dataesr/scientific-paragraphs-categorization huggingface dataset:
MED12-improved dataset for the Atlantic Here, the ocean model NEMOMED12, regional version of NEMO V3. 2, same as in Sect. 3. 3. 4 but on 75 levels, is used in a forced mode. As atmospheric conditions, we use ALDERA  (Hamon et al. 2016) , a new atmospheric forcing resulting from the dynamical downscaling of ERA-Interim with the regional atmospheric model ALADIN. The relaxation method of the SSH in the buffer Atlantic zone is the same as for CNRM-RCSM4 and MORCE-MED. In this simulation, we choose to prescribe a complete signal of Atlantic sea level to consider both local and global changes, at seasonal and interannual time scales. To achieve this, we use data from the ORAS4 global ocean reanalysis  (Balmaseda et al. 2013 ) which includes sea level contributions from ice sheet mass loss, glaciers ice melt, changes in land water storage, as well as global thermal expansion.
ORAS4 provides Atlantic boundary conditions for SSH, temperature and salinity to the regional ocean model for the period 1980-2012. Because ORAS4 underestimates the regional seasonal cycle in the near Atlantic region compared to satellite-derived gridded data (Fig. 1 ), we decided to add a 12-month correction, calculated from the difference between CCI-ECV and ORAS4, in the buffer zone, considering a 12-month climatology for the 1993-2010 period for both datasets. We add this 12-month correction to each year of ORAS4 near-Atlantic sea level data prescribed in the buffer zone of the Mediterranean model. In MED12, the Black Sea net inflow is also treated as a river. This simulation covers the 1980-2012 period. In summary, the differences between the four ORCMs are in the model resolution and forcing, and in the information imposed in the Atlantic.
Seasonal cycle The seasonal cycle of sea level averaged over the Atlantic buffer zone (from the western limit to Gibraltar) differs a lot in the different simulations, depending on the information prescribed. With the E-P-R water report method, LMDZ-MED shows an inverse seasonal cycle in the buffer zone simply because the net E-P-R water budget is larger in the Mediterranean during the winter season. This method had been designed to prevent the Mediterranean from emptying as there is a net freshwater loss over the basin. However, the downside of this approach is that the seasonal cycle modelled in the Atlantic box is wrong, as shown in Fig. 1. In the case of CNRM-RCSM4, the seasonal cycle is underestimated compared to the altimetry-derived dataset CCI-ECV because the COMBINE global ocean reanalysis does not assimilate altimeter-derived SLA.
The MORCE-MED model prescribes, from 2002 on, sea level information from a global ocean reanalysis which assimilates SLA from satellite. MORCE-MED is therefore closer to the seasonal cycle of the reference CCI-ECV than the two previous simulations. However, MED12 is obviously the closest, since the seasonal cycle prescribed at the boundary corresponds to CCI-ECV, due to the correction applied to the data derived from ORAS4 in the MED12 simulation. However one can notice from the differences between the CCI-ECV and MED12 curves that the relaxation only happens on a small band of the Atlantic box of the model domain. After 6 • W, the model evolves freely.
Interannual variability Figure  2  represents interannual sea level variations averaged over the Atlantic zone of the models. The absence of trend is noted for LMDZ-MED, which does not have Atlantic variable sea level conditions, as well as for CNRM-RCSM4 and MORCE-MED (before 2002) whose prescribed Atlantic conditions do not integrate SLA from satellites. Considering CCI-ECV as the reference, MED12 represents best the interannual variations and trend with a correlation of 0. 91 with the CCI-ECV dataset (Table  1 ). Among the other three simulations, CNRM-RCSM4 performs best until 2003, because MORCE-MED uses the relaxation towards GLO-RYS-1 only from 2002 onward. The interannual variability of MORCE-MED before 2002 is thus very poor due to the experimental setup which does not take into account the Atlantic interannual variability before that year.
Seasonal cycle Figure  3  represents the seasonal cycle of the models and the reference. The "EPR water report" method used in LMDZ-MED leads to a flat seasonal cycle. For the three other models, the simulated seasonal cycle is highly dependent on the quality of the Atlantic dataset. The prescription of data from a reanalysis with no assimilation from satellite sea level information, as in CNRM-RCSM4, leads to a Mediterranean sea level which has an underestimated seasonal cycle. MORCE-MED and MED12 both use Atlantic data including satellites information: MED12 has a correction of the seasonal cycle toward CCI-ECV in the prescribed Atlantic dataset, and MORCE-MED prescribes data from the GLORYS-1 reanalysis which assimilates satellite data for the simulated period 2002-2008, and applies a correction to follow the seasonal cycle of GLORYS-1 for the simulated period 1989-2001.
For these reasons, MED12 and MORCE-MED both have a Mediterranean seasonal cycle which is consistent with the reference. This is clearly depicted by the root mean squared deviation (RMSD) with the value of 1 cm for both MORCE-MED and MED12, 2. 5 cm for CNRM-RCSM4 and 5 cm for LMDZ-MED (Table  2 ). MED12 presents a 1-month forward lag of the max, whereas MORCE-MED and CNRM-RCSM4 have no lag. Concerning the amplitude of the seasonal cycle, MED12 (13 cm) and MORCE-MED (12. 5 cm) are both in good agreement with the  reference (14 cm). CNRM-RCSM4 only simulates half of the amplitude displayed in satellite-derived data (Table  2 ).
Spatial patterns In the eastern basin, MED12 shows the most consistent circulation compared to  Rio et al. (2014). For example, in the other simulations, the Algerian current crosses the Sicily Channel but then circulates too far north in the Ionian, especially in MORCE-MED. MED12 displays the most realistic pattern for this feature. In the Levantine basin, the Rhodes cyclonic gyre, where winter convection occurs, is only accurately represented in MED12. The anticyclonic Ierapetra gyre (South-East of Crete) is nicely displayed in the reference dataset but this feature is absent in all the model simulations. The differences between the simulations and the altimetric data are mainly seen on spatial mesoscale patterns, the very large scale patterns being in agreement between both products. This is probably because the model spatial resolution ranges from 6 to 12 km and is not enough to properly simulate all the mesoscale and sub-mesoscale processes that play a role in the Mediterranean.
The positive/negative dipole south-east of Crete could be attributed to a shift of the Ierapetra gyre. In the western basin, the interpretation of the changes is more delicate. Concerning models' capability to represent spatial trends, we found that, except in MORCE-MED, local negative trend anomaly patterns of the western basin are in correct agreement with those displayed by the satellite-derived data despite the models' difficulties to represent an adequate circulation in the Alboran region (see Sect. 4. 2. 3). For the eastern basin, the negative trend anomaly pattern of the north-west Ionian, attributed to the post-EMT recovery, is present in all models, although it is shifted southward in MORCE-MED. Concerning the absence of trend anomaly patterns in North Aegean in all models, the representation of the Bosphorus is too crude to be able to show the impact of changes in the Black Sea outflow on the circulation.
Conclusions This study aimed at evaluating different approaches to simulate Mediterranean sea level variability and change with regional Mediterranean climate models. In particular, several choices commonly used for the Atlantic lateral boundary conditions (LBCs) were analysed and the resulting simulated Mediterranean sea level was compared with observational datasets. Results show that Mediterranean mean sea level is strongly influenced by the Atlantic conditions and thus the quality of the information in the LBCs is crucial for the good modelling of Mediterranean sea level. This allows to account for global sea level change (including global steric and mass addition), since the Atlantic is known to drive the Mediterranean variability at interannual and interdecadal time scales. With improved Atlantic LBCs, the MED12 simulation accounts for both local and large-scale sea level changes from the near-Atlantic.
Acknowledgements F. Adloff was supported by an  AXA Research Fund postdoctoral fellowship. G. Jordà  acknowledges a Ramón y Cajal contract ( RYC-2013-14714 ) funded by the  Spanish Ministry of Economy  and the  Regional Government of the Balearic Islands. This work is part of the  Med-CORDEX initiative  (http://www. medcordex. eu)supported by the HyMeX programme (http://www. hymex. org) and of the  CMUG  project supported by  ESA  in the framework of the  CCI program. This research has received funding from the  French National Research Agency (ANR)  project  REMEMBER  (contract Fig. 6 Spatial anomalies of sea level trend with respect to basin average, in mm year -1 , for the common period 1993-2008. Model results are compared with the trends from the satellite-derived dataset CCI-ECV.
We use high-resolution satellite observations of winds and ocean color as well as hydrography and in situ Chl-a fluorescence measurements from cruises and a mooring site to assess whether along-front winds modulate the upwelling at the SBF off Patagonia. We explore to what extent these upwelling patterns modulate biological productivity, addressing the biological impact of the oscillating wind effect on the upwelling through the Chl-a response. Section 2 describes general characteristics of the Patagonian shelf region and proposed mechanisms for the local upwelling at the shelf break. In section 3 we present the data sources used. Satellite-based results suggest that along-front winds can modulate Chl-a at the SBF, potentially through isopycnal tilting, and are presented in section 4. 1. We show synoptic evidence of isopycnal tilting under opposing along-front wind conditions from hydrographic sections across the SBF (section 4.
Satellite Data We use satellite winds and Chl-a estimates to evaluate the influence of along-front winds on phytoplankton blooms near the SBF off the eastern coast of Patagonia. The long satellite records (i. e. , 12 years) allow us to assess statistical significance and complement evidence of wind-induced upwelling at the SBF from in situ observations. Daily satellite Chl-a serves as a proxy for phytoplankton biomass within one optical depth of the surface (e. g. ,  Morel and Berthon, 1989). The data are available at 9 km resolution, covering the period 2000-2011 for which satellite wind data are also available. We use satellite Chl-a derived using the Southern Ocean SPGANT algorithm  [Mitchell and Kahru, 2009] , which uses the maximum band-ratio algorithm  [O'Reilly et al. , 1998] , but with coefficients fitted to Southern Ocean data, and which merges satellite data from several sensors (i.
e. , OCTS, SeaWiFS, MODIS-Aqua, and VIIRS). Although SPGANT has not been validated against in situ Chl-a for this particular region, our conclusions do not rely on Chl-a absolute values and are not sensitive to the choice of the Chl-a data set used (e. g. , Chl-a from GlobColour leads to qualitatively similar results). Because Chl-a is lognormally distributed, for averaging we use a geometric mean, obtained by averaging the logarithm of Chl-a and then inverse-transforming the averaged logarithm  [IOCCG, 2004]. We use Cross-calibrated Multi-Platform (CCMP) 6 hourly winds for the period 2000-2011  [Atlas et al. , 2011]. CCMP winds result from a 4-D variational analysis based on multiple satellite data sets, in situ winds and European Centre for Medium-Range Weather Forecasts (ECMWF) analysis winds.
In Situ Observations We extend the analysis from satellite fields to examine processes occurring throughout the water column in the vicinity of the SBF using the historical database of hydrographic stations collected in the region by the Servicio de Hidrograf ıa Naval (SHN, www. hidro. gov. ar/ceado/ceado. asp) and the Instituto Nacional de Investigaci on y Desarrollo Pesquero (INIDEP, http://www. inidep. edu. ar/oceanografia/) in Argentina. A summary of all in situ observations used in this study is presented in Table  1.
Historical Hydrographic Stations We examined the historical database of hydrographic stations from the SHN from 1987 onward, when CCMP satellite winds are available, as well as stations collected by INIDEP for the years 1992-1998. For this study, we analyzed stations within 200 km of the 200 m isobath, between approximately 388S and 508S. We restricted the analyses to stations south of 388S to avoid buoyancy effects from the La Plata River plume, which outflows anticyclonically toward the north near 368S, and may hinder isopycnal steepening at the shelf break associated with oscillating winds (for the plume extent see, e. g. ,  Piola et al. [2000 Piola et al. [ , 2008]] ,  Guerrero et al. [2014] , and  Matano et al. [2014] ). Hydrographic stations for transects across the SBF off Patagonia were sampled in the years  1988-1994, 1996, 1997, 2005, and 2006  during different seasons.
GEF Cruises In 2005 and 2006, hydrographic stations for transects across the SBF off Patagonia were sampled during Global Environmental Facility (GEF) Patagonia cruises on board R/V ARA Puerto Deseado. Cross-shelf transects were carried out in October 2005 (GEF-1), March 2006, and September 2006 (GEF-3) with a horizontal resolution of 25 km. CTD profiles for all GEF cruises are available at the National Oceanographic Data Center, and details of the data processing are reported by  Charo and Piola [2014]. In this work, we present   1992 -1995 , 1997 -1998 , Variable (SHN and INIDEP) 2003 , 2005 , 2006  a Web links for data sources can be found in the data section and acknowledgements. Journal of Geophysical Research: Oceans 10. 1002/2016JC012059 results from two hydrographic transects acquired during GEF-1 and GEF-3 that were sampled with opposing meridional wind conditions, at 418S (off Rinc on) and at 448S (off Vald es Peninsula).
The data are assumed to have a priori uncorrelated noise, with a noise to signal ratio of 0. 25, and interpolated data with error greater than 0. 1 were masked. Shipboard wind observations were recorded during GEF-1 and GEF-3 cruises. For GEF-1, hourly winds recorded at the bridge are available (N 5 19 for Rinc on and N 5 18 for Vald es transects). During GEF-3, an integrated sampling system made continuous measurements of wind direction and speed. Original data were averaged every 10 min and converted to 10 m winds (N 5 231 for Rinc on transect). These observations, recorded at higher temporal resolution than that of satellite winds, are useful to show directional wind variability during the time periods when the hydrographic transects were sampled. Wind roses for each of these transects were constructed to show the distribution of relative frequencies of wind direction and intensities.
Composites of Satellite Chl-a by Wind Direction To investigate the impact of along-front winds on Chl-a variability at the SBF, we first looked at composites of satellite Chl-a, segregating the data by wind direction (Figure  3 ). We use summer data here, because in the summer phytoplankton growth is presumed to be nutrient limited, and previous correlation analyses showed wind speed and satellite Chl-a to be correlated in summer in this region  [Carranza and Gille, 2015] , while correlations are weak and nonsignificant in other seasons. Each satellite Chl-a grid cell was screened for positive (i. e. , southerly) or negative (i. e. , northerly) along-front winds. Because north of 448S the shelf break does not have a north-south orientation (see Figure  1 ), we rotated winds 358 clockwise north of 448S Journal of Geophysical Research: Oceans 10.
Acknowledgments We greatly acknowledge SHN and  INIDEP from Argentina  for collecting and providing a vast majority of the in situ observations in the region, available from Charo and Piola [2014], from the World Ocean Database at the National Oceanographic Data Center (NODC, https://www. nodc. noaa. gov/OC5/WOD/pr_wod. html),and at http:// www. inidep. edu. ar/oceanografia; Chris Linder  for facilitating the MATLAB code to compute cross-front mean transects (available at http://www. whoi. edu/science/PO/people/clinder/software. html; James Pringle  for useful discussions and two anonymous reviewers whose comments contributed to improve this manuscript; and  Mati Kahru  for processing and providing the satellite Chl-a data. NASA's Research, Education and Applications Solution Network (REASoN)  and  MEaSURES  programs funded development of the CCMP wind fields, which are distributed by the  Physical Oceanography Distributed Active Archive Center  (http://podaac.
jpl. nasa. gov/). MMC and STG were funded by a  NASA   NESFF fellowship  ( NNX12AN41H 001 ),  NASA's Ocean Vector Wind Science Team  ( NASA NNX14A078G ), and the  Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project  ( NSF PLR-1425989 ). ARP acknowledges the support of grant  CRN3070  from the  Inter-American Institute for Global Change Research , which is supported by the  US National Science Foundation  grant  GEO-1128040. SIR acknowledges the support of grant  PIDDEF 47/12  from the  Argentine Ministry of Defense. Journal of Geophysical Research: Oceans.
S4F ), there was no enrichment of MBD-Venus for these loci (Supplemental Fig. S4L ), suggesting that the latter has very limited affinity for mCHH. We then evaluated DYNAMET binding quantitatively. The data for SUVH9-Venus over TEs (Supplemental Fig. S4C ) suggest a good correlation between CHH methylation levels and binding intensity (R 2 = 0. 68). For MBD-Venus, while stronger enrichment in highly methylated TEs is highly significant, we observed enrichment even for low levels of methylation (<10%, Supplemental Fig. S4G ). The same was true for gene body methylation. Thus, SUVH9-Venus is a better quantitative tool than MBD-Venus. To complement these analyses, we then estimated the correlation between the ChIP data for MBD-Venus and MeDIP-seq (methylated DNA immunoprecipitation [MeDIP] followed by sequencing) data from Arabidopsis (Supplemental Fig.
Enrichment in TEs was reduced in the drm2 mutant (Supplemental Fig. S6A, C ) and in the cmt2 mutant on both short TEs and for the central domain of longer TEs (Supplemental Fig. S6B ), both of which are known targets of CMT2  (Zemach et al. 2013). Enrichment was eliminated in the drm1 drm2 cmt2 cmt3 quadruple mutant (Supplemental Fig. S6D ), which abolishes CHH methylation  (Stroud et al. 2014). These experiments collectively indicate that SUVH9-Venus recognizes DRM2and CMT2-dependent CHH methylation. We next tested whether MBD6-Venus and SUVH9-Venus DYNAMET lines could monitor changes in methylation states in planta. We thus analyzed DYNAMET fluorescence during male gametogenesis, where GWBS data are available  (Calarco et al. 2012). Similarly, we observed in living cells that MBD-Venus patterns were stable throughout male gametogenesis, with a slight reduction in the vegetative nucleus (Fig.
Microscopy Imaging of fluorescence for DYNAMETs and other reporter lines from freshly dissected tissues was performed with an inverted laser scanning confocal microscope (Zeiss LSM780) and a 40× oil immersion objective lens (plan apo, N. A = 1. 3; Zeiss) with settings for detection of Venus (excitation, 514 nm; emission, band-pass 515-545 nm) or mCherry (excitation, 561 nm; emission, band-pass 561-621 nm). Fluorescence was acquired with a GaAsP detector with the pinhole set to one Airy unit and optimal sectioning as suggested by the software (Zen). For quantification of the immunostaining experiments, we acquired images using a Zeiss LSM780 inverted confocal scanning microscope with sequential acquisitions for each fluorochrome (excitation, 405 nm for DAPI and 488 nm for Atto 488 and Alexa 488).
Bioinformatics Quality filtering and base calling were performed using the CASAVA pipeline. Reads were mapped to the Arabidopsis TAIR10 annotations using Bowtie2, allowing one mismatch. Binning and read counts/coverage relative to annotations were performed using BedTools2 intersectBed. ChIP peak detection was performed using MACS's call peak function, relative to input DNA used as control. Aggregate profiles for exon/intron coverage were performed using ngsplot. Aggregate profiles over TEs were calculated with VAP  (Coulombe et al. 2014). Data sets for the Arabidopsis methylome and MeDIP-seq were downloaded from Gene Expression Omnibus (GEO; accession nos. GSM1499351 for methylome data and GSM1326797 for MeDIP data). To estimate correlation coefficients between the MeDIP and MBD-Venus data, reads were binned over 500-bp windows, and correlations were calculated using R.
Acknowledgments We thank  T. Harrop ,  F. Roudier , and  M. Mirouze  for critical reading of the manuscript;  G Conejero ,  J. Cau , and  M. Lartaud  from the  Montpellier RIO Imaging Facility  for assistance in microscopy and image processing;  S. Jacobsen  for the quadruple mutant drm1 drm2 cmt2 cmt3; and  T. Lagrange  for the NRPD1 antibody. We acknowledge funding from the  European Union   H2020 program  ( REP-658900-1 ) to D. G. ; the  Agence Nationale de la Recherche  ( ANR-12-BSV2-0013 ) to D. G. , C. M. , B. S. , and M. I. ; and the  Howard Hughes Medical Institute-Gordon and Betty Moore Foundation , the  National Science Foundation  ( DBI-1025830 ), and the  National Institutes of Health  ( 5R01GM067014 ) to R. A. M.
) (expert agreement). Chest ultrasound may also be used to help with differential diagnoses in pulmonologists trained in its use. Chest CT scan is not systematically recommended. It is superior to chest x-ray for diagnosing pleural effusion, pneumonia or cancer. It should be offered: • in general, in case of diagnostic uncertainty (expert agreement); • specifically, if the clinical and biological data lead to suspect pulmonary embolism (angio-CT scan) (grade G1A). It is recommended to systematically perform an electrocardiogram for inpatient AECOPD (expert agreement). It is not recommended to systematically perform an echocardiography except in case of suspicion of cardiogenic pulmonary oedema (expert agreement).
Our data indicate that depending on the cellular origin of the tumour, STING ligands may also exert direct antitumour propertiesindependently from their adjuvant activity. We also assessed VLPs as vehicles to deliver CDNs to malignant T-cell lines and thereby revealed impressive activation of cell death in both murine as well as human cancer cell lines. Based on this, we speculate that CDN containing VLPs generated to specifically target cancer cells might be a promising strategy to deliver CDNs directly to cancer cells. This might be especially relevant in the context of T-ALL, where tumour cells reside in areas not accessible for intratumoral drug delivery. Importantly, our findings are not in controversy to the capability of cGAMP or other STING agonists as adjuvant for immunotherapy. In fact, when added to T cells cGAMP was only weak in triggering apoptosis.
Briefly, cells were washed once with PBS and once with Annexin V binding buffer. Cells were stained with Annexin V (1:200 dilution) for 15 min at 4 °C in Annexin V binding buffer. Data were acquired on an Accuri C6 (BD) flow cytometer and analysed in FlowJo. The induced cell death was calculated as [(% death with CMA treatment -% death without CMA treatment)/ (100 -% death without CMA treatment)]. The FACS gating strategies are shown in Supplementary Fig. 10.