dataesr/scientific-paragraphs-categorization huggingface dataset:
e. the largest ones) are taken into account when a large separation is retained. A direct assessment of the scale dependence is, however, greatly preferable. This may be obtained by band-pass filtering the signals (see e. g. Fisher & Davies 1963; Wills 1964; Favre et al. 1967; Goldschmidt et al. 1981; Kim & Hussain 1993). However, the phase of the signals must be rigorously preserved by the filter and aliasing should also be avoided  (Goldschmidt et al. 1981). To circumvent these difficulties, an intrinsically spectral method is preferred. In a flow that is statistically homogeneous along x, stationary in time and when highly resolved data are available, a spectral convection velocity can be assessed from the frequency-wavenumber spectrum. Considering the space-time Fourier transform û(k x /(2π), f ) of u(x, t), û k x 2π , f = ∞ -∞ ∞ -∞ u(x, t) exp(-i(2πft + k x x)) dt dx, (1.
Data storage must consequently be chosen in order to procure satisfying accuracy in the wavenumber range of interest specific to the available database, as illustrated in the next section. 3. Method illustration in a zero-pressure-gradient boundary layer at Re θ = 13 000 The method derived in the previous section is meant for spatially developing turbulent flows, thanks to its local streamwise formulation, and for high Reynolds numbers, where it significantly reduces data storage compared with methods based on frequency-wavenumber spectra. A natural test case is the spatially developing boundary layer at high Reynolds numbers. Superstructures cannot be easily observed at Reynolds numbers smaller than Re θ = 10 4 because of the insufficient scale separation from other coherent motions. However, as recalled in the Introduction, no subsonic direct numerical simulation over a long streamwise numerical domain favourable to the full development of the largest coherent motions has been published for Re θ > 10 4 , because of the computational cost involved.
As a result, the new method is assessed on the resolved fluctuations from a wall-resolved large eddy simulation (WRLES). This not only indicates what may be expected from a future DNS dataset, especially thanks to the high resolution in the outer layer, and enables comparison with classical streamwise spectrum reconstruction methods, but also illustrates the relevance of convection velocity information to investigate the accuracy of turbulence models by determining whether or not resolved fluctuations are physically grounded, for example in the inner layer which is under-resolved compared with DNS. The numerical dataset used for the present illustration at Re θ = 13 000 has been thoroughly validated in  Deck et al. (2014b)  and is briefly presented in the following.
Turbulent boundary layer dataset used for the illustration The compressible Navier-Stokes equations are solved on multiblock-structured grids using the FLU3M code developed by ONERA. Implicit time integration is carried out by the second-order backward scheme of Gear. The convective fluxes are discretized by a modified low-dissipative AUSM + (P) scheme  (Mary & Sagaut 2002). The solver accuracy has been assessed for direct numerical simulation and large eddy simulation by comparison with experimental data including spectra in many studies The WRLES mesh resolution is x + = 50 and z + = 12 in the streamwise and spanwise directions respectively, so that the inner site dynamics is not modelled, even though it is under-resolved compared with DNS. The first mesh cell is located within one wall unit from the wall.
The numerical dataset has been thoroughly validated, including spectral data, in  Deck et al. (2014b). Two visualizations relevant to the present study are presented in figures 1 and 2. This provides an insight not only into the shape of the largest resolved coherent motions near Re θ = 13 000 but also into physical space when interpreting spectral data. Figure  1  presents a numerical schlieren in a streamwise/wall-normal plane near the Re θ = 13 000 station, revealing the presence of inclined structures through the whole boundary layer thickness, which suggest packets of hairpins, consistent with the experimental findings leading to the hairpin packet paradigm of  Adrian et al. (2000). Some of these structures are indicated by dashed lines. The length of these packets is typically 1 δ to 3 δ, scaling with the local boundary layer thickness.
In figure  5  the footprint of the largest scales is indeed faster (consistent with  Hutchins et al. 2011)  and their top is slower than the other structures. k x G uu (k x )/u 2 τ. The convection velocity is compared with the mean velocity in figure  7 (a). Near the wall, it is significantly larger than the mean velocity at all length scales. It is slightly smaller than the mean in the outer part of the outer layer, in good qualitative agreement with  Favre et al. (1967) , del Álamo & Jiménez (2009),  Chung & McKeon (2010) ,  Lee & Sung (2011)  and  Atkinson et al. (2015) , among others. However, the lack of datasets matching the present Reynolds number and type of flow prevents a direct comparison. The three energy sites (inner, outer in the range of scales of the hairpin packets, and outer superstructure site) feature a common property: the convection velocity is higher than the mean velocity near the foot of the site, and lower than the mean near the top of the site.
The classical convection velocity defined as the ratio x max (τ )/τ and which depends on τ may be seen as the mean velocity of this frame of reference during the time τ. The frame is not necessarily inertial, because its instantaneous velocity d( x max (τ ))/dτ may vary in time. Favre et al. (1967)  consider the value of d( x max (τ ))/dτ at a given time τ as another possible definition of the convection velocity (but this is done in a mathematical way, with no reference to the instantaneous velocity of a moving frame). Furthermore,  Favre et al. (1967)  show that if the flow is homogeneous along x, both mean and instantaneous velocities coincide when τ vanishes, and are given by As mentioned in § 5. 2, the present dataset requires dual definitions, all the more as the flow is spatially developing.
The relation between the present global convection velocity C u and the micro scale is emphasized by the weight f 2 G uu ( f ) in (5. 1) for the weighted harmonic average giving C u from U c ( f ), confirming that C u and U corr are dominated by the dissipative scales and explaining the results of figure  10  for the smaller-scale-restricted convection velocity. This, however, implies that C u and U corr (massively used in experiments) are dominated by the smallest resolved scales, which are not necessarily the dissipative scales if an under-resolved simulation or experiment is considered, possibly explaining some discrepancies between datasets of different resolution (see e. g. a comparison in  LeHew et al. 2011)  if the convection velocity of the dissipative scales and of larger scales is different.
Conclusion The wavelength-dependent convection velocity of large-eddy-simulation-resolved streamwise velocity fluctuations has been assessed in a zero-pressure-gradient smooth flat plate turbulent boundary layer across the whole boundary layer profile at Re θ = 13 000. The Reynolds number is high enough for the convection of superstructures to be observed, but the extreme computational cost of the simulation is out of reach of the published subsonic DNS datasets, so that a WRLES has been used, resorting to the zonal detached eddy simulation (ZDES) mode 3 technique and validated in  Deck et al. (2014b). As no existing method would satisfactorily address the spatial growth of the boundary layer and the data storage implied by the high Reynolds number while enabling a thorough physical interpretation of the obtained values, a new spectral method has been derived in the present study.
It is also concluded that the correlation-based assessment of the convection velocity from experiments may similarly depend on the probe resolution. The present method not only adds the scale dependence description level, but also enables the rejection of the smallest scales when assessing the global convection velocity. This could be a way of comparing datasets with different degrees of resolution. The study of convection velocity may be seen as an additional approach to coherent motions at high Reynolds numbers. Coherent motions are associated not only with a spectral energy site, but also with an almost uniform convection velocity zone, as explained by del  Álamo & Jiménez (2009)  and illustrated here. The study would be enhanced by simulations at even higher Reynolds numbers with clearer scale separation.
The numerical dataset has been thoroughly validated, including spectral data, inDeck et al. (2014b). Two visualizations relevant to the present study are presented in figures 1 and 2. This provides an insight not only into the shape of the largest resolved coherent motions near Re θ = 13 000 but also into physical space when interpreting spectral data. Figure1presents a numerical schlieren in a streamwise/wall-normal plane near the Re θ = 13 000 station, revealing the presence of inclined structures through the whole.
Acknowledgements This work was performed using HPC resources from  GENCI-CINES  (Project ZDESWALLTURB, grant  2012-[c2012026817 ]). The authors wish to thank  P. Sagaut ,  P. -É. Weiss ,  R. Laraufie  and  L. Larchevêque  for fruitful discussions. All the people involved in the evolution of the FLU3M code are warmly acknowledged. The thesis of N. R. is partly funded by the  French defence procurement agency DGA.
2a, b ). No FITC signal was present in the lungs that were not inoculated with tumour cells (data not shown). However, FITC-Dextran was detected at significant levels within lungs from mice inoculated with SPARC-competent cells as evidenced by the diffuse areas of fluorescence signal observed in lung sections. In contrast, the intensity of fluorescence was significantly abolished by SPARC silencing (Fig. 2c ). Permeable Dextran exudation was quantified in the lysates of individual lungs. These analyses confirmed that compared to control cells, vascular permeability was dramatically reduced in lungs from mice inoculated with SPARCdeficient tumour cells, indicating that knockdown of SPARC prevents tumour-induced permeability of lung capillaries (Fig. 2d ). By contrast, 501Mel cells overexpressing SPARC showed a significant increased in the amount of FITC-Dextran lodged in the lungs compared to parental cells (Fig.
Images were acquired on a Zeiss LSM 510 META laser scanning confocal microscope. Lung homogenates were prepared in RIPA buffer supplemented with protease inhibitors and phosphatase inhibitors, briefly sonicated and quantified using a protein assay kit (Bio-Rad) according to the manufacturer's manual. Fluorescence was measured in lung homogenates using a Fluoroskan microplate reader (excitation, 485 nm; emission, 530 nm). Quantification of extravasated FITC-dextran was determined in lung homogenates from different animals and calculated against a FITC-Dextran standard curve. Data are expressed as µg of FITC-Dextran per µg of protein from lung homogenates. For short-term lung colonization assays, melanoma cell populations were differentially labelled with 5 µM CellTracker Green CMFDA or CellTracker Orange CMTMR dyes as described  56.
Equal numbers (7. 5x10 5 ) were coinjected into the tail veins of nude mice, which were sacrificed 30 min or 24 h later. At 24 h, lungs were flushed and perfused free of blood with PBS to remove intravascular tumour cells. Fluorescently labelled cells in lungs were counted microscopically. For experimental lung metastasis studies, 1x10 6 melanoma cells engineered to express a luciferase reporter gene (LUC+ cells) were resuspended in 100 µl of PBS and injected into the tail veins of nude mice. Lung metastasis was monitored and quantified using bioluminescence imaging (BLI). Mice were anesthetized and injected intraperitoneally with 50 mg/kg D-luciferin (PerkinElmer) in PBS. Images were acquired using a Photon Imager (Biospace Lab) system and data analysed with the M3Vision software (Biospace Lab).
Cells were allowed to migrate for 7-24 h at 37°C in 5 % CO 2. Complete EGM2 medium plus 10 % FBS was used as a chemoattractant. The Transwells were then fixed in 4 % paraformaldehyde, cells on the apical side of each insert were scraped off, and the Transwell membrane mounted onto glass slides. Migration to the basolateral side of the membrane was visualized with a Zeiss (PALM) immunofluorescent microscope at x5 magnification. Pictures of 5 random fields were captured for quantification using NIH ImageJ analysis software. All experiments were performed in duplicates and repeated at least three times. Tumor-endothelial cell adhesion assay. The adhesion assays were performed in black 96-well culture dishes in triplicate. HUVECs were seeded onto gelatin, allowed to grow to confluence for 4 days and stimulated for 16 h with TNFα (10 ng/ml).
Melanoma cells were pulsed with 5 µM CellTracker Green CMFDA for 30 min and collected by HyQTase treatment. Fluorescent dye-labelled cells were then allowed to adhere onto the endothelial cell monolayer for 7 h. Non-adherent cells were removed by washing with PBS supplemented with 1 mM CaCl 2 and 1 mM MgCl 2 and fluorescence was measured. Data are expressed as the percentage of adherent cells per total cells. Detection of ROS by flow cytometry. ROS levels were measured using the redox-sensitive dye CM-H 2 DCFDA (Molecular Probes, Life Technologies). The green fluorescence was collected using a Miltenyi Biotec flow cytometer and the MACSQuantify software for acquisition and analysis. Human samples and immunohistochemistry. Lung metastatic melanoma specimens were obtained from the consenting patient through the Histopathology and Dermatology Departments, Nice University Hospital, France.
Acknowledgments We thank  R. Ballotti ,  R. Halaban ,  M. Herlyn ,  D. Figarella-Branger  and  E. Van Obberghen -schilling for tumour cells and members of C3M's research team 11 for helpful discussions. We also greatly acknowledge the C3M animal room facility and the  C3M imaging core facility (Microscopy and Imaging platform Côte d'Azur, MICA). We are thankful to  E. Baudelet  for technical assistance in MS analysis. This work was funded by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Fondation ARC , the  Fondation de France  and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference #  ANR-11-LABX-0028-01. The Marseille Proteomic facility (MaP; http://map. univmed. fr/) is supported by  IBiSA  ( Infrastructures Biologie Santé et Agronomie ),  Canceropôle PACA ,  Région PACA  and  Institut Paoli-Calmettes.
Satellite-derived sea ice concentration (SIC) and reanalyzed atmospheric data are used to explore the predictability of the winter Euro-Atlantic climate resulting from autumn SIC variability over the Barents-Kara Seas region (SIC/BK). The period of study is 1979/80-2012/13. Maximum covariance analyses show that the leading predictand is indistinguishable from the North Atlantic Oscillation (NAO). The leading covariability mode between September SIC/BK and winter North Atlantic-European sea level pressure (SLP) is not significant, indicating that no empirical prediction skill can be achieved. The leading covariability mode with either October or November SIC/BK is moderately significant (significance levels ,10%), and both predictor fields yield a cross-validated NAO correlation of 0. 3, suggesting some empirical prediction skill of the winter NAO index, with sea ice reduction in the Barents-Kara Seas being accompanied by a negative NAO phase in winter.
Datasets and methodology This study is based on lagged maximum covariance analysis (MCA; e. g. ,  Bretherton et al. 1992; Czaja and Frankignoul 2002 ) between winter sea level pressure (SLP) anomalies, the predictand, and SIC anomalies, the predictor. The MCA estimates the main modes of covariability between two fields while making no a priori assumption on the spatial patterns. It performs a singular value decomposition of their (area weighted) covariance matrix, and provides a pair of spatial patterns and associated standardized time series (hereafter, expansion coefficients) for each covariability mode. Each mode is characterized by the squared covariance (sc), the squared covariance fraction (scf), a measure of the fraction of covariability explained, and the correlation between the expansion coefficients (cor).
The predictand field is SLP anomalies over the North Atlantic-European region 208-908N, 908W-408E averaged from December to February (DJF) as given by the ERA-Interim (hereafter ERA-Int), available from the European Centre for Medium-Range Weather Forecasts (ECMWF;  Dee et al. 2011). The same region and season are used to define the winter NAO index, obtained as the leading principal component of detrended winter SLP anomalies (Fig. 1 ; e. g. ,  Hurrell et al. 2003) ; the correlation between the detrended (Fig. 1b , solid line) and undetrended (Fig. 1b , dashed line) winter NAO index is 0. 96. The predictor fields are monthly Arctic SIC anomalies in September-November, which provide 3-to 1-month lead times. The SIC dataset is from the NOAA/NCDC passive microwave monthly Northern Hemisphere sea ice concentration record, provided by the National Snow and Ice Data Center (NSIDC; Comiso 2012).
No threshold for the absence or presence of sea ice has been applied (i. e. , continuous fractions have been used). We consider the 1979/80-2012/13 period. To explore the dynamical mechanisms involved in the SIC influence, different atmospheric fields are extracted from ERA-Int. In addition to SLP, daily geopotential height from 1000 to 1 hPa (37 vertical levels) is used. Daily zonal (u) and meridional (y) wind at 200 hPa are retrieved to analyze the role of internal dynamics and eddy-mean flow interaction through the perturbation kinetic energy (PKE), where PKE 5 0. 5(u 0 u 0 1 y 0 y 0 ), and eddy momentum flux (u 0 y 0 ), where the time-mean covariances have been computed from filtered daily data using a 24-h difference filter (e. g. ,  Wallace et al. 1988; Chang and Fu 2002). The wave activity flux at 200 hPa is also computed as in  Karoly et al.
All monthly anomalies are calculated by subtracting the corresponding monthly climatology. To reduce the effect of long-term trends, a third-order polynomial (i. e. , cubic trend) is removed by least squares fit at each grid point for all anomalous fields before performing the multivariate or univariate analyses. We found, however, that linear detrending yields very similar results. The empirical prediction model used in the study is based on linear regression where the predictand is either the winter NAO index or gridpoint anomalies. For the latter, SLP and surface air temperature (SAT) at 2 m from ERA-Int, together with the satellitederived land-ocean Global Precipitation Climatology Project (GPCP), version 2. 2, dataset provided by NASA GSFC's Laboratory for Atmospheres  (Adler et al. 2003) , are considered.
2013), although some efforts are being carried out (e. g. ,  Batté et al. 2014 ). In addition, the mechanism identified here supports the notion that dynamical prediction systems will require a proper representation of the stratosphere  (Folland et al. 2012) , which can be achieved by simulating realistic stratospheric variability and its coupling to the troposphere  (Charlton-Perez et al. 2013 ) and reducing biases in the climatological polar vortex  (Peings et al. 2012). Acknowledgments. The research leading to these results has received funding from the European Union 7th Framework  Programme (FP7 2007 -2013) , under Grant Agreement 308299 (NACLIM -www. naclim. eu). AdlC is supported by the FP7 project EMBRACE (Grant Agreement 282672) and thanks the Spanish project CGL2012-34997. The authors acknowledge the insightful comments from three anonymous reviewers and thank the editor Dr.
The data confirmed warming in the twentieth century  (Büntgen et al. , 2008). For recorded data, the longest meteorological time series in the French Pyrenees began in 1882 at an astronomical observatory located on the Pic du Midi (2862 m a. s. l. ). A mean annual temperature increase of 0. 83 • C was observed over 1882-1970 with a significant decrease in the mean annual diurnal temperature range (2. 89 • C per century), mainly due to a significant increase in the daily minimum temperature  (Bücher and Dessens, 1991; Dessens and Bücher, 1995). Recent work on data homogenization within the framework of the Pyrenean Climate Change Observatory depicts a uniform warming for the massif over the last 60 years, and highlights a significant warming signal from the 1980s onwards  (Soubeyroux et al.
, 2013; Berthier et al. , 2014). We generated a 2 m horizontal resolution DEM with 1 m vertical resolution and 1. 8 m vertical accuracy (GeoView © software 6. 6,  Marti et al. , 2014). In this study, the Pléiades DEM was used to generate the surface elevation of the deglaciated margin between 1983 and 2013. To map the differences in surface elevation on the glacier between 1983 and 2013, the DGPS surface elevation was used because it was acquired closer to the end of the summer.
Climatic data To infer potential drivers for Ossoue Glacier variations, we used several climatic data sets, including three mean monthly air temperature data sets. www. the-cryosphere. net/9/1773/2015/ The Cryosphere, 9, 1773-1795, 2015  -The closest meteorological station to Ossoue Glacier was located at Gavarnie, in the same valley (1380 m elevation, 11 km east of the glacier, time series from January 1991 to May 2012). -To cover the glacier reconstruction period, we also used the temperature time series recorded at the Pic du Midi observatory (42. 93 • N, 0. 14 • E; 2874 m a. s. l. , 30 km northeast of the glacier). The time series was homogenized, and gaps were filled from January 1881 to May 2011  (Bücher and Dessens, 1991; Courraud, 2011). We used raw data from 2011 to October 2013. -We extended the Pic du Midi temperature time series with the CRUTEM 4 data set (5 • × 5 • gridded version) over the period 1858-1890  (Jones et al.
, 2012). The mean annual difference between these data sets due to the elevation difference between the stations was removed from the CRUTEM data set (-13. 07 • C). All temperatures time series correlated well (Spearman's ρ > 0. 96, p values < 0. 05). Temperature time series were averaged over summer periods (JJAS), winter periods (ND-JFMA), and hydrological years (beginning 1 October) We also used two monthly precipitation data sets. -Monthly precipitation was recorded at the Gavarnie Valley station simultaneously with the temperature (see above for station location; period of record: January 1991 to May 2012). -Monthly precipitation was recorded over the period 1882-2013 at Tarbes-Ossun Météo-France station (43. 18 • N 0. 00 • E, 360 m elevation, 50 km northeast of the glacier). These data were homogenized until 2000 and used as raw data since then  (Moisselin et al.
, 2002). Both precipitation data sets showed a significant correlation (Spearman's ρ = 0. 56, p value < 0. 05). We calculated annual and winter precipitations (NDJFMA). October and May precipitation values were not considered because the precip- itation was as often liquid as solid in these months, and thus presumably did not contribute significantly to accumulation on the glacier. We performed correlations between the meteorological data (temperature and precipitation) and Ossoue Glacier mass balance measurements over the 2001-2013 period. To reconcile the different recording periods of the meteorological and glaciological measurements, summer and annual mass balances were linearly interpolated by using a fixed date system (1 October,  Cogley et al. , 2011). The mass balances terms B w were extrapolated to 31 May.