrays. The qPCR used an external standard (luciferase) for normalisation so that there was no requirement to correct for testis volume and the results are corrected only for Sertoli cell number  [16]. For comparison, data in Fig. 4  shows results from the arrays on day 20 animals and the qPCR studies on the adult animals. In general, transcript differences seen in adult SCARKO and FSHRKO.
[43]. Comparing the datasets from the RiboTag mice  [41, 42]  with the overall Sertoli cell transcriptome  [43]  shows that 82%  [41]  and 71%  [42]  of genes identified in the RiboTag mice are expressed in the Sertoli cells, as may be expected, but the data outlined here using the iDTR mouse would suggest that the criteria for selecting Sertoli cell-specific/enriched genes may not have had adequate stringency. Studies using a variety of techniques, including cell ablation, have highlighted the role that the Sertoli cells play in regulating testicular function in the adult  [2, 3, [46] [47] [48]. These Sertoli cell actions include maintenance of germ cell and Leydig cell populations, stimulation of peritubular myoid cell activity and maintenance of the testicular vasculature  [4]. How the Sertoli cells act to orchestrate each of these activities and maintain testicular function remains largely unknown, however.
Quantitative RT-PCR analysis Total RNAs were isolated from independent biological replicates of chMM cultures infected for 5 days with RCAS-BP(A) retroviruses overexpressing each of the TFs or no recombinant protein as control. RNA extracts were obtained as described for RNA sequencing. 500 ng of RNA extracts were used as template for cDNA synthesis using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). Quantitative RT-PCR was performed by using the SYBR Green PCR Master mix (Applied Biosystems) in duplicates. Relative mRNA levels were calculated according to the 2-DDCt method  [65]. DCts were obtained from Ct normalized with chick RPS17 (S17) and GAPDH. For each investigated gene, the mRNA levels of control chMM cultures were normalized to 1. Statistical analysis was performed by using Mann-Whitney U test with the GraphPad Prism V6 software.
Transcription factor peak calling Quality of the TF ChIP-seq data was evaluated following the ENCODE consortium guidelines and metrics (Additional file 3: Table  S5 )  [80]. Peaks were called by using MACS2 v2. 1. 0. 20140616  [77]  with low-stringency parameters to obtain a significant list of peaks (--bw 130/135, as determined by the cross-correlation analysis; -g 1. 0e9; --to-large; -p 0. 025). Irreproducible discovery rate (IDR) analysis was performed on the top 125,000 peaks according to their p-value  [80]  (parameters: peak. half. width -1; min. overlap. ratio 0; is. broadpeak F; ranking. measure p. value). The final set of TFBS was determined by selecting the number of peaks with an IDR threshold below 0. 01 obtained from the pooled-replicate consistency analysis.
Binding motif analysis Motif analysis was performed by using DREME v4. 11. 2  [81]  (default parameters) on the 150-bp sequences surrounding the summits (± 75 bp) of the 1,000 most significant TF peaks that overlapped with promoters and enhancers. Recognition motifs thus identified were then compared against motif databases by using Tomtom v4. 11. 2  [82]  (default parameters).
Acknowledgements: We are grateful to  Stefan Mundlos  ( Charité Universitätsmedizin  and  Max Planck Institute for Molecular Genetics, Berlin, Germany)  for generously sharing resources. We are thankful to the  Sequencing Core Facility of the Max Planck Institute for Molecular Genetics  for processing the RNA-seq. We are thankful to the  Next Generation Sequencing Core Unit of the Berlin-Brandenburg Center for Regenerative Therapies  for processing the ChIP-seq. We thank  Peter Hansen  and  Peter N. Robinson  ( Charité Universitätsmedizin, BCRT, Berlin, Germany ), as well as  Marius van den Beek  and  Christophe Antoniewski  ( Institut de Biologie Paris-Seine, ARTbio, Paris, France ) for.
Funding: This work was funded by the  Deutsche Forschungsgemeinschaft (DFG ; grant  GK1631 ), the  Université Franco-Allemande (UFA/DFH ; grants  CDFA-06-11  and  CT-24-16 ), the  Association Française contre les Myopathies  (AFM; grants  16826  and  18626 ), the  Fondation pour la Recherche Médicale  (FRM; grant  DEQ20140329500 ), the  INSERM  and the  CNRS. MO was part of the  MyoGrad International Research Training Group  for Myology and received financial support from the  FRM  (grant  FDT20150532272 ).
Input data The necessary meteorological input data to run DYRESM is as follows: air temperature and relative humidity at 2 m above soil level, wind speed at 10 m above soil level, precipitation, solar radiation and cloud cover. Meteorological data for the period 2006-2014 were provided by the French meteorological service, Météo-France, for the meteorological station of Aix-en-Provence (14 km west of the reservoir, 173 m. a. s. l. , 43 • 32 N 5 • 25 E). An adiabatic gradient correction of -0. 006 • C/m was initially applied to the air temperature measurements to take into account the difference in altitude between the Aix-en-Provence meteorological station and the Bimont reservoir. Since cloud cover data were not available for the Aix-en-Provence station, cloud cover data were obtained from the Marseille meteorological station (43 • 19 N 5 • 29 E).
The SCP provided measured daily inflow and outflow data. Infiltration was considered constant and was adjusted to close the hydrologic budget. The SCP also provided water temperature data for the artificial tributary. Water temperature at the Cause stream was estimated through a sigmoid air temperature-water temperature regression model  (Mohseni et al. , 1998)  fitted with field data measured in  2009-2010. The lake bathymetry between the elevations 274. 9 m. a. s. l. and 341 m. a. s. l. was derived from the aggregation of two data sources: 1) raster data measured by the Irstea Hydrobiology Unit using a depth recorder up to an elevation of 329 m. a. s. l. and 2) a 5 m resolution MNT raster obtained from Spot-5 satellite images and provided by the Regional Centre of Geographic Information (CRIGE).
INPUT DATA CORRECTION COEFFICIENTS The light extinction coefficient was estimated from the SD as follows: LEC = C LEC SD (1) where the coefficient C LEC is usually 1. 7. A constant SD equal to the mean of the study period (4. 99 m) was used, and the coefficient C LEC was considered a calibration parameter that can vary in the range of 0. 61-1. 9 (Margalef, 1983). Calibration coefficients were also applied to the meteorological forcing to account for differences in microclimatic conditions and shading. An additive coefficient was also applied to the temperature of the inflows to analyse the sensitivity of the model to inflow temperatures. T a, lake = T a + C Ta (2) HS lake = C HS * HS C HS ∈ [0, 1] (3) W lake = C 1, W + C 2, W W (4) T wx, lake = T wx + C Twx (5) The variables T a , HS, W and T wx are measured air temperature, solar radiation, wind speed and water temperature of inflow x, respectively.
ACKNOWLEDGEMENTS We thank two anonymous reviewers for their comments to improve the manuscript. This paper also benefitted from discussions with  Alexey A. Voinov  and  Björn Reineking. The authors thank  Météo-France  for providing the meteorological data, and the  Société du Canal de Provence  for providing hydrological and water quality data. The authors thank  Nathalie Reynaud  and  Yann Le Coarer  for their help in preparing the reservoir bathymetry, the members of the  Onema-Irstea consortium Martin Daufresne ,  Tiphaine Péroux ,  Julien Dublon  and  Delphine Rebière  for the acquisition of water temperature and  Secchi depth data , and  Marie-José Salençon  and  Magali Gant  from  EdF R&D  for their comments. The funding was provided by the research programme of the joint  Irstea-EdF research group HYNES.
The experiment, the glider sensors including the ISFET sensor, and the method of validation are described in Sect. 2. The shipbased data are presented in Sect. 3. 1, and a comparison between ship and glider measurements is made in Sect. 3. 2. 1. Figure  1. The locations of the 93 dives undertaken by the Seaglider (red markers), the eight ship CTD casts in which water samples were obtained (white markers), and meteorological buoy M1 (yellow marker) within the REP14-MED observational domain off the coast of Sardinia, Italy between 11 and 23 June 2014. GEBCO 1 min resolution bathymetry data (metres) were used (http://www. bodc. ac. uk/projects/ international/gebco/), and surface circulation patterns were adapted from figures presented by  Millot (1999). The initial pH results and validation, the method of further correcting pH, and an artefactual light-induced effect are described in Sect.
A comparison between the ship c(O 2 ) measurements and the calibrated glider c(O 2 ) measurements is made in Sect. 3. 2. 1. Glider variables have been processed using an opensource MATLAB-based toolbox (https://bitbucket. org/ bastienqueste/uea-seaglider-toolbox/) in order to correct for differing timestamp allocations, sensor lags  (Garau et al. , 2011; Bittig et al. , 2014) , and to tune the hydrodynamical flight model  (Frajka-Williams et al. , 2011). Outliers outside of a specified range (e. g. 6 standard deviations) were flagged and not used for analysis, and glider profiles were smoothed using a lowess low-pass filter with a span of 5 data points (< 4 m range), which implements a local regression using weighted linear least squares and a first-order polynomial linear model. Individual profiles were inspected afterwards to ensure that potentially correct data points were not removed.
The data retrieved from the integrated pH sensor and the p(CO 2 ) sensors could not be used due to quality issues. We think the regular on/off cycling of power to the integrated dual pH-p(CO 2 ) sensor between sampling did not allow it to function properly. In future, we would suggest the addition of backup batteries to supply power to the sensor between sampling. The cause of the problem with the stand-alone p(CO 2 ) unit is unclear. To measure pH, the activity of H + ions is determined using the interface potential between the Cl-ISE and the semiconducting ion-sensing transistor coated with silicon dioxide (SiO 2 ) and silicon nitride (Si 3 N 4 ). The ISFET pH sensor was previously found to have a response time of a few seconds with an accuracy of 0. 005 pH and suitable temperature and pressure sensitivities  (Shitashima et al.