dataesr/scientific-paragraphs-categorization huggingface dataset:
Even though the distribution of Voronoi cell areas of the 2D-projected dataset in Fig. 1C  lies within the confidence envelope for the background model indicating no clustering, we segmented this dataset keeping only the regions with the local density stronger than the mean density calculated from the 2D Voronoi diagram  5. The resulting clusters, as expected, have random size much smaller than the original radius of 30 nm (Fig. 1C , left and right panels). We have also implemented the averaging of the local densities using the first-rank 5 neighbors in 3D. This option helps to remove spurious clusters in the background but it also leads to reduction of the volume of the detected clusters because the density of the near-border regions within clusters is averaged with the low-dense background (Fig.
21 The standalone software for visualization of 3D Voronoi tessellations was written in Python using the Mayavi data visualization library  6  along with NumPy 7 and SciPy 8. SMLM datasets are loaded as a 3D CSV point list file. The user can then navigate through the 3D Voronoi cells determined by the input points. The cells are colored according to their size, from blue (big cells) to red (small cells). The user can set a cell size threshold to see only smaller cells where events are aggregated, use a clipping box to see inside the cells and toggle the drawing of the event points. The size of the cells is computed in parallel using QHull's "ConvexHull" function  3  from SciPy. Cells delimited by vertices outside of the event range are merely ignored. The Mayavi interface allows the user to change a lot of drawing parameters on-thefly.
Colors, opacity, orientations, lightning, clipping shape etc. are all tunable through the built-in parameter adjustment wizard. As an example, it takes 6 hours to segment a large experimental dataset with a volume of ~200 µm 3 that contains 10 6 localizations using an i7 quad-core single processor with 32 Gb of memory. This is much faster in a single run than what could be done for example with the current 3D implementation of DBSCAN  9  in VividSTORM 10 where one single run takes 8 hours for 67 000 points (which is a rather small data set). In contrast, our Voronoi-based segmentation uses an automatically determined threshold value and takes in total 6 hours for 1 000 000 points (15 times as many) processed in one single run, i. e. it is more robust, non-dependent on user-set parameters and runs faster (in total range of 30-100 times faster depending on the number of trials needed and the complexity of the data).
Acknowledgements We thank  Leica Microsystems  for the access to the Leica SR GSD 3D system and  Didier Hentsch ,  Pascal Kessler  and  Yves Lutz  of the  IGBMC Imaging Centre  for support. This research was made possible by the adaptive optics plug-and-play accessory MicAO 3D-SR for PALM/STORM microscopes of Imagine Optic (www. imagine-optic. com). This work was supported by the  INCa programme  and the  Centre Nationale pour la Recherche Scientifique (CNRS). The super-resolution microscope setup is supported by the  Alsace Region  and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  and  Instruct  as part of the  European Strategy Forum   on Research Infrastructures (ESFRI).
5 of 5 influence of the corresponding fluorophore molecules (Fig. 2B). Because the geometrical properties of Voronoi regions reflect the environment characteristics of a given molecule, we can use their short-range collective properties determined from the diagram in addition to the experimentally determined coordinates and intensities of the fluorophores. One of the most important properties, the density d in the neighborhood of the molecule i, can be determined simply as the inverse value of the volume V of the Voronoi cell: d i = 1/V i. To segment a SMLM dataset, every localization should be assigned to a cluster or to the background. The minimal local density of molecules inside clusters, i. e. the segmentation threshold, can be determined automatically by comparing the distribution of Voronoi cell sizes of the experimental distribution with that of the background noise model, as has been recently shown by us for the 2D case 19.
Indeed, using scanning electron microscopy (SEM) on the mesenteries remodeled by cancer cells and CAFs, we observed frequent holes often surrounded by big bundles of the ECM that were absent in control BM (Supplementary Fig. 2d ). To test the physical properties of remodeled BM, we measured BM stiffness under different conditions using atomic force microscopy (AFM). Compared with control BM, BM modified by cancer cells and CAFs was significantly softer (Fig. 2e ). It showed a marked reduction in fibers of intermediate stiffness, consisting instead of patches of soft, inhomogeneous material sparsely interspersed with thick fibers, which also increased the overall roughness of the BM (Fig. 2e ). These data reveal that the decrease in BM stiffness is most likely the result of rearrangements of ECM networks, as CAFs alter the molecular composition, organization, and physical properties of the BM, making it permissive to cancer cell invasion.
Sections were mounted in AquaPolymount (Polysciences). To estimate the enrichment of CAFs in the stroma, samples were stained with vimentin, a fibroblast marker and with αSMA, a CAFs marker. For each sample, images of five different fields of the tumor and one of the normal adjacent tissue were acquired using ×10 objective of an epifluorescence microscope (Leica DM6000B) equipped with a Cool Snap CCD camera. Images analysis was performed using Metamorph software (Roper scientific). Proportion of CAFs in stroma was calculated as a ratio between areas positive for αSMA (general marker of CAFs) and vimentin (label both CAFs and non-activated fibroblasts). Muscle tissue was not included while areas of αSMA staining around the vessels was subtracted from all images. For each patient, the values are presented as average of the five randomly chosen fields from the same sample.
Images were acquired with an inverted AOBS two-photon laser scanning confocal microscope SP8 (Leica) coupled to femtosecond laser Chameleon Vision II, Ti: Sapphire pumped Optical Parametric Oscillator (680-1400 nm) (Coherent Inc) using 25×/1. 0NA water-immersion objective. The microscope is equipped with two non-descanned detectors: NDD1 (500-550 nm), NDD2 (≥590 nm). Fluorescence channels were recorded simultaneously using the excitation wavelength 960 nm. BM was visualized by reflection microscopy, using light at a wavelength of 488 nm and a standard PMT detector, at a low gain (value of 500 in a Leica SP8). Images were recorded every 15 min up to 72 h. 3D stacks were obtained at a step size of 1 µm intervals. Data are analyzed from 1-2 experiments. The images were processed with Leica Application Suite (LAS), ImageJ (NIH), or Imaris (Bitplane).
Images were processed in LAS AF Lite software, ImageJ, and Imaris. The size of the gap was measured at each time point using ImageJ. Increase in hole size was presented as a ratio between the size of the hole at t = 0 and t = 12 h in %. Scanning electron microscopy. Sample preparation: BM samples were fixed in 2. 5% glutaraldehyde solution, in a 0. 1 M cacodylate buffer overnight, at 4 °C. They were then washed three times in a 0. 2 M cacodylate buffer and post-fixed for 30 min at RT with 2% osmium tetroxide in 0. 2 M cacodylate buffer. Samples were then dehydrated in a series of graded ethanol baths (25, 50, 75, 95, and 100%) and then transferred to the SEM lab where they were treated for critical point drying by CO 2 , using Baltec CPD030 technology and gold-palladium metallization, using an Ion Beam Coater GATAN.
The medium was changed at Day 4 and consequently AFM force mapping was performed as described below. AFM force mapping. The AFM was comprised of a Nanonis AFM Controller (SPECS Zurich GmBH, Switzerland) using a custom written software. For all measurements of mesenteries, DNP- AFM data analysis. Force maps were analyzed using the custom-made software in Labview. All curves were transformed to force vs. tip-sample distance. First the tilt and offset were corrected. Subsequently the contact area was fitted using a power law (power = 2) and the non-contact area with a straight line. The E-modulus was calculated from the unloading curve and according to the modified Oliver-Pharr model (Plodinec and Lim  37  ). For roughness analysis, quasi-topography data were extracted from contact points measured during force mapping.
QI imaging mode provided the topography data. These were then imported into Gwyddion, where a plane level fit was performed and the RMS of the area was used to quantify the surface roughness of mesenteries. For the high-resolution data, in total 12 maps were generated each comprising 10,000 force curves. Mesentery data were analyzed in least three independent experiments (for high-resolution, counting one mesentery as one experiment) and six low-resolution experiments (two mesenteries for each condition) from which the representative mesenteries were used for highresolution testing). The nanomechanical measurements of HCT16 cells were performed on minimum three different culture dishes per condition and by measuring at least four maps per condition. Proteomic analysis of CAFs and NAFs. To prepare SILAC media, SILAC DMEM lacking two amino acids, arginine and lysine was (Life Technologies) was supplemented with their isotopically labeled counterparts.
Tandem MS was performed by isolation at 1. 6 Th with the quadrupole, HCD fragmentation with normalized collision energy of 35, and rapid scan MS analysis in the ion trap. The MS2 ion count target was set to 104 and the max injection time was 100 ms. Only those precursors with charge state 2-7 were sampled for MS2. The dynamic exclusion duration was set to 60 s with a 10 ppm tolerance around the selected precursor and its isotopes. The instrument was run in top speed mode with 3 s cycles. Data analysis was performed at the Institut Curie laboratory of mass spectrometry proteomics. Raw MS files from the Orbitrap were analyzed via Sequest HT with Proteome Discoverer (1. 4, Thermo Scientific) using the Uniprot Human database (032015). Enzyme specificity was set to trypsin and a maximum of two miss cleavages was allowed.
Oxidized methionine, N-terminal acetylation, carbamidomethyl cysteine, label 13C615N2 Lysine, and label 2H4 lysine were set as variable modifications. For identification, the false discovery rate (FDR) was set to 1% with Percolator q-values. The resulting files were further processed by using the Institut Curie-developed software myProMS 38 version 3. 0 (work in progress), which performs search engine results validation, false positive rate (FDR)-based data filtering, protein quantification, statistical analysis, and data visualization. For SILAC-based protein quantification, peptides XICs (extracted ion chromatograms) were retrieved from Proteome Discoverer. Scale normalization was applied to compensate for mixing errors of the different SILAC cultures as described by Yang et al. 39  Protein ratios were computed as the geometrical mean of related peptides.
About 100 ng of total RNA was amplified, converted to complimentary DNA (cDNA), and labeled according to Affymetrix recommendations based on the WT. Amplified molecules were controlled after purification steps to monitor yields and sized of molecules. The labeled cDNA was hybridized and analyzed on Affymetrix GeneChip Human Gene 2. 1 ST arrays, using the Genetitan device at the Genomic platform of Institut Curie. The data were analyzed by Genosplice (Paris).
Acknowledgements We thank all members of the  Vignjevic lab ,  G. Montagnac ,  M. Piel ,  A. M. Lennon ,  D. Louvard ,  G. Borisy  for helpful discussions. M. P. and P. O. would like to thank  Prof. Roderick Y. H. Lim  for advice during manuscript preparation and for providing the laboratory and microscopy infrastructure. We also thank  O. Leroy ,  O. Renaud ,  G. Arras ,  V. Masson  for help with imaging, FACS, and mass spec experiments; and all members of the animal house facility. We acknowledge imaging facilities PICT- IBiSA@Lhomond and PICT@BDD. We also thank  Région Ile-de-France »  ( 2013-2-EML-02-ICR-1  and  2014-2-INV-04-ICR-1 ) and  Fondation pour la Recherche Médicale  ( DGE 20121125630 ) grants (to D. L. ), the NanoteraProject, awarded to the  PATLiSciII Consortium (M. P and P.
Data availability. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD003670. The gene array data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE78947 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE78947). All analyzed data are available within the article and Supplementary Files, or available from the authors upon request.
ES is a multilayer snowpack scheme of intermediate complexity. Based on Crocus parameterizations, ES will be used for the upcoming CMIP6 CNRM-CM simulations  (Eyring et al. , 2016). Similar studies were performed over Siberian regions, using ISBA-Crocus  (Brun et al. , 2013)  and ISBA-ES  (Decharme et al. , 2016). Models were evaluated based on snow height, snow water equivalent and soil temperature, but processes related to the simulated snowpacks have not been studied in detail yet. Here, simulations are tested using field data obtained at one specific site on Bylot Island (73 • N, 80 • W), located north of Baffin Island in the Canadian Arctic archipelago. The mean annual air temperature for the period 1998-2013 is -14. 3 • C and permafrost thickness has been estimated to be over 400 m  (Fortier and Allard, 2004).
, 2016a). It measures air temperature and relative humidity with a ventilated HC2S3 sensor from Rotronic, wind speed with a cup anemometer, surface temperature from an infrared IR120 sensor, and upwelling and downwelling shortwave and longwave radiation with a CNR4 radiometer associated with a CNF4 heating/ventilating unit from Kipp & Zonen. An issue with the HC2S3 sensor caused erroneous air temperature measurements in the first year. This was fixed in summer 2014, providing a fairly complete meteorological dataset since then. Snow height was automatically monitored with a SR50A acoustic gauge installed on the automatic weather station. A few meters further, three TP08 heated needle probes (NPs) from Hukseflux were placed at 7, 17 and 27 cm above the ground in summer 2013 to measure the snow temperature and its thermal conductivity k snow.
As we are focusing on the station site, we will only use data from the three snow pits dug within 20 m of the station both years. Soil temperature and volumetric water content (VWC) have been monitored since July 2013 with Decagon 5TM probes, installed at depths of 2, 5, 10 and 15 cm. They were not calibrated for our specific soil, so we used the manufacturer's calibration for mineral soils which may produce an error in water content of 3 %. The temperature sensor accuracy is within ±1 • C. The active layer was 17 cm thick at the time of installation so the sensors could not be placed deeper. A TP08 heated needle probe was inserted at 10 cm depth, just below the litter layer, to automatically monitor the soil thermal conductivity k soil. The method used is the same as for the k snow measurements, and data analysis is also detailed in  Domine et al.
Meteorological driving data To calculate the energy and mass budget of the surface, the model needs the following input data: air temperature and specific humidity, wind speed, incoming shortwave and longwave radiation, precipitation rate (solid and liquid) and atmospheric pressure. We used observed local meteorological data when available, and missing data are filled with ERA-Interim (ERAi) reanalysis  (Dee et al. , 2011). Available from 1979 to present, for a 0. 7 • grid with a 3-hourly time resolution, ERAi provides a continuous meteorological dataset available globally. Air temperature and wind speed observations are available since 2004 from the 10 m tower. After July 2014, we used the ventilated air temperature and humidity, and the wind speed measured at BylSta. Radiation measurements were not used because the radiometer shifted by about 5 • from its horizontal position when the tripod that supported it sank with the active layer thawing, causing errors in the data.
The atmospheric pressure and precipitation were not measured, so we used ERAi reanalysis data for missing variables and possible data gaps. To make the ERAi data consistent with the original field data, they were corrected following the method of  Vuichard and Papale (2015). This method consists of calculating the linear regression between ERAi data and available field measurements. The regression coefficients (slope and intercept) are used to correct systematic biases in ERAi data, in order to better match the local meteorological conditions. Hence, we found a mean bias of -2. 3 • C in air temperature, -2. 7 × 10 -4 g kg -1 in air humidity and +0. 7 m s -1 in wind speed given by ERAi. The correction helped to reduce the standard deviation between the corrected ERAi and the observational data by respectively 20, 4.
ISBA includes the dependency of soil hydraulic and thermal properties on SOC content as fully described in  Decharme et al. (2016). Briefly, depending on its content, which decreases sharply with soil depth, including organic carbon reduces the dry soil thermal conductivity, increases its porosity and therefore increases its saturated hydraulic conductivity. Accounting for the effect of SOC could significantly help improve the simulation of the soil thermal regime, especially in Arctic areas where soils store large amounts of SOC  (Hugelius et al. , 2014). From the Harmonized World Soil Database (HWSD; FAO, 2012) with a 1 km resolution, the SOC content for our study site is estimated at 5. 05 kg m -3 for the first soil horizon (0-30 cm), and 4. 34 kg m -3 from 30 cm to 1 m depth. A few soil samples were collected in July 2013 on the southern plain of Bylot Island for a radio-carbon analysis (ADAPT 2014).
1 and 2) are not appropriate for our dataset. The simulated k snow values are almost always higher than measurements, and accounting for the  Sun et al. (1999)  additional term (run ES) amplifies the error. The regression curve from  Sturm et al. (1997)  fits our measurements better, because this parameterization is based on Arctic and subarctic snows instead of focusing on alpine conditions. However, for a given density, the k snow value can vary by a factor of 4 to 5  (Domine et al. , 2016a; Sturm et al. , 1997)  so that density-k snow correlations cannot be used to accurately determine the thermal conductivity of Arctic snow. In fact, as demonstrated by  Löwe et al. (2013) , snow thermal conductivity depends on both density and microstructure. By adding second-order bounds based on a microstructural anisotropy parameter, they could improve the estimation of k snow.
Acknowledgements. This work was supported by the  French Polar Institute (IPEV)  through grant  1042  to Florent Domine, by  NSERC  through the  Discovery Grants Program  and by the  BNP Paribas Foundation. Mathieu Barrere received scholarships from Envi-roNord, and grants from  Consulat Général de France à Québec and Région Rhône-Alpes. The  Polar Continental Shelf Program (PCSP) efficiently provided logistical support for the research at Bylot Island. We are grateful to  Denis Sarrazin  for his precious technical support on the CEN weather stations, and to  Gilles Gauthier  and  Marie-Christine Cadieux  for their decades-long efforts to build and maintain the research base of the  Centre d'Etudes Nordiques at Bylot Island. Bylot Island  is located within  Sirmilik National Park , and we thank  Parks Canada  and the  Pond Inlet community (Mittimatalik)  for permission to work there.
Code and data availability. Forcing data used for this research and evaluation data of Figs. 2, 6, 8, 10 and 11  are available for further use in the scientific community at Nordicana D, https://doi. org/10. 5885/45460CE-9B80A99D55F94D95 (Barrere and  Domine, 2017). The strong differences between Arctic and alpine snowpack types encourage to incorporate such datasets in the upcoming model intercomparison exercises, in particular ESM-SnowMIP (http://www. geos. ed. ac. uk/~ressery/ ESM-SnowMIP. html) which currently does not include sites featuring tundra snow types. The model is open source and it is available via the SURFEX platform which can be downloaded at http://www. umr-cnrm. fr/surfex/. The simulations presented in this paper were realized with SURFEX version 8. 0, revision 4006.
Tumor specimens were also analyzed by mRNA sequencing, miRNA sequencing, DNA methylation arrays and reverse phase protein arrays for targeted proteome analysis. Our analysis strategy involved a systematic interrogation by platform to identify genomic alterations in PCC/PGL, including germline mutations, somatic mutations, fusion genes and copy number alterations. Multi-platform integration and computational analysis was then performed to (1) characterize the broad molecular correlates of prominent driver alterations; (2) identify a PCC/PGL molecular subtype classification; (3) identify disrupted pathways; and (4) identify molecular discriminants of metastatic disease. The integrated clinical and genomic datasets are available through the NCI's Genomic Data Commons.
To our knowledge, CSDE1 has not been described as a driver gene in any cancer type nor been previously associated with the Wnt signaling pathway. Querying a cancer mutation a database  (Cerami et al. , 2012)  for CSDE1 returned rare truncating mutations in other tumor types, suggesting that CSDE1 may have a driver role in other cancers. Our results provide significant, clinically-relevant information by confirming and identifying several molecular markers -including germline mutations in SDHB, somatic mutations in ATRX and fusions involving MAML3 -that were associated with an increased risk of aggressive and metastatic disease. The molecular alterations described herein also may serve as potential drug targets. For example, SDH-mutant tumors have high levels of glutamine, and glutaminase inhibitors  (Gross et al.
, 2014)  are currently being evaluated in NCT02071862. As the MAML3 fusion genes activate Wnt signaling, downstream inhibitors, such as antagonists of β-catenin (PRI-274)  (Lenz and Kahn, 2014)  and STAT3 (BB1608), merit investigation. Cancers with alternative lengthening of telomeres associated with loss of ATRX have been shown to be sensitive to ATR inhibitors  (Flynn et al. , 2015b). Finally, FDA-approved targeted therapies are available for patients whose tumors carry VHL, RET, BRAF, EPAS1 and FGFR1 mutations. In summary, our comprehensive characterization significantly advances the molecular understanding of PCC/PGLs and enables the advancement of precision medicine for this rare disease.
Experimental Procedures Samples and clinical data PCC/PGL tumor tissue, normal tissue and blood samples were obtained from patients with informed consent and with approval from local institutional review boards (IRB) at tissue source sites (see Supplemental Experimental Procedures). Cases with neoadjuvant treatment were excluded. Head and neck PGLs were not included because such tumors are often embolized prior to surgery leaving excessive necrotic tumor tissue that is insufficient for molecular analysis. Adjacent normal tissues were at least two cm away from the tumor, mostly in the adrenal cortex. An expert endocrine pathologist (A. S. T. ) reviewed frozen sections to confirm PCC/PGL diagnosis and to determine the presence of any cortical cells in the tissue. Clinical records were analyzed by an expert subcommittee to assign clinical outcome events by consensus definitions.