dataesr/scientific-paragraphs-categorization huggingface dataset:
The logarithmic fold changes (CD vs HC) are plotted against negative logarithmic adjusted p values calculated with the R package MSstats and performed from triplicate injections. 23 All targeted proteins were found to be either upregulated or downregulated in CD patients compared with controls, and the results validated all candidates identified in the discovery experiments. CD, Crohn's disease; HC, healthy controls; SRM, selected reaction monitoring. In conclusion, our metaproteomics approach spanning discovery to confirmation demonstrates, for the first time, the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacteria, even from a small number of patients and HCs. We provide an initial list of CD-associated microbial proteins extracted from a typical group of patients, which could represent major common features in CD patients.
Acknowledgements We are grateful to  Bertrand Nicolas  for his help in the preparation of figures. Contributors CJ, DPK, CC, HS, PS, LB, JD: conception and design of the study; CB, AG, SV, CC, SM, PL, FB, FL, WC, VL, NP: acquisition of data; CJ, DPK, CB, AG, SV, CC, SM, FB, WC, VL, NP: analysis and interpretation of data; CJ, DPK, CB, SV, CC: drafting of the manuscript; DPK, AG, CC, HS, PS,  J-FG , SDE, AVD, JD, BV, PM, VM: critical revision of the manuscript for important intellectual content; CJ, DPK,.
Model, Experiments, and Data Sets 2. 1. Coupled Model Components Our regional coupled model NOW (for NEMO-OASIS3-WRF) couples state-of-art atmospheric (WRF) and oceanic (NEMO) models through the OASIS3 coupler  [Valcke, 2013]. The NOW components can be precisely configured by using the multiple options offered with each model (physical parameterizations, numerical schemes, etc. ). The model components and the main options used for this study are briefly described below. The ocean component is based on the NEMO (Nucleus for European Modelling of the Ocean) modeling system. Vertical mixing is modeled with a prognostic turbulent kinetic energy scheme, with background vertical diffusion and viscosity of 10 25 m 2 s 21 and 10 24 m 2 s 21 , respectively  [Blanke and Delecluse, 1993]. Additional subgrid-scale mixing parameterizations include a bi-Laplacian viscosity and an iso-neutral Laplacian diffusivity  [Lengaigne et al.
The OASIS coupler exchanges averaged oceanic and atmospheric parameters asynchronously every hour in order to fully capture high-frequency air-sea interactions. The exchanged surface fields between the two models are the following: SST, wind stress, longwave and shortwave radiations, turbulent heat fluxes, precipitation, and evaporation. The geographical domain of this coupled model extends from 25. 5 E to 142. 25 E and from 34. 5 S to 26 N and covers the entire tropical and subtropical IO (Figure  1 ). The bathymetry and orography as well as the land-sea mask for both models are obtained from a smooth combination of the 2 min U. S. Geological Survey (USGS) topography and the General Bathymetric Chart of the Oceans (GEBCO) data over shelves. Land use categories are from the USGS 24-category data set.
Acknowledgments The model described in this paper is available at  IPSL (Institut Pierre Simon Laplace ) website: http://forge. ipsl. jussieu. fr/now. Guillaume Samson  was supported by the  IS-ENES   FP7-Project  funded by the  European Commission. The authors thank  IFCPAR (Indo French Centre for Promotion of Advanced Research), New Delhi  for funding of the proposal 4907-1,  LEFE (Les Enveloppes Fluides et l'Environnement) program  for funding of the proposal CYCLOCEAN AO2010-538863 and IDRIS for computing facilities. Journal of Advances in Modeling Earth Systems 10. 1002/2014MS000324.
Specifically, SSWs can be classified into polar vortex displacement events or polar vortex splitting events. This paper aims to study the Arctic stratosphere during such events, with a focus on the stratopause using the Modern Era-Restrospective analysis for Research and Applications reanalysis data set. The reanalysis dataset is compared against two independent satellite reconstructions for validation purposes. During vortex displacement events, the stratopause temperature and pressure exhibit a wave-1 structure and are in quadrature whereas during vortex splitting events they exhibit a wave-2 structure. For both types of SSW the temperature anomalies at the stratopause are shown to be generated by ageostrophic vertical motions. Transformed Eulerian mean diagnostics are used to show differences in the planetary wave activity between displacement and splitting events.
Moreover, most of the studies on the stratopause have focused on the last decade because of the lack of satellite data before then. In order to understand the occurrence of the ES events in the last decades and their formation after certain SSW events, a first approach could consist of exploring common features of the stratopause during SSW events a in a large dataset like a meteorological reanalysis. Chandran et al. (2013)  examined the influence of the type (displacement/splitting) of SSW on the formation of an ES and found that 68 % of the ES events followed a splitting event in a 52 years simulation in the World Atmosphere Community Climate Model. It is therefore interesting to compare and contrast the stratopause behaviour during the two types of SSW in order to better understand the mecanism of ES events, especially using reanalysis data which resolve the upper stratosphere and lower mesosphere.
In the same way, it is useful to see if the stratopause structure is a good indicator of the middle-atmospheric circulation during the two types of SSW events, especially as many SSW events start at high altitudes and descend through the stratosphere. The paper is structured as follow. First of all, the data and analysis techniques used in this work are described in Sect. 2. Section 3 presents our results on the stratopause climatology and on the stratopause behaviour during SSWs in the Modern Era-Restrospective analysis for Research and Application (MERRA) data. A general conclusion in Sect. 4 closes the article.
The MERRA dataset In this study, the MERRA reanalysis dataset was chosen because it extends to 0. 02 hPa, this includes the stratosphere and much of the mesosphere. The MERRA reanalysis uses the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) model  (Rienecker et al. 2011). GEOS-5 uses two GW parametrizations: drag from orographic GWs based on  McFarlane (1980) , and drag from non-orographic GWs based on  Garcia and Boville (1994). In this study we make use of the daily-mean and monthly-mean data from the 1st January 1979 to the 31st December 2012. All data are available on 42 standard pressure levels (from 1000 to 0. 1 hPa). The horizontal resolution of the model is 1. 25 • latitude by 1. 25 • longitude. How- ever, additional models levels were also used to extend the analysis to 0.
02 hPa. The consistency of the additional levels with the interpolated levels of the final MERRA product has been found to be good by comparing time series and the climatologies of overlapping levels around 0. 1 hPa. The MERRA data represent well the polar stratospheric circulation  (Rienecker et al. 2011 ) and capture most features of the upper stratosphere-lower mesosphere as well as the time evolution of the undisturbed polar stratopause. However,  Manney et al. (2008)  show that different structures of the upper stratosphere and mesosphere are not well captured in analyses performed using systems that assimilate only nadir-sounding radiance observations (like MERRA reanalyses). They particularly note that models with a lid heights below 0. 01 hPa and with a coarse GW scheme may not represent the mesospheric circulation well under extreme events like SSWs.
Satellite data To validate the MERRA representation of the stratopause, the satellite MLS high-resolution data and the High Resolution Dynamic Limb Sounder (HIRDLS) data have been used. MLS is positioned at a 705 km sun-synchronous orbit and measures thermal microwave emissions from the Earth's limb up to a latitude of 80 •  (Waters 2006). The vertical resolution of the temperature measurements is 5. 5 km at 3 hPa and 8 km at 0. 01 hPa The temperature precision for the v3. 3 version (used in this study) is 1 K with a height-dependant bias between -2. 5 and 1 K  (Livesey et al. 2011). HIRDLS has a better vertical resolution than MLS (1 km), a latitude coverage up to a latitude of 80 • and the temperature precision at the stratopause is 1 K  (Gille et al. 2003). The instrument was damaged during the takeoff and has been out of order since March 2008.
Comparisons with satellite data In this section the representation of the stratopause during sudden stratospheric warmings is assessed in the MERRA dataset by comparison with satellite observations. Figure  4  shows the daily northern polar cap temperature (averaged over 70-88°N) from the MERRA (bottom), MLS (middle) and HIRDLS (top) datasets for the period 2004-2012. All three data sets show that, during an SSW event, a warm temperature anomaly (red) quickly propagates downward from the upper winter stratosphere to the mid-low stratosphere. Subsequently the polar stratosphere becomes nearly isothermal and the normal winter stratospheric state reappears a few days later. The stratopause (green line) follows well the quick downward propagation of warm anomalies and abruptly drops during the events.
Then it soars either to an altitude higher than before the event (after the 2006 warming for instance) or to around its normal winter altitude. Mesospheric coolings which matches the stratospheric warmings can be seen with thin dark-blue strips at mesospheric altitudes during the SSWs. No obvious conclusion about possible differences of the polar cap stratopause behaviour between displacement and splitting events can be drawn with this plot. The fact that one ES event is preceded by a splitting event (2009) and one is preceded by a displacement event shows that the occurrence of an ES is probably not dependant on the type of SSW event. This agrees with the results of  Chandran et al. (2013). Comparing the three datasets, one can see that the polar cap stratopause is similar in the three datasets in summer and in relatively undisturbed winters  (e.
g. 2004/2005, 2006/2007 and 2007/2008 winters). However, when the middle atmosphere is affected by strong SSW events e. g. in  2005/2006, 2008/2009, 2009/2010 and 2011/2012 , the stratopause drop just after the onset date is under-estimated in the MERRA dataset. In particular, the ES formation after the strong 2006 vortex displacement and the strong 2009 vortex splitting is not represented in the MERRA data as observed in  Manney et al. (2008). Figure  5  explores these issues further by showing the difference between polar cap temperature for the MERRA and MLS data for 30 day periods either side of the 5 SSW events shown in the previous figure. There is an underestimation of the downward propagation of the upper stratospheric warming in the MERRA data set, where the disturbed mid-stratosphere is colder by ∼ 15 K before the SSW than MLS.
In the mesosphere the MERRA analysis is too warm, underestimating the mesospheric cooling (by about 20 K) in the MERRA data before the SSWs. Both these differences, and the subsequent underestimation of the stratopause descent are probably due to an imperfect parametrization of GWs [probably insufficient consideration of small scales GWs  (Garcia and Boville 1994) ] in the middle atmosphere in GEOS-5, the model used for the data assimilations for the MERRA dataset. Two principle explanations for these differences and the poor representation of the ES can be found. Given that the GEOS-5 model has an upper limit at 0. 02 hPa ( ∼ 75 km), it can clearly not reproduce a stratopause above this. Moreover, the ES events have been associated with non-orographic GW drag  (Wright et al. 2010; Chandran et al.
2011; Limpasuvan et al. 2012; Tomikawa et al. 2012; Yamashita et al. 2013 ) so poor parametrization of GWs in GEOS-5 at high altitudes could be another reason for the absence of ES events and for the errors in the middle atmosphere representation after the warmings in MERRA data  (Manney et al. 2008). Other factors that may play a significant role include radiation calculations and bias correction in data assimilation during the creation of reanalysis data  (Manney et al. 2008). This is especially true near the stratopause area where very little data are used in the assimilation. Nevertheless, even if the MERRA dataset cannot fully reproduce the extreme stratopause behaviour, it can help to understand several of its features during SSW events.
The zonal mean stratopause during vortex splitting and displacement events In order to identify anomalies during SSW events, a daily climatology of temperature, zonal wind, geopotential height, E-P flux diagnostics, stratopause pressure and stratopause temperature has been made using all the available data between 1979 and 2012 (Figs. 6 and 7 for displacement and splitting events, respectively). Significance is assessed using a Monte-Carlo algorithm with 10,000 resamples of winter days over the last 34 years that are not affected by a SSW. Figures  6  and 7  show the anomalies at the onset dates of the SSW (plus/minus one day; denoted -1_1) and 3 day averages up to 21 days before and after the SSW, so that the evolution can be observed. A clear dipole of temperature anomalies (Figs. 6a  and 7a ) appears over the high latitudes in both types of warming: a positive anomaly in the high latitude stratosphere and a negative in the high latitude mesosphere.
Conclusion The MERRA reanalysis data have been used to calculate a 34-year climatology of the stratopause with a specific focus on 20 vortex displacement events and 10 vortex splitting events. A comparison between MLS, HIRDLS and MERRA data has been performed to evaluate the quality of the stratopause identification in the MERRA dataset. Although the three datasets agree well for the undisturbed stratopause, when the stratopause becomes strongly disturbed, several differences appear. An underestimation of the drop during strong SSWs and the absence of the 2006 and 2009 ES events are noticeable in the MERRA data comparing with the satellite data. It is likely due to a too low cap limit or to a poor parametrization of GWs in the GEOS-5 model. Moreover, to look at MLS data has also shown that ES events can occur after a displacement event (in 2006) and after a splitting event (2009) in agreement with  Chandran et al.
Acknowledgments The authors thank the two anonymous reviewers for their insightful comments. Thanks also to  Lesley Gray  for her constructive comments and helpful discussions,  Corwin Wright  for providing satellite data and  Michael Bosilovich  for help in acquiring the non-standard levels for the MERRA data. Thanks to the Global Modeling and Assimilation Office (GMAO) and the  GES DISC  for the dissemination of MERRA. DMM is funded by a  NERC  grant. Finally EV thanks the department of Geosciences of the Ecole Normale Superieure (Paris)  for having given to him the opportunity to carry out such a research project. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Patient tumor samples HNSCC samples were obtained from patients included in the CARISSA multicenter blinded institutional review board-approved phase II trial of post-operative irradiation with cisplatin ± gefitinib (GORTEC 2004-02 -NCT00169221). Clinicopathological data have been reported  [54]. Inclusion criteria required tumor samples to contain at least 50% tumor cells. For Western analyses, tumor fragments (mean=190mg) were frozen in liquid nitrogen within 15 minutes after surgery and subsequently processed for quantitative Western blotting as described  [54]. The remaining mirrored tumor fragments were used for histological control and fixed in formalin for immunohistochemical analyses.
Immunofluorescence and microscopy For immunocytochemistry, cells plated on glass coverslips were fixed in a solution of 3% paraformaldehyde containing 3% sucrose. Cells were permeabilized with 0. 2% Triton X-100. After staining, the coverslips were mounted in ProLong® Gold antifade reagent (Invitrogen). Fluorescence was observed through 40X(1. 3NA), or 100X(1. 3NA) oil objectives on a Zeiss inverted microscope (Axiovert 200M) equipped with a CoolSnap HQ cooled charge-coupled-device camera (Roper Scientifique, Evry, France). Phase contrast and www. impactjournals. com/oncotarget video microscopy were performed using a 10X(0. 25NA) air objective. Image acquisition was performed using the MetaMorph Imaging System (Universal Imaging Corp. , Downingtown, PA). Cell migration was followed by manual tracking of non-dividing cells and analyzed using MatLab (The MathWorks), as described  [57].
ACKNOWLEDGEMENTS The authors thank the  GORTEC (Groupe d'Oncologie Radiotherapie des tumeurs de la Tete Et du Cou ), the  GETTEC (Groupe d'Etudes des Tumeurs de la Tete Et du Cou) , the physicians/pathologists and patients who participated in the Carissa clinical study. We thank  Jim Norman  for kindly providing the human telomeraseimmortalized fibroblasts,  Nathalie Ebran-Bendahhou  for providing the orthotopic xenografted tumors as well as  Marjolijn Mertz  and  Samah Rekima  for help with image acquisitions. Financial support for this work was provided by the  French National Institute of Cancer (INCa ,  ESCAPE /PAIR-VADS ), the  Ligue Nationale Contre le Cancer  (post-doctoral fellowship to L. V. ) the  Canceropole PACA  and the  French Government LABEX SIGNALIFE program  ( ANR-11-LABX-0028-01 ).
Kolumbo and Santorini To perform simulations of the tsunami generated by Kolumbo explosion registered at Santorini, we use topography and bathymetry data prepared in  Nomikou et al. (2013b)  (cf. Fig. 2 ). The swath bathymetry was obtained from several oceanographic surveys. The first data were collected in 2001, and further refined in 2006. The resulting computational grid has a spatial resolution of 50 m. Time step size is chosen to be t = 0. 15 s that satisfies the CFL criterion (cf. Eq. 6). Artificial tide gauges were placed offshore near areas of particular vulnerability in case of tsunami (harbours, touristic resorts, coastal towns).
Larger explosion powers might impact harbours inside the caldera with 1-2 m high waves. Figure  10  shows the maximum depth of the incoming water waves along the original coastline on the main Santorini island for several explosion powers. Maximum wave amplitude A max at four main coastal towns as a function of explosion energy is represented on Fig. 11. Highest amplitudes are recorded in Pori that lies closest to the Kolumbo volcano. by around 1 % to all tsunami cases registered on Earth (cf. Sect. 1) they are often neglected when estimating the potential tsunami hazard although they might be particularly deadly at short distances from the volcano. To test the model, we first investigate the 1996 tsunami generated in Karymskoye lake, Kamchatka. Shortly after the event, run-ups around the lake were measured that provides a unique data set to calibrate the numerical model  (Belousov et al.
Governing equations in COMCOT τ x ρ = gn 2 m H 7/3 P P 2 + Q 2 , (A4) τ y ρ = gn 2 m H 7/3 Q P 2 + Q 2 , ( A5 ) where n m is the Manning's roughness coefficient. For the simplicity, only equations in the Cartesian coordinate system are given here. However, COMCOT package also solves for the governing equations in the spherical geometry with Coriolis force due to rotation of the Earth included. Cartesian coordinate system was used in all tsunami calculations in Karymskoye lake, whereas spherical coordinates were employed in the calculations of tsunamis generated by Kolumbo underwater explosion.
Acknowledgements. M. Ulvrova is grateful to  Xiaoming Wang  for his help with COMCOT. We thank  E. Pelinovsky ,  D. Papanikolaou ,  K. I. Konstantinou  and an anonymous referee for constructive comments and reviews and editor  A. Costa  for handling the manuscript. We also thank participants of the field trip to Santorini island in May 2013. The support for this research has been provided by  FP7-ENV-2013 program ASTARTE  and the  Laboratory of Excellence ClerVolc. This is Laboratory of Excellence ClerVolc contribution no. 86. Multibeam data for the region around Santorini were obtained aboard R/V AEGAEO of HCMR, during 2001 (GEOWARN,  IST-1999-12310 ) and  2006 (THERA 2006 ,  OCE-0452478 ). Matina Alexandri  and  Dionissis Ballas  are gratefully acknowledged for their important and effective contribution during the cruises.
Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA.
Introduction Many branches of science are needing to tackle problems associated with the increasing scale of datasets. A common approach for identifying the important information within large amounts of data is to identify recurring patterns, achieving intelligent data reduction. A generic approach for thisone that is often used to reduce data to its dominant constituentsis principal component analysis (PCA). However, even such a simple approach is computationally challenging for very large datasets. Here we are specifically interested in the use of resting-state functional magnetic resonance imaging (rfMRI), a powerful and popular approach for studying functional brain networks. Analysis of a multisubject rfMRI imaging study often begins at the group level, for example, estimating group-averaged functional connectivity across all subjects in a resting-state fMRI study.
For very large datasets, this can become problematic, as the computational expense and/or memory requirements for many analysis methods increase as the number of subjects increases, and so can quickly become impractical. Several important analysis approaches can be applied in a computationally practical way if the dataset can first be reduced by a grouplevel PCA. For example, one of the most widely used methods for analysing such data is independent component analysis (ICA), which identifies multiple distinct networks simultaneously from a given dataset. ICA is often applied with relatively low dimensionality (the number of independent components), resulting in relatively extended networks being estimated. ICA can also be used to obtain a detailed data-driven functional parcellation, when applied with relatively high dimensionality, e.