dataesr/scientific-paragraphs-categorization huggingface dataset:
We identified a set of genes directly regulated by the pVHL/HIF-2α pathway, and observed a distinct and graded ability of the different VHL mutants to regulate these genes: the more important the pVHL loss of function is, the more dysregulated the HIF-2α target genes are. To validate our results in naturally occurring tumors, we studied the expression profiles of the 30 genes identified above in massive human expression compendium that contains thousands of public expression datasets from various cancer types (SEEK). A large amount of kidney cancers datasets was significantly selected. The datasets from Beroukhim and collaborators  (25)  with known VHL status, were carefully analyzed, and 17 genes were highly overexpressed in ccRCC that have lost VHL. These genes have a role in oncogenesis (Supplementary Table  S7 ), and some of them are overexpressed in tumors and associated with poor prognosis (STC2, ADM in RCC  (31) (32) (33) ; PFKFB3, NDRG1 in other tumors (see references in Supplementary Table  S7 ).
Acknowledgments This work was supported by grants from the  Ligue Nationale contre le Cancer (Comités du Cher et de l'Indre, de la Loire Atlantique et des Côtes d'Armor) , the  French NCI (INCa, PNES "Kidney cancer") , the  "Association pour la Recherche sur le Cancer" (ARC) , the " Association VHL France " as well as by the  HypoxiaNet COST Action   TD0901. S. C. was supported by postdoctoral fellowships from the  Fondation "Cancer, Aidez la recherche!" and the "Fondation Gustave Roussy ". We are grateful to  G. Lenoir  and  E. Solary  for their constant support. The authors thank  S. Hermouet ,  F. Sigaux ,  C. Brahimi  for fruitful discussions and  J. Bombled ,  V. Delauzun ,  G. Allègre ,  D. Caiola  and  R. Carcenac  for technical assistance. We also thank  D. Venne ,  H. Labasse ,  J.
This paper describes the NO y plumes originating from lightning emissions based on 4 yr (2001Á2005) of MOZAIC measurements in the upper troposphere of the northern mid-latitudes, together with ground-and space-based observations of lightning flashes and clouds. This analysis is primarily for the North Atlantic region where the MOZAIC flights are the most frequent and for which the measurements are well representative in space and time. The study investigates the influence of lightning NO x (LNO x ) emissions on large-scale (300Á2000 km) plumes (LSPs) of NO y. One hundred and twenty seven LSPs (6% of the total MOZAIC NO y dataset) have been attributed to LNO x emissions. Most of these LSPs were recorded over North America and the Atlantic mainly in spring and summer during the maximum lightning activity occurrence.
However, using CO as a tracer of anthropogenic emissions could help to differentiate between lightning and anthropogenic emissions, as both emission sources are high over North America  (Olivier and Berdowski, 2001). The INTEX-NA project provided observations of nitrogen species (NO x , HNO 3 and PAN) influenced by lightning during the summer 2004 (1 JulyÁ15 August) over the Eastern United States and the Atlantic. Measurements were performed by one aircraft (with 18 science flights) downwind of convective events in order to characterise chemistry of some plumes over the Atlantic. Brunner et al. (1998)  observed large-scale plumes (LSP) of pollution in the UT extending over hundreds of kilometres over the Eastern part of North America in the data from the NOXAR experiment (Nitrogen OXides and ozone along Air Routes).
Thomas et al. (2014)  discuss the NO y measurements made in MOZAIC, showing that lightning emissions have a potentially large influence on the seasonal and regional distribution of NO y in the UT. We use the same MOZAIC measurements together with groundbased and space-borne observations of lightning activity and clouds to provide the detailed description of lightningrelated LSPs. Section 3 explains the methodology used to extract the LSPs from the entire data base. In particular, Lagrangian simulations with the FLEXPART model are used to identify the convective origin of the plumes. Section 4 gives the overall picture with a special focus on the LSPs observed over the North Atlantic flight corridor, downwind of thunderstorm systems over North America.
MOZAIC in-situ data The primary data used in this study are from in-situ observations of NO y , O 3 , and CO made with autonomous instruments deployed aboard commercial long-haul aircraft in the MOZAIC programme  (Marenco et al. , 1998). Most of the measurements are recorded in the UT/LS, between 9 and 12 km altitude. In MOZAIC, regular measurements of O 3 and RH (Relative Humidity) have been performed since 1994, using a UV-absorption monitor for O 3  (Thouret et al. , 1998)  and a Vaisala Humicap sensor for RH  (Helten et al. , 1998). In 2001, a NO y instrument was installed on one of the MOZAIC aircraft using a chemiluminescence detector combined with a catalytic converter for conversion of the different NO y compounds to NO  (Volz-Thomas et al. , 2005). CO has been measured regularly on all MOZAIC aircrafts since December 2001, using a modified infrared filter correlation monitor  (Ne´de´lec et al.
, 2003). For the integration interval of 4s applied in MOZAIC, the accuracy of the measurements has been estimated as follows: O 3 :92 ppbv 92%  (Thouret et al. , 1998) ; RH:96%  (Helten et al. , 1998; Smit et al. , 2008) ; CO (30s response time):95 ppbv95%  (Ne´de´lec et al. , 2003) ; and NO y :950 pptv95% (Volz-  Thomas et al. , 2005; Pa¨tz et al. , 2006). Between December 2001 and May 2005, 1685 MOZAIC flights are available with combined measurements of NO y and the other trace gases. It must be noted that the MOZAIC data do not provide direct information on NO x.
Satellite cloud imagery Observations of clouds were obtained from infrared (IR) images provided by geostationary satellites, mainly the Geostationary Operational Environmental Satellite (GOES) East satellite (channel 4). The data were provided at hourly resolutions by the SATMOS service (http://www. satmos. meteo. fr/). These data give a good overview of synoptic weather systems in order to localise deep convection potentially leading to lightning activity. Geostationary satellite IR imagery also offers the possibility to track short-and long-lifetime convective systems and, consequently, to identify the spatial and temporal occurrence of those convective clouds from which the trace gases measured along the flight tracks originate. According to the regions of interest, only the data over North Atlantic and North America were used in this study.
Identification of tropospheric air masses A twofold approach is used to distinguish air masses of stratospheric-origin from those of tropospheric-origin in the MOZAIC records: (1) All data for which the potential vorticity (PV) computed from ECMWF analysis and interpolated over the MOZAIC observations is larger than 2 pvu are discarded. This criterion is commonly used to distinguish between tropospheric and stratospheric air  (Hoskins et al. , 1985). (  2 ) Since PV at the time of the observation may not be sufficiently restrictive to discard stratospheric-origin air masses, we also discard all air masses for which the back trajectories have been associated with PV values larger than 2 pvu during the last 3 d before the observation. For each MOZAIC observation in the entire MOZAIC NO y database (every 0.
18 in latitude or longitude) five clustered particle positions such as latitude, longitude, pressure, PV and the fraction of the total number of particles for each cluster, are used to exclude stratospheric air masses and those influenced by recent intrusions from the stratosphere when at least three clusters show stratospheric origin. Figure  2  illustrates how the methodology removes the stratospheric air masses from the MOZAIC dataset based on O 3 Á CO scatter plots (colour-coded with NO y ). The left panel (a) displaying non-filtered data shows the stratospheric reservoir, i. e. the vertical branch with high O 3 and NO y and low CO, the tropospheric reservoir (horizontal branch with high CO, variable O 3 and NO y ) and the transition between the two branches  (Hoor et al. , 2002; Brioude et al.
Identification of lightning-related plumes Starting from the tropospheric dataset (described in Section 3. 2), plumes in the UT (pressure lower than 300 hPa) are extracted based on the seasonal and regional frequency distributions of NO y and on the coincident CO mixing ratios. 3. 3. 1. Seasonal NO y and CO distributions in the upper troposphere. Tables  1  and 2  summarise the frequency distributions of NO y and CO respectively in the UT (p B300 hPa) in terms of mean, median and Q3 for each season and for three different geographical regions (North America, Atlantic, and Europe). NO y plumes are defined as to be part of the upper 25% of the seasonal distribution (higher than the 75% percentile Q3; Table  1 ). This selection describes the air masses with distinct NO y anomalies respect to the median (Q2).
As a specification in terms of statistical analysis, the Q3 splits off the highest 25% of data from the lowest 75% and the median or Q2 parameter cuts dataset in half. The median is generally used to assess atmospheric baseline concentrations for atmospheric observation datasets, that is an atmosphere free of recent pollution or of very clean influence and characteristic of well-mixed air masses of different origins (Hemispheric Transport of Air Pollution HTAP report, 2010: http://www. htap. org/). Therefore, this selection allows focusing on NO y enhancements that are related to quite recent emissions and avoids ambiguous cases of aged plumes that may have already undergone extensive mixing with potentially higher influence of multiple sources than recent air masses. As detailed in  Thomas et al.
Characterisation of LSPs and validation of the lightning-NO x influence 3. 4. 1. Further characterisation of LSPs. Using the criteria defined above, 1395 NO y enhanced plumes were found in the MOZAIC database. Figure  3  displays the frequency distribution of the horizontal extent of the plumes for the three geographical regions: North America (NAM, 308NÁ608N/1208WÁ608W), Atlantic (ATL, 08NÁ608N/ 608WÁ258W) and Europe (EU, 358NÁ708N/258WÁ408E), in summer (JJA). In general, plumes have a horizontal extent of less than 300 km (77, 88 and 83% for the measurements made respectively over NAM, ATL and EU). Although the horizontal extent of the plumes is difficult to assess from 1D data trajectories such as MOZAIC flights, Fig. 4  shows that plumes wider than 300 km on average have NO y mixing ratios about 0.
Cumulative distributions of BT were computed at each FLEXPART grid point to derive the percentile for BT below 220 K (referring to Fig. 5b ). NLDN observations were further used to quantify the CG lightning activity temporally and spatially coincident with the FLEXPART simulations. The NLDN data are integrated over 91. 5 hours around the FLEX-PART calculation output at each 3-hour time-step. Finally, the number of flashes is integrated for each LSP and over each season (referring to Fig. 5c ). Note that the NLDN observations do not correspond strictly to GOES-IR BTs B220 K. There is a good agreement between FLEXPART backward plumes coming from the LT & UT (!1 )10 L(3 %) and the occurrence of high convective clouds observed by GOES and CG lightning flashes observed by NLDN. Figure  5  displays also a clear seasonal variability for the three data fields.
Seasonal and regional distributions of the LSPs When applying the criteria described in Section 3 to the MOZAIC database, 127 plumes (6% of the total MOZAIC NO y dataset and less than 10% of the 1395 lightning-related plumes) are identified as LSPs (Table  3 ). As summarised in Fig. 6 , most of the plumes are observed in SON (43 plumes, observed over 35 flights), JJA (41 plumes, observed over 32 flights) and MAM (38 observed over 21 flights), whereas only five plumes are observed in DJF (over five flights). Just over half (51%) of the LSPs detected along the MOZAIC routes are found over the East coast of the North American continent and over the North Atlantic flight corridor; the remaining LSPs are detected over Europe (41%) and over Northern Africa and Asia (8%). Figure  7  gives the backward trajectories of LSPs for each season from FLEXPART calculations.
Conclusions and perspectives This study further illustrates the importance of lightning NO x emissions focusing on LSPs of enhanced NO y advected in the UT of the northern middle latitudes. The analysis of 1685 MOZAIC flights (over the period 2001Á2005) of NO y , O 3 , and CO was conducted together with a characterisation of the convection with the use of ground-based observations of lightning activity and spaceborne observations of cloud. A methodology to extract lightning-related LSPs from the MOZAIC database is described. This consists in discarding aircraft exhaust emissions, stratospheric intrusions and pollution from the surface to keep only tropospheric air masses potentially influenced by lightning NO x. This latter source linking convection and lightning activity is then identified using FLEXPART simulations, GOES images and NLDN data.
As a result, 1395 enhanced NO y plumes (anomaly above the Q3) attributed to LNO x emissions were identified. Furthermore, a plume horizontal extent threshold of 300 km was used in order to select the exceptional events. This leads to 127 LSPs, almost 6% of the total MOZAIC dataset, which is significant enough to require a detailed analysis. Larger plumes induce higher NO y anomalies (on average 0. 5 ppbv higher than the plumes B300 km) and thus have a stronger potential impact on the chemistry downwind. The methodology allows focusing on the LSPs related to lightning emissions only. Although the relatively stringent selection retains a small fraction of the total plumes observed, it largely reduces any remaining ambiguity. The majority of these plumes are observed in summer (41), autumn (43) and spring (38), over the Atlantic (74% with 2,5 ppbv of NO y on average), Europe (18% with 1,5 ppbv of NO y on average), and over Africa (8% with 1,5 ppbv of NO y on average).
This distribution matches the global seasonal variations of NO y in the UT  (Thomas et al. , 2014). Only five LSPs (not detailed here because not statistically significant) are detected in winter, which is the season with the minimum lightning activity in the northern hemisphere. The main findings of this study based on the entire MOZAIC data base are the following: (1) 74% of the LSPs are observed downwind of the lightning activity occurring over North America, i. e. the transatlantic LSPs, leading to a negative (positive) west to east zonal gradient of NO y (O 3 ) of (0. 4 ('18) ppbv in spring, (0. 6 ('14) ppbv in summer and (0. 3 ('7) ppbv in autumn. Higher NO y values are measured close to emissions and lower values are measured eastward after dilution. As NO y is chemically conservative in the UT after injection through convection, the zonal gradient can be explained by mixing of the plumes with background air.
Finally, the present study demonstrates the strength of MOZAIC to provide unique sampling of the atmosphere in data sparse regions. The successor programme IAGOS (http://www. iagos. org), unlike the previous MOZAIC programme, allows the NO x measurement, which is already working on board one aircraft. The NO x measurement will offer the possibility to document more LSPs and quantify more precisely their dependence to the properties of the lightning activity which will be derived from the up-coming geostationary MTG (Meteosat Third Generation) and GOES-R (Geostationary Operational Environmental Satellites) lightning detection missions.
Acknowledgements This work has been funded by INSU-CNRS and DFG in the frame of the bilateral project Integrated analysis of long-range pollution transport to mid-and high-latitudes over Europe using model simulations, satellite observations, and aircraft measurements (INTAS). The authors acknowledge the strong support of the European Commission for the support to the MOZAIC project (1994Á2003) and the preparatory phase of IAGOS (2005Á2012), Airbus, and the Airlines (Lufthansa, Air-France, Austrian, Air Namibia, Cathay Pacific and China Airlines so far) for carrying the MOZAIC or IAGOS equipment and for performing the maintenance since 1994. MOZAIC is presently funded by INSU-CNRS (France), Me´te´o-France, CNES, UniversiteṔ aul Sabatier (Toulouse, France) and Research Center Ju¨lich (FZJ, Ju¨lich, Germany).
The MOZAIC-IAGOS data are available via http://www. pole-ether. fr thanks to the support ETHER, French data centre service. Geostationary satellite data were provided by Me´te´o France SATMOS data centre. TRMM Microwave Imager (TMI) data were downloaded from the TRMM dataserver. LIS datafiles were retrieved from the NASA LIS dataserver. SSMI observations were downloaded from the GHRC data centre. We are grateful to Dr. Carlo Morale`s (Universidade de Sao Paulo) and Dr. Alec Bennett (UK Met Office) for providing ZEUS and ATDnet lightning data during the early stage of the study. We also acknowledge Nick Demetriades for his help with the NLDN data and Jeremy Vouzelaud for his contribution in the early stage of the study. Finally, we would like to thank the Anonymous Reviewers for their careful reading of the manuscript and their constructive suggestions.
This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multi-task learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection.
MTL has been proven efficient for motor imagery in  [1]  where several classifiers were learned simultaneously from several BCI subject datasets. Our second contribution is thus focused on the problem of performing sensor selection and learning robust classifiers through the use of an MTL mixed-norm regularization framework. We propose a novel regularizer promoting sensor selection and similarity between classifiers. By doing so, our goal is then to yield sensor selection and robust classifiers that are able to overcome the data scarcity problem by sharing information between the different classifiers to be learned. The paper is organized as follows. The first part of the paper presents the discriminative framework and the different regularization terms we have considered for channel selection and multi-task learning.
Experimental Data We have first evaluated the proposed approaches on a simple simulated P300 dataset generated as follows. A P300 wave is extracted using the grand average of a single subject data from the EPFL dataset described in the following. We generate 11000 simulated examples with 8 discriminative channels containing the P300 out of  16   The first real P300 dataset we used is the EPFL dataset, based on eight subjects performing P300 related tasks  [20]. The subjects were asked to focus on one of the 3×2=6 images on the screen while the one of the images is flashed at random. The EEG signals were acquired from 32 channels, sampled at 1024 Hz and 4 recording sessions per subject have been realized. Signals are pre-processed exactly according to the steps described in  [20]  : a  [1, 8]  Hz bandpass Butterworth filter of order 3 is applied to all signals followed by a downsampling.
Hence, for each trial (training example), we have 8 time-sample features per channel corresponding to a 1000 ms time-window after stimulus, which leads to 256 features for all channels (32×8=256 features). On the overall, the training set of a given subject is composed of about 3000 trials. Another P300 dataset, recorded by the Neuroimaging Laboratory of Universidad Autónoma Metropolitana (UAM, Mexico)  [24] , has also been utilized. The data have been obtained from 30 subjects performing P300 spelling tasks on a 6×6 virtual keyboard. Signals are recorded over 10 channels leading thus to a very challenging dataset for sensor selection, as there are just few sensors left to select. For this dataset, we only use the first 3 sessions in order to have the same number of trials for all subjects (≈4000 samples).
The EEG signals have been pre-processed according to the following steps : a  [2, 20]  Hz Chebychef bandpass filter of order 5 is first applied followed by a decimation, resulting in a post-stimulus time-window of 31 samples per channels. Hence, each trial is composed of 310 (10×31) features. We have also studied the effectiveness of our methods on an Error Related Potential (ErrP) dataset that has been recorded in the GIPSA Lab. The subjects were asked to memorize the position of 2 to 9 digits and to remind the position of one of these digits, operation has been repeated 72 times for each subject. The signal following the visualization of the result (correct/error on the memorized position) was recorded from 31 electrodes and sampled at 512 Hz. Similarly to Jrad et al. [21] , a  [1, 10]  Hz Butterworth filter of order 4 and a downsampling has been applied to all channel signals.
Using an 1 regularizer yields a classifier which embeds feature selection, denoted as SVM-1 in the sequel. Three mixednorm regularizers inducing sensor selection have also been considered : an 1 -2 denoted as GSVM-2, and 1q referred as GSVM-q, with q being selected in the set {1, 1. 2,. , 1. 8, 2}) by a nested cross-validation stage, and adaptive 1q norm, with q = 2 denoted as GSVM-a. For the multi-task learning setting, two MTL methods were compared to two baseline approaches which use all features, namely a method that treats each tasks separately by learning one SVM per task (SVM), and a method denoted as SVM-Full, which on the contrary learns an unique SVM from all subject datasets. The two MTL methods are respectively a MTL as described in Equation (  9 ), denoted as MGSVM-2s and the same MTL but without similarity-promoting regularization term, which actually means that we set λ s = 0,   1 : Performance results on the simulated dataset : the average performance in AUC (in %), the average percent of selected sensors (Sel) and the F-measure of the selected channels (in %).
Best results for each performance measure are in bold. The p-value refers to the one of a Wilcoxon signrank test with SVM as a baseline. indicated as MGSVM-2. For these approaches, performances are evaluated as the average AUC of the decision functions over all the subjects. The experimental setup is described in the following. For each subject, the dataset is randomly split into a training set of n = 1000 trials and a test set containing the rest of the trials. The regularization parameter λ has been selected from a log-spaced grid ([10 -3 , 10 1 ]) according to a nested 3-fold cross-validation step on the training set. When necessary, the selection of q is also included in this CV procedure. Finally, the selected value of λ is used to learn a classifier on the training examples and performances are evaluated on the independent test set.