dataesr/scientific-paragraphs-categorization huggingface dataset:
As can be seen from FIG. 3 , under these conditions, the radial profiles of density fluctuation amplitude show the well-known increase with radius up to the E r shear region. Concerning the wavenumber dependence, a pronounced increase of large scale (i. e. k ⊥ in the range of 4-8 cm -1 ) turbulence is detected when ECRH is used to increase R/L Te. In the higher k ⊥ -ranges accessible to the experiment (8-13 cm -1 and 13-18 cm -1 ), such an increase is not observed. It can also be seen that the absolute level is lower at higher wavenumber, consistent with the usual decay of spectral power as function of k ⊥. Comparing these findings to local nonlinear flux-matched turbulence simulations using the GENE code  [29] , it is found for all points shown in Fig. 3  that the dominant unstable mode propagates in ion-diamagnetic direction, which corresponds for these scales to the ITG instability in the GENE code.
Future strategies will include systematic study of the already implemented feedback of ECCD power and use of a FADIS inline-ECE system for detecting the resonant surface as well as use of equilibrium information from online prediction of the discharge evolution using the RAPTOR code  [35]. The present control can also target more than one surface, and an example of a discharge where the DCS automatically changes the target is shown in FIG. 5. Here, an initial (3,2) NTM is fully suppressed by targeting the q=1. 5 surface (black crosses in Fig. 5 b ), from ECE/Mirnov analysis, ρ (m,n) from equilibrium reconstruction shown for information only). Later in the discharge, a (2,1) NTM occurs and DCS recognizes from the mode amplitudes shown in FIG. 5 c ) that this is the dominant mode, changes the target to the q=2 surface, and suppresses the mode there.
Consistent Exhaust Scenarios for DEMO As shown in Section 6, stationary exhaust at steady-state exhaust compatible target load has been demonstrated with N 2 -seeding and strong D-puff leading to a partially detached divertor up to P sep /R = 10 MW/m, corresponding to 2/3 of the value required for ITER and DEMO. It was mentioned in Section 1 that for DEMO, a higher core radiated fraction is needed if the ITER divertor solution is to be adopted. Studies of this scenario have been performed in ASDEX Upgrade using simultaneous feedback controlled injection of N 2 to cool the divertor and Ar or Kr to increase the core radiation. Fig. 8  shows the operational space of ASDEX Upgrade employing up to 26 MW of heating power  [42]. It can be seen that DEMO relevant core radiation fractions have been obtained, albeit at relatively low P sep /R while on the other hand, the data points approaching the ITER and DEMO relevant P sep /R are at the lowest core radiation fraction.
For the Irrig. Water Demand (Eq. 3), annual values were processed from aggregated monthly data of crop water demand for the individual crop groups within each cell, after reducing by available green water for each particular month. The share of each crop group within the crop group area is accounted for (% of Area, Eq. 3). Since for each crop group, up to two specific crops can be grown in rotation on the same area but never concurrently (Supplement), the number of crops (n, Eq. 3) in this case refers to the number of crop groups, rather than specific crops. Similarly, the Crop Water Demand refers to the sum of the crop water demand of the actually grown crops in the crop group.
Acknowledgements. Rens Van Beek ,  Geosciences -Utrecht University , kindly provided data from the  PCR-GLOBWB model. Agnès Ducharne, Laboratoire METIS -Université Pierre et Marie Curie -Paris , provided critical view, specifically on Crop Water Demand. The work has been partially funded by the  French Ministry of Agriculture  and by  USAID (Enhanced Regional Food Security through Increased Agricultural Productivity  to  Sustainably Reduce Hunger  project of the  USAID Southern Africa Feed The Future (FTF)  programme ( 2012-2013) ). It was also supported by  CGIAR Strategic Research Program on Water, Land and Ecosystems (WLE). Edited by:  M. Vanclooster.
(B) Receiver operating characteristic (ROC) curves and precision-recall curves for the ADTree model, separated for inclusion (blue) and skipping exons (red). We indicate the average area under the ROC curve (0. 735), precision (0. 687) and recall (0. 686) for both classes from the 10-fold cross-validation of the model (Additional file 1: Table  S7 ). ADTree, Alternate Decision tree. the question whether AGO1 and HP1α may have specific binding motifs. Our datasets provide a good opportunity to test this hypothesis. We thus calculated enriched DNA motifs in the AGO1, CTCF and HP1α cluster sets independently (Methods). Due to the high overlap between the three samples, we assessed the enrichment of heptamers using all significant non-overlapping clusters, and built consensus motifs represented as position weight matrices (PWMs) using both complementary strands (Methods).
Datasets Splicing changes between MCF7 and MCF10 were measured using a splice junction array (Affymetrix HJAY) (data available at GSE38864). We considered 1,694 regulated cassette events with significant change (average probe fold-change >1. 5) in the comparison between MCF7 and MCF10. Only events from genes that do not change expression significantly (log 2 -fold change P-value >0. 01) and do not overlap with non-regulated events (see below) were kept, producing 1,134 events, 442 for inclusion and 692 for skipping. Further, in order to have a balanced training set, we randomly chose 442 events of skipping; hence 442 exon cassette events of each type (inclusion or skipping in MCF7, compared to MCF10) were finally selected. As a control, a set of non-regulated events was selected, defined to be exon triplets tested on the array from the same host genes as the regulated events, such that they do not overlap with regulated events and are negative for splicing regulation according to the array experiments.
This resulted in 1,970 non-regulated events. ChIP-Seq data for AGO1, total H3, H3K36me3, H3K9me2, H3K27me3, HP1α, 5 methylated cytosine (5metC) and RNAPII in MCF7 and MCF10 cells were obtained from GSE56826. For AGO1, we only used data for the 4B8 antibody for both MCF7 and MCF10 cells  [37]. As control samples we used ChIP-Seq for a nonspecific antibody (immunoglobulin G (IgG)) and specific controls samples for HP1α and 5metC in MCF7 and MCF10 cells (available from GSE56826). Additional ChIP-Seq data for RNAPII in MCF7 cells was obtained from  [61]  and ChIP-Seq data for CTCF in MCF7 and MCF10 cells was obtained from  [62]. Reads were mapped to the reference genome hg18 using Bowtie  [63]  keeping the best unique matches with at most two mismatches to the reference (-v 2 -beststrata -m 1).
Based on the mean size of the fragments obtained after sonication for each sample, mapped reads were extended to 200 nt in the 5′ to 3′ direction using Pyicoteo  [64, 65] , except for AGO1 reads, which were extended to 350 nt. Using BedTools  [66]  we removed reads overlapping centromeres, gaps, satellites, pericentromeric regions and the 'Duke excluded' regions, which are regions of low mappability defined by ENCODE  [67]. For each sample, we built clusters with the reads that were overlapping each other on the genomic coordinates, discarding single-read clusters, using Pyicoteo. In order to avoid the usage of clusters that are possibly part of the background signal and not of the real ChIP-Seq signal, we used the control samples to identify significant clusters. As the coverage of reads between samples and control can be highly variable, in order to estimate the background level, we considered that each sample is composed of a number of regions with high coverage, likely to be significant, and a large number of regions with low coverage, assumed to be equivalent to the background  [68, 69].
The described model does not include this normalization. Pairwise correlation heatmaps were obtained by calculating Pearson correlation between z-scores for every pair of attributes for each subset of triplets: with E2 either included, skipped or non-regulated, according to the splicing array. The processed datasets are available in a publicly available Biomart  [73]  instance at  [74] , where the information for each event is linked to an Ensembl gene and transcript IDs to facilitate the cross queries with Ensembl databases. Datasets can be exported in various formats, including ARFF (attribute-relation file format), which can be uploaded directly into the WEKA system  [75]  for Machine Learning analyses.
Thus, the WSE method gives the frequency at which each attribute is selected in the optimization procedure. For WSE we used a Genetic Search algorithm to explore different combinations of attributes and an ADTree  [79]  to evaluate the attributes. Repeated runs of WSE did not change the resulting top attributes. Attributes selected by WSE and CFS at frequencies ≥50% and a position in the IG ranking in the top 50% were finally selected. When the selected attributes corresponded to the same ChIP-Seq signal in overlapping regions, the one with the highest IG was selected. In this way, a minimal set of non-redundant attributes with optimal performance was selected (Additional file 1: Table  S6 ). For each of the models the accuracy was calculated using 10-fold cross validation. In this procedure, the datasets are split into training and testing sets in 10 different ways.
Cluster association and motif analysis To study the significance of the co-occurrence of the different ChIP-Seq clusters in specific regions we used the block bootstrap and segmentation method developed in the Encode project  [82]. Using a list of genomic regions and two lists of features mapped to them, this method provides a z-score corresponding to the number of standard deviations of the observed overlap compared to the random expected overlap. We ran version 0. 8. 1 of the script Block Bootstrap and Segmentation method with parameters -r 0. 1 -n 10,000, where r is the fraction of each region in each sample and n is the number of bootstrap samples used. As input for this method, ChIP-Seq clusters and mappable genome regions were used. The motif analysis was carried out in the following way.
Acknowledgments The authors would like to thank  J. Valcárcel  for useful discussions and comments on the manuscript, and  J. Carroll  for kindly sharing his data on CTCF. This work was partly supported by the  European Alternative Splicing Network (EURASNET). Additionally, E. A, N. B, A. P. and E. E were supported by grants  BIO2011-23920  and  CSD2009-00080  from  MINECO (Spanish Government ) and by a grant from the  Sandra Ibarra Foundation for Cancer ; A. R. K. was supported by grants from the  Agencia Nacional de Promoción de Ciencia y Tecnología of Argentina , the  University of Buenos Aires , the  Howard Hughes Medical Institute ; and M. A. by a short term fellowships from  EMBO , the  Journal of Cell Science and UICC.
Elastography is a technique that was first developed for ultrasound imaging, in order to add a stiffness contrast to standard images by measuring the local displacement in a tissue under dynamic or static mechanical excitation. Here, we measure the compression in real time on living spheroids and use a recently proposed  [28]  mechanical modeling of the spheroid as a continuous medium to interpret quantitatively the spatio-temporal behavior of the measured deformation. First, we exclude any potential artifact due to the cryosection process in previous work  [28]. Second, we dynamically measure the compression field as a function of the time. Our experimental data confirm that the response of a spheroid to a mechanical perturbation deviates from what is expected for a homogeneous isotropic sphere and confirms, with real-time measurements inside living spheroids, that a radial gradient in volumetric compression builds up in the core of the spheroid within minutes after the pressure jump.
POC fluxes at 100, 150, and 200 m depth were inferred from total 234 Th export fluxes estimated from 234 Th deficit in surface waters by applying the modeling approach of  Savoye et al. (2006)  for the 234 Th activity balance. 234 Th export fluxes were then converted into POC fluxes using POC / 234 Th ratio of large (> 53 µm) potentially sinking particles at the depth of export. Upper-ocean 234 Th and carbon export obtained in HNLC and Fe-enriched waters were used to assess the impact of natural fertilization on the vertical transfer of carbon. 234 Th-derived fluxes were compared to free-drifting sediment and polyacrylamide gel trap data  (Laurenceau-Cornec et al. , 2015a). Using primary production estimates  (Cavagna et al. , 2014)  we examine spatial and temporal variations in export efficiency during the survey.
, 2014b). This range of Kz values for KEOPS2 is much lower than for KEOPS1 (4 × 10 -4 m 2 s -1 ) obtained using the Osborn model  (Park et al. , 2008a). Vz was calculated using total 234 Th activities instead of the dissolved 234 Th (total 234 Th-particulate 234 Th) because of a poor vertical resolution of particulate 234 Th data in the first 200 m. For all stations, the diffuse flux (Vz) estimated at 100, 150, and 200 m depth was always below 50 dpm m 2 d -1 and represents a negligible contribution to the particle-associated export flux. Lateral transport may also impact the 234 Th budget  (Savoye et al. , 2006)  especially for stations located downstream of the Kerguelen Islands. From our data, this contribution cannot be quantified precisely, and is only qualitatively considered. Given the mean residence of surface water parcels over the plateau at station A3 (2-3 months)  (Park et al.
Acknowledgements. We are grateful to KEOPS2 chief scientists  Stéphane Blain  and  Bernard Quéguiner , and to the captain and crew of  R/V Marion Dufresne  for their assistance and help during the cruise. This research was supported by the  French National Research Agency  (grant no. ANR-10-BLAN-0614 ), the  LEFE-CYBER program  of  Institut des Sciences de l'Univers (INSU) ,  Institut Paul Emile Victor. Financial support was obtained from  Belgian Science Policy (BELSPO , grant  SD/CA/05A ),  Flanders Research Foundation (FWO , grant  G071512N ),  Vrije Universiteit Brussel (Strategic Research Plan) , the  Antarctic Climate and Ecosystem Cooperative Research Centre (ACE-CRC, Hobart, Australia). We are very grateful to  Michael Korntheuer  for state-of-the-art beta counter maintenance, and  Jacques Navez  and  Laurence Monin  for helpful laboratory assistance.
The vertical distribution of dust aerosol represented by extinction profiles is also analysed using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. The annual dust emission simulated by ALADIN over northern Africa is 878 Tg year -1. The Bodélé Depression appears to be the main area of dust emission in northern Africa, with an average estimate of about 21. 6 Tg year -1. The simulated AOTs are in good agreement with satellite and sun photometer observations. The positions of the max-ima of the modelled AOTs over northern Africa match the observed positions, and the ALADIN simulations satisfactorily reproduce the various dust events over the 2006-2010 period. The AOT climatology proposed in this paper provides a solid database of optical properties and consolidates the existing climatology over this region derived from satellites, the AERONET network and regional climate models.
Because of the important role that dust might play in future Published by Copernicus Publications on behalf of the European Geosciences Union. climate change and its potential high impact on the Earth's ecosystems and natural and human environments, it is important to know where the major dust sources are, how dust concentration varies in space and time, and what controls this variability. Northern Africa is the world's main source of dust aerosol, with a relative contribution of about 50 % of the total worldwide production  (Zender et al. , 2003). This region is well suited for studying the impact of aerosols on the radiation budget and climate. Therefore, an accurate database of aerosol content in this region is crucial to identify and quantify this impact, particularly in regional climate models (RCMs).
Changes to this database in numerical models have a sensitive impact on model performance. For example, various studies  (Tompkins et al. , 2005; Rodwell, 2005)  have shown the positive impact of the switch from the  Tanré et al. (1984)  climatology to the  Tegen et al. (1997)  climatology for various aspects of the ECMWF model  (Morcrette et al. , 2009). Tompkins et al. (2005)  have performed two 5day forecasts of the African easterly jet (AEJ) with the old and new climatology, and the results are compared with highresolution dropsonde data from the JET2000 campaign. The results of these simulations show that the new aerosol climatology significantly improves some aspects of the AEJ structure and strength. In the same study, 4 months of 5day forecasts were realized and compared using the contrasting aerosol distributions.
The results show a clear improvement with the new climatology, with the jet strengthened, elongated to the east and less zonal, in agreement with the analyses. The new climatology suppresses deep convection by stabilizing the atmosphere, preventing the ITCZ (intertropical convergence zone) from progressively migrating north during the forecast. A strong reduction in mean equivalent potential temperature at the lowest model level is noted, with the southerly displacement of the ITCZ. More recently,  Kocha et al. (2012)  have shown the impact of dust storms on the cold extratropical outbreak and on the African easterly jet. Today, several data sets for aerosol parameters in northern Africa are available. The Aerosol Robotic Network (AERONET; http://aeronet. gsfc. nasa. gov/; Holben et al. , 1998) , with its specifically designed geographical coverage, provides a robust database of aerosol optical thickness, while the data themselves describe local characteristics at station positions.
The erodible soil fraction is related to bare and rock soil. These surface types are derived from the global data set of land surface ECOCLIMAP at a 1 km resolution, which combines the global land cover maps at a 1/120 • resolution and satellite information  (Masson et al. , 2003). Two hundred and fifteen ecosystems were obtained by combining existing land cover and climate maps, in addition to using advanced very high-resolution radiometer (AVHRR) satellite data. The ECOCLIMAP database is designed in compliance with the SURFEX "tile" approach: each grid box is composed of four adjacent surfaces for nature (ISBA vegetation classes), urban areas (TEB -Town Energy Balance -model), sea, or ocean and lake. The mass fractions of clay, sand and silt are provided from the global 10 km FAO soil data sets.
However, the DB algorithm takes advantage of this surface phenomenology by performing aerosol retrievals in the visible blue spectrum (such as the 0. 47 µm spectral channel in MODIS) and by utilizing the selected aerosol model in the inversion to generate the AOT  (Hsu et al. , 2004 (Hsu et al. , , 2006;; Shi et al. , 2013). Thus, a combination between these two products is made to complete the AOT database for the whole of northern Africa (ocean and land). Over bright arid regions, only DB data are available, offering no alternative choice. Conversely, in the areas with dense vegetation and ocean, only DT data are available and are therefore used in our study in these regions. In addition, we have transition areas with low vegetation, such as the Sahel (10-15 • N). For these areas, both the DB and DT products are available.
Comparison of simulation outputs to Aqua-MODIS observations Figure  10  shows the Level-3 monthly AOTs derived from the combination of the Dark Target and Deep Blue products (MYD08_D3. 051, MODIS-Aqua Ver. 5. 1) at a 1 × 1 • resolution averaged from 2006 to 2010. The MODIS data show important dust activity from January to August. We observe high-AOT values, in excess of 0. 5, over large portions of northern Africa. The most important dust activity is observed in March. Two maxima exceeding 1 can be identified for this month. A primary maximum is located over the Gulf of Guinea, Nigeria, Benin and the region of Ouagadougou (south-west of Niger). This maximum is associated with the southward dust transport, which is very significant in this season. The secondary maximum is located in the Bodélé Depression in Chad and is therefore collocated with the main area of dust emission.
Comparison with AERONET measurements The AOTs simulated by ALADIN have also been compared with the AERONET observations available in the AMMA database and the MODIS products. Figures  11  and 12    Soroa (Fig. 11 ). These three stations are located at the same latitude (13 • N), and they mark the southern boundary of the sources of dust emission. They are affected by dust transport associated with the harmattan wind from March to June, which explains the large AOT values in this season. The low AOTs are observed from November to January, with values around 0. 35, corresponding to the low dust emission activity. In August, the AOTs are also low at Banizoumbou, Cinzana and Soroa. For this month, the West African monsoon is well established and the air circulation is upturned, driving dust aerosol towards the north.
A comparison between MODIS and ALADIN shows that the variations in the averaged monthly AOT are well correlated between the two data sets, but there are noticeable differences in terms of quantification. For instance, over Banizoumbou, MODIS observations are slightly larger than AERONET observations for all months, with a maximum of about 1 observed in April. This overestimation is particularly perceptible in the wet monsoon season (July and August). For this site, the MODIS data provide a good correlation coefficient (0. 864) (Fig. 12 ). For ALADIN, the AOT maximum is given in March, with a rather large value of about 1. 2. ALADIN overestimates the AOTs from November to March and underestimates them from April to September, except for July. For Banizoumbou, a lower correlation coefficient (0.
Indeed, this region is affected by salt and biomass-burning products, which heavily influence the extinction coefficients. It is interesting to note that, despite the absence of any data assimilation process for dust content in ALADIN, the simulations remain overall satisfactorily correlated with observations. This result suggests that the model, whose initial and lateral boundary conditions are regularly refreshed by the global model ARPEGE, does not generate any significant drift of dust content over the whole 5-year range of the simulations. Furthermore, the model seems able to maintain a correct relative impact of emission and deposition processes, which is reflected by the realistic characteristics of the predicted AOT fields. In future, ALADIN's ability to simulate the dust aerosol content over the Mediterranean Sea will be investigated.
Acknowledgements. This paper is dedicated to the memory of Laurent Gomes. Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from  France , the  United Kingdom , the  USA  and  Africa. It  has been the beneficiary of a major financial contribution from the  European Community 's  Sixth Framework Research Programme. Detailed information on scientific co-ordination and funding is available on the AMMA International web site http://www. amma-international. org. We also acknowledge the MODIS mission scientists and associated  NASA  personnel for the production of the data used in this research effort. This work was supported by the  Algerian Met Office (ONM) ,  Météo France ,  Centre National de Recherches Météorologiques (CNRM)  and the  French Embassy in Algeria.