dataesr/scientific-paragraphs-categorization huggingface dataset:
The full list of Gene Ontology terms from the analysis of the grade II and grade IV networks can be found in S3 Dataset. The functional profiles of the targets of the two groups of Rho GTPases also contained a subset of similar functional terms such as synapse, cell projection, neurotransmitter transport, vesicle membrane, cell junction. This suggests that in grade IV gliomas the link between Rho GTPases and normal glial function is only partially interrupted. Interestingly, we found that although most (15/19) of the Rho GTPases were differentially expressed between grade II and grade IV gliomas, there was not a clear trend in the direction of change (S2 Fig). Additionally, none of the Rho GTPases were differentially expressed between grade II and grade III gliomas (S2 Fig). Overall, this supports the hypothesis that distinct subsets of Rho GTPases are potentially important players in grade IV gliomas.
A gene clustering procedure identified 14 distinct clusters which were stratified according to their expression profiles (S3 Fig) (for gene lists of each cluster and the Gene Ontology analysis see S4 Dataset). Functional profiling of the early transcriptional response post-implantation (up to 12hrs) revealed an increase in expression of extracellular matrix components. The intermediate transcriptional response (13 to 24hrs) was characterised by a wave of transcriptional repression related to the inflammatory response, regulation of apoptosis, lipid biosynthetic process and lysosomes functions. These included the extracellular matrix remodelling genes, BMP2 and BMP6, chemokine signalling genes CCR1 and CCL20 and the inflammatory cytokine IL1A (S3 Fig). Consistent with the development of a fully vascularized tumour at 48 hours post-implantation we saw a dramatic increase in the expression of genes related to the cell cycle, cell adhesion, blood vessel development, and cell migration in the time window between 37 and 48 hours.
Four Rho GTPases were up-regulated in this time window. Two of these (RHOC and RND3) were among the Rho GTPases with a strong grade IV specific connectivity profile in multiple datasets (Fig 3D ) and the remaining two (RHOBTB1 and RHOBTB2) were nonspecific. Since GTPases and their potential transcriptional targets were all modulated at this specific time window we hypothesized that if a cause and effect relationship exists it should be within the time frame and resolution of our sampling (12 hours). We therefore reverse engineered a static ARACNE mutual information network representing the neighbourhood of RND3, RHOC, RHOBTB1 and RHOBTB2 during the tumour implantation time course (S4A Fig). Remarkably, we found that the resulting network shared many properties with the grade IV network derived from the clinical study  (Fig 3).
Differential expression analysis revealed 2,606 genes up-regulated and 2,099 genes down-regulated compared to non-silencing controls (siControl/siRND3). A Gene Ontology analysis of these genes was consistent with the predictions made from the reverse engineered networks (Fig 3E; S1 Table ). The full Gene Ontology analysis can be found in S5 Dataset. Functions related to tumour development including inflammatory response, regulation of apoptosis, cell migration and cell cycle were differentially regulated in directions consistent with the correlation patterns within primary glioma and U87 Ontology terms enriched within genes connected to grade II specific Rho GTPases or grade IV specific Rho GTPases (determined by connectivity, shown in panel D). False discovery rate < 10%, number of genes indicated in brackets (grade II: green, grade IV: red).
Copy number variation (CNV) and expression of RND3 is predictive of clinical outcome Having shown that RND3 plays a role in the development of GBM, we asked whether its expression could be influenced by genetic mutations such as copy number variation of the RND3 locus, and if both expression and CNV may be predictive of patient survival. We first approached this question using data available within the REMBRANT database of functional genomics data  [29]. We focused on GBM patients and found that patients with an increased RND3 copy number showed significantly lower probability of survival compared to patients with a normal or reduced RND3 copy number (Fig  7A ). Using the same database we then could verify that RND3 gene expression was also predictive of survival and that RND3 CNV and expression were positively correlated (Fig 7B ).
RND3 is under-expressed in prostate cancer and induces apoptosis and cell cycle arrest  [48]. In addition, a previous study in glioma positioned RND3 as an anti-tumour gene, decreasing proliferation and inducing apoptosis in U87 cells  [46]. However, these data were based on the overexpression of RND3 in a cell type that already expresses high levels of RND3 (S7A Fig), leading to a non-physiological situation. It is also possible that RND3 has a bell-shaped activity profile, where inhibition is seen when it is absent or low and when it is highly overexpressed such as after transfection into cells. The strength of our approach, in contrast to many of these previous studies, relies in the fact that a systems biology approach that combines clinical and experimental datasets has been used in our study.
Identification of genes differentially expressed in low and high-grade glioma Differentially expressed genes between grade II (n = 45) and grade IV (n = 81) gliomas were identified using an existing microarray study by Sun et al  [49]. Raw data were downloaded from the GEO database (accession GSE4290) and normalized using the Robust Multiarray Average algorithm (RMA)  [50]. Statistical significance was determined using a t-test followed by correction for multiple comparisons using the Benjamini-Hochberg method to estimate the false discovery rate (FDR)  [51]. Genes with a log 2 fold change greater than 1. 5 and an FDR < 1% were selected. This transcriptional signature was compared to a transcriptional signature from a similar study comparing grade II (n = 50) and grade IV (n = 24) glioma (accession GSE52009) for validation purposes.
Modularization of the human protein interactome representing tumour stage transition In order to achieve this goal we first subset the human protein interactome by selecting proteins encoded by genes differentially expressed between grade II and grade IV glioma samples. The interactions between this subset of proteins were identified using the Michigan Molecular Interaction (MiMI) protein interactions databases  [53] , which merged and integrated a number of protein interactions databases such as BioGRID  [54]  and HPRD  [55]. This protein-protein interaction (PPI) network consisted of 1423 nodes and 18,681 edges. Hubs were identified within the network by calculating the percolation score  [56]  for each node, in order to identify potentially important proteins. There were 93 hub nodes with a percolation score greater than 2 standard deviations above the mean.
Reverse engineering transcriptional networks in glioma Low and high-grade glioma transcriptional networks were generated from grade II and grade IV samples from the Sun et al dataset (GSE4290)  [49]  and Cancer Genome Atlas datasets (TCGA) (http://cancergenome. nih. gov/). Normalised, batch corrected TCGA datasets were downloaded from the MD Anderson MBatch (http://bioinformatics. mdanderson. org/ tcgambatch/) website. Mutual information networks were inferred using the ARACNE method  [58]  with the Rho GTPases set as hub genes. Significant gene-gene interactions were defined using a p-value cut-off of 1x10 -5. A dynamic model of gene expression in the developing tumour during the first 5 days following U87 deposition on the CAM has been inferred from our microarray data using a custom bioinformatics pipeline based on time-delay correlation.
Western blot analysis Protein were separated by SDS-PAGE and then transferred to nitrocellulose membranes using previously described methods  [61]. Western blotting was performed using antibodies against cleaved Caspase 3 (Cell Signalling Technology), RND3 (Millipore), MCM3 (Abcam), Lamin A/ C (Santa Cruz Biotechnology), α-tubulin (Sigma) and Flag (Sigma M2). The western blot bands were quantified using ImageJ Software.
Microarray analysis of implanted tumours and U87 cells RNA was extracted from U87 cells and snap frozen tumours using RNeasy columns (Qiagen, UK). RNA purity was assessed using a NanoDrop spectrophotometer and each sample had a 260/280 ratio of 1. 8-2. 1. RNA was reverse transcribed and the cDNA was labelled with fluorescent Cy3 dye using the Agilent Low-input Quick Amp Kit (Agilent, UK). cRNA was purified using RNeasy columns (Qiagen, UK) and hybridised overnight to Agilent Human 8x60k Whole Genome or Agilent Chicken V1 Whole Genome microarrays according to the manufacturer's protocol. Microarrays were scanned using an Agilent SureScan microarray scanner and processed using Agilent Feature Extraction software. Data was normalised using quantile normalisation. In order to remove probes from the analysis that could potentially hybridise to both chicken and human cRNA we performed a separate microarray analysis.
We created separate pools of RNA from CAM and U87 cells to create chicken and human reference samples. The chicken and human reference RNA was then hybridised to both human and chicken whole genome microarrays. RNA extraction, generation of fluorescently labelled cRNA, microarray hybridisation and scanning protocols were identical to those used for the implanted tumour tissue. After subtraction of the background signal, the relative contribution of the human cRNA to the total fluorescence observed for each probe on the chicken array was calculated, and vice versa. Any probe for which the cross-hybridisation of cRNA from the other species resulted in a fluorescent signal > 64 or the relative contribution to the total signal was greater than 15% was removed. 5,272 probes were removed from the chicken dataset and 9,128 probes were removed from the human dataset.
7 software. The mass spectrometer alternated between a full FT-MS scan (m/z 380-1,600) and subsequent collision-induced dissociation (CID) MS/MS scans of the 7 most abundant ions. Survey scans were acquired in the Orbitrap with a resolution of 30,000 at m/z 400 and automatic gain control (AGC) 1x10 6. Precursor ions were isolated and subjected to CID in the linear ion trap with AGC 1x10 5. Collision activation for the experiment was performed in the linear trap using helium gas at normalized collision energy to precursor m/z of 35% and activation Q 0. 25. The width of the precursor isolation window was 2 m/z and only multiplycharged precursor ions were selected for MS/MS. MS/MS scans were searched against NCBI database using Mascot algorithm in Proteome Discoverer 1. 1 software (Thermo Fisher Scientific).
Linking RND3 to GBM subtypes To investigate if expression of RND3 is dependent on GBM subtype  [8] , genes with known signatures and genetic mutations influencing the subtype were compared with RND3. The eight genes included: NF1, PDGFRA, IDH1, EGFR, TP53, FIP1L1 (FIP1L1 can become a fusion protein with PDGFRA), PTEN and CDKN2A. Normalized gene expression, scaled CNV and somatic mutation data free from batch effects was downloaded from the MD Anderson Bioinformatics Cancer Genome Atlas MBatch resource (bioinformatics. mdanderson. org/tcgambatch). A regression analysis was performed using either univariate regression or the ensemble classification and regression algorithm Random Forest (RF), where RND3 was used as the dependent variable and the 8 gene CNV and expression data as the predictor variables.
Survival analysis The effects of alterations in expression and copy number variation of Rho GTPases on glioblastoma patient survival was assessed using the REMBRANDT  [29]  and TCGA databases. The online tools available on the REMBRANDT website (www. caintegrator. nci. nih. gov/ rembrandt) were used to generate Kaplan-Maier survival curves and log rank p values. Similarly, the TCGA data was analysed by first calculating ranks for each patient according to the expression or standardised CNV of RND3, and then finding the optimum partitioning of patients that maximises the significance of the Cox regression model. This was then used to generate Kaplan-Maier survival curves and log rank p values.
Accession numbers The following datasets are deposited within Gene Expression Omnibus: Implantation of U87 cells on the chicken CAM (GSE43674), RND3 silencing in U87 cells (GSE43812). Supporting Information S1 Dataset. Gene Ontology analysis of the genes found within network modules M1-M4. Gene lists have been split into those up-regulated or down-regulated in grade 4 glioma compared to grade 2 glioma. Significantly enriched Gene Ontology terms were selected using a 10% False Discovery Rate cut-off. (XLSX) S2 Dataset. Rho GTPase gene connectivity in networks derived from the Cancer Genome Atlas glioma grade II and IV datasets. Rho GTPase connectivity was assessed in multiple ARACNE networks at different significance levels for gene-gene connections (p-values < 1e -5 or 1e -6 ). The same threshold used for the original data was used for defining Rho GTPases with strong grade specific connectivity (the proportion of grade II connections / proportion grade IV connections > 0.
4 or < -0. 4). (XLSX) S3 Dataset. Gene Ontology analysis of genes connected to Rho GTPases in the grade II and grade IV gene expression networks. The Gene Ontology analysis of the network neighbourhood of grade II specific Rho GTPases in grade II networks and the network neighbourhood of grade IV specific Rho GTPases in the grade IV networks are shown in separate worksheets. No filtering has been applied, all mapped Gene Ontology terms and the relevant genes are included. (XLSX) S4 Dataset. Gene lists and Gene Ontology terms mapped to gene clusters in the high level map of transcriptional changes in the chicken CAM glioblastoma implantation model. Gene Ontology analysis has been performed on genes regulated at each level of the transcriptional map (1-13hrs up, 13-24hrs down, 13-24hrs up, 37-48hrs down, 37-48hrs up-regulated-).
(XLSX) S5 Dataset. Gene Ontology analysis of genes up-regulated in U87 siRND3 cells compared to U87 siControl cells. All mapped Gene Ontology terms are included. (XLSX) S1 Fig. A comparison of differentially expressed genes between grade II and grade IV glioma in two independent datasets. Enrichment score (ES) plots generated by comparing S1 Text. A novel modularization approach to integrating multiple functional genomics datasets. This document describes the background, methodology and performance of the novel multi-level data integration approach used in this study to generate the networks in Fig 6A. (DOCX) S1 Table.
Moreover, d-and h-ADF measured in the same pairs were positively correlated (Supplementary Fig. 1 ), suggesting that some synaptic connections are more susceptible to AD facilitation, probably because analogue signal propagation along the axon depends on the distance between the soma and the presynaptic terminals. These data demonstrate that h-and d-ADF coexist in CA3 pyramidal neurons and that the underlying mechanisms are likely to be independent. h-ADF was observed in young CA3 neurons (DIV8-10 prepared from P5-P7 rats), and thus it could mainly result from the low density or immature properties of voltage-gated ion channels. We therefore determined whether h-ADF was also found in mature CA3 pyramidal cells. Paired recordings of connected CA3 neurons were obtained in DIV24-DIV32 slice cultures.
5 EGTA, 2 MgCl 2 , 2 Na 2 ATP and 0. 3 NaGTP (pH 7. 4). Recordings were performed at 30 °C in a temperature-controlled recording chamber (Luigs & Neumann, Ratingen, Germany). Usually, the presynaptic neuron was recorded in current clamp and the postsynaptic cell in voltage clamp. Both pre-and postsynaptic cells were held at their resting membrane potential (approximately À 77 mV). Presynaptic APs were generated by injecting brief (5 ms) depolarizing pulses of current at a frequency of 0. 1 Hz. PPR was assessed with two presynaptic stimulations (50-ms interval). Voltage and current signals were low-pass-filtered (3 kHz), and sequences (200-500 ms) were acquired at 10-20 kHz with pClamp (Axon Instruments, Molecular Devices) version 10. Electrophysiological signals were analysed with ClampFit (Axon Instruments) and custom-made softwares written in LabView (National Instruments).
Postsynaptic responses could be averaged following alignment of the presynaptic APs using automatic peak detection. The presence or absence of a synaptic connection was determined on the basis of averages of 30-50 individual traces. TTX (Latoxan, 15-25 nM) was bath-applied. The membrane potential was corrected for the liquid junction potential ( À 13 mV). Pooled data are represented as mean ± s. e. in all the figures, and we used the Mann-Whitney U-test or Wilcoxon rank-signed test for statistical comparison. Calcium imaging. CA3 pyramidal neurons were imaged using a LSM710 Zeiss confocal system. For imaging calcium in CA3 neurons, 50 mM Alexa-594 and 250 mM Fluo-4 (Invitrogen) were added to the pipette solution. Alexa-594 fluorescence was used to reveal neuronal morphology, whereas fluorescence signals emitted by Fluo-4 were used for calcium imaging.
Simultaneous recordings from the soma and axonal cut ends (blebs, at 50-80 mm from the cell body) were obtained in L5 pyramidal neurons. In that case, identification of the axonal bleb was made visually at the surface of the acute slice. Dynamic clamp. Synaptic-ADF experiments (Fig. 2a ) were performed using a dynamic-clamp system (SM-1; Cambridge Conductance, Cambridge, UK) and a computer-generated conductance profile delivered via AD interface (Digidata 1440A; Molecular Devices). The conductance profile was based on recordings of GABAergic currents obtained from CA3 neurons in voltage clamp while stimulating extracellularly the pyramidal layer in the presence of 2 mM kynurenate (Sigma). The conductance waveform follows a double exponential profile: y ¼ À (1 À exp( À (t/t r ))) Â exp( À (t/t d )) , where t r ¼ 4.
Acknowledgements This study was supported by the  Institut National de la Santeet de la Recherche Medicale ,  Centre National de la Recherche Scientifique ,  Agence Nationale de la Recherche  ( ANR Blanc 'REPREK'-11-BSV4-016-01  and  ANR Sante& Bien-Etre  ' AXODE'-14-CE13-0003-02 ),  Fondation pour la Recherche Medicale  (doctoral grant  FDT-2011-0922774  to A. B. , postdoctoral grant  SPF-2011-1223409  to V. M. and grant  DSV-2013-1228768  to D. D. ), the  Ecole Normale Superieure  (doctoral grant to M. Z. ) and the  French Ministry of Research  (doctoral grant to A. B. ). We thank  L. P. Savtchenko  and  J. M. Goaillard  for helpful discussion,  M. Seagar  for comments on the manuscript,  B. Mazet  for help with Matlab and  N. Boumedine  for excellent technical assistance.
0 cm and radiation length of X 0 = 0. 88 cm such that one calorimeter layer corresponds in total to 0. 13 λ I and to 2. 8 X 0. The overall dimensions of the prototype are 0. 9 × 0. 9 × 0. 95 m 3 , amounting to 4. 9 λ I and to 108 X 0. The high granularity of the detector is ensured by the 3 × 3 cm 2 tiles placed in the centre of each active plane, surrounded by 6 × 6 cm 2 and 12 × 12 cm 2 tiles at the edges. The last 8 layers of the stack are equipped with 6 × 6 cm 2 tiles in the centre surrounded by 12 × 12 cm 2 tiles. In total the calorimeter is equipped with 7608 cells. The temperature of each layer is monitored by five sensors in order to correct for the temperature dependence of the SiPM response. The response of all SiPMs is calibrated and monitored with a LED system  [5]. The data were taken in 2011 at the H8 beam line  [9, 10]  of the CERN Super Proton Synchrotron (SPS).
Calibration The response of all calorimeter cells is equalised to a common physics signal produced by minimum ionising particles (MIP), which is used throughout the paper as the energy scale, being the most natural one for the comparison with Monte Carlo simulations. For this calibration, data from dedicated muon runs are used, as muons are considered to be minimum ionising particles. Several steps are necessary to translate signals recorded by the SiPM measured in ADC counts into deposited energy in units of MIP. First a track finding algorithm  [13]  is used to reconstruct the muon track. The energy distribution in each cell for hits associated to a reconstructed muon track is fitted with a Landau function convolved with a Gaussian function. The MIP calibration factor of a single cell i, A MIP i [ADC] is given by the most probable value of the signal of a muon in that cell, expressed in ADC counts.
Data selection The data analysis was performed for e + events with beam momenta from 15 GeV to 40 GeV, π + and proton events with beam momenta from 25 GeV to 150 GeV, and K + events at 50 GeV and 60 GeV. For these data sets, high selection purity can be achieved  [16]  and leakage effects are limited (cf. section 4. 2). For most of the particles and beam momenta, 20 000-100 000 events (figure  4 ) pass the selection steps introduced in the following. For 60 GeV and 80 GeV, high statistics hadron data sets were recorded with approximately up to 700 000 events after event selection. Beam momenta with particularly low number of events after event selection are 40 GeV e + (6 000 events), 50 GeV K + (3 000 events), and 150 GeV π + (5 000 events).
Monte Carlo simulation The simulation includes the W-AHCAL, the two trigger scintillators and the three wire chambers. Particles of a given type and energy are generated at z = -55 m. The transverse beam profiles in the simulations are chosen such that they reflect the beam profiles measured in the corresponding data runs. Interactions are modelled using the QGSP_BERT_HP and the FTFP_BERT_HP physics lists of Geant4 version 9. 6. p02. These lists are described in  [12]. The extension HP indicates that the physics lists use a data driven high precision neutron package for the transport of neutrons below 20 MeV down to thermal energies. This is crucial for the simulation of the high-A absorber tungsten where many more neutrons due to spallation are produced than in steel absorbers  [19]. The size of the Monte Carlo data sets are chosen such that they reflect the approximate number of events of the individual data sets used in the analysis.
Systematic uncertainties The most relevant sources of systematic uncertainties for the W-AHCAL test beam data are the limited knowledge of the saturation scaling factor of the SiPM response function, the uncertainty in the MIP calibration, the stability of the W-AHCAL response over time, and the choice of the event selection based on the shower start layer. For the Geant4 simulations, the most relevant systematic uncertainty originates from the simulation of the cross-talk between neighbouring scintillator tiles. The systematic uncertainties due to all relevant sources and all discussed observables are studied and quantified. In the following, for brevity, only the systematic uncertainty on the reconstructed visible energy and, in specific cases, on the longitudinal or the radial energy profile are quoted.
Summary and conclusions We have presented analysis results of e + , π + , K + , and proton data with beam momenta from 15 GeV to 150 GeV recorded with the CALICE W-AHCAL prototype in 2011 at the CERN SPS. The measured calorimeter response, energy resolution, and the longitudinal and radial shower development are compared to predictions of the Geant4 physics lists QGSP_BERT_HP and FTFP_BERT_HP as implemented in Geant4 version 9. 6. p02. The simulations show a reasonable overall agreement with experimental data. As electromagnetic showers are well understood, a comparison of positron-induced showers between experimental data and simulations is used to validate the detector calibration and the implementation of the detector simulation. Data and simulation agree with each other within the uncertainties for the detector response and the energy resolution.