dataesr/scientific-paragraphs-categorization huggingface dataset:
4  (left panel) and shows large drop at latitudes inside the Arctic Ocean. An analysis of the Centre for Polar Observation and Modelling (CPOM) dataset  (Armitage et al. , 2016)  or the Climate Change Initiative (CCI) dataset  (Rose et al. , 2019)  does not show the same pattern. Unrealistically low variance levels will cause the interpolation to dampen signals during the  mapping, which is unwanted. We estimate an updated signal variance map by locally taking the maximum variance level among DUACS, CCI and CPOM data for latitudes greater than 60 • N. The resulting variance map is shown in Fig. 4  (right panel). This method introduces a discontinuity, which shall be addressed in future versions of the product, but does not impact the final product variance (see Fig. 7 , right panel). Two terms control the prior error level in the input data: uncorrelated noise and long-wavelength errors (LWEs).
Accurate error priors will prevent error artifacts being interpreted as real signals during the interpolation. Both were tuned to account for regional characteristics. DUACS standard processing uses different noise levels for different missions, reflecting the actual level of noise of each mission, plus the unobservable part of the ocean dynamics. For the Arctic dataset, we rely on a simpler approach with a noise level for open ocean measurements: one for leads measurements and one for LWEs. Uncorrelated noise level priors are derived from existing DUACS noise levels under the following assumptions: -Over ocean, our measurements should be slightly noisier than the standard processing due to the modeled wet tropospheric correction and a less accurate mean sea surface model  (Pujol et al. , 2018).
Again we derive the LWE priors from existing DUACS priors and set a minimum LWE variance of 10 cm 2 for all latitudes greater than 68 • N to account for uncorrected radial orbit errors. This value was empirically set based on the analysis of global empirical orbit error corrections (available from DUACS) which provide a proxy for radial orbit errors. The resulting LWE variance distribution, which is used for all three missions considered here, is shown in Fig. 6. Again, our prior estimation method does introduce some discontinuities that shall be addressed in future dataset version, but these do not appear to have a large negative impact on product quality. Correlation scales are kept unchanged from the DUACS global processing, and SL anomaly fields are interpolated onto a 25 km EASE2 grid  (Brodzik et al.
Data quality assessment In this section we present some product validation results. Assessing the product accuracy is difficult in the Arctic Ocean as few independent validation datasets are available  (Smith et al. , 2019). Here we rely on comparisons between mono-mission products (Sect. 4. 1), analysis of regional statistics (Sect. 4. 2), comparisons to the DUACS global product (Sect. 4. 3) and comparisons to tide gauge data available in the basin (Sect. 4. 4).
Regional statistics Maps of the mean SL and variance of SL in the Arctic Ocean, derived from the combined products, are shown in Fig. 7. SL variance levels are consistent throughout the Arctic Basin. This suggests a good product accuracy as high variance levels in the interior of the Arctic Ocean would be an indication of errors. The variance distribution is consistent with previous datasets  (Armitage et al. , 2016; Rose et al. , 2019)  with high variance levels along the Russian Arctic coasts. Such variance levels could result from continental shelf wave propagation  (Danielson et al. , 2020). Another prominent feature is the SL variance's slight drop above 81. 5 • N. This is expected, as in this area C2 is the only radar altimeter mission, and the product is therefore unable to reach the same resolution as at lower latitudes.
Comparisons to the DUACS global product The Arctic regional product presented here focuses on SL estimation in ice-covered areas, and its accuracy over open ocean might be hindered by some processing choices such as using the modeled wet tropospheric correction or the lack of a proper cross-calibration prior to the optimal interpolation. Comparing the Arctic Ocean product with the Copernicus Marine Environment Monitoring Service (CMEMS) global dataset  (Taburet et al. , 2019)  is a way to assess whether, despite these processing choices, we still have an acceptable performance over open ocean surfaces. To perform this comparison, CMEMS grids are bilinearly interpolated onto the 25 km EASE2 grid used for the Arctic Ocean product. The mean and variance of SLA differences are shown in Fig. 8.
Comparisons to tide gauges Tide gauges provide an independent measurements of SL variability and are very valuable for the validation of satellite altimetry products (e. g. ,  Cipollini et al. , 2016). They are relatively scarce in the Arctic Ocean. Here we use data from the GLOSS/CLIVAR (Global Sea Level Observing System/Climate and Ocean -Variability, Predictability, and Change) tide gauge dataset from the University of Hawaii Sea Level Center (UHSLC)  (Caldwell et al. , 2010). Hourly tide gauge time series are de-tided using a Demerliac filter, corrected for dynamical atmospheric effects using the MOG2D model  (Carrère and Lyard, 2003)  and from glacial isostatic adjustment effects using the ICE5G-VM2 model  (Peltier, 2004). A total of 27 stations are left after removal of records with obvious issues or large data gaps.
Co-location with the altimetry dataset is performed by averaging altimeter grid points within 50 km (multi-mission data) or 150 km (mono-mission data) of the tide gauge position. Table  5  summarizes the correlations and root-mean-square deviation (RMSd) of altimetry and in situ SL differences for the combined product and for the three mono-mission products. For the combined product, the mean correlation across all stations is 0. 78, and the mean RMSd is 5. 3 cm, indicating an excellent agreement between tide gauge and altimeter data. The combined product performs better than any mono-mission product, indicating that the product accuracy benefits from the multi-mission combination. To illustrate the level of agreement between altimetry and tide gauges, the Prudhoe Bay station records are shown in Figure  9.
Data availability The different gridded datasets described in this paper are freely available, after registration, on the AVISO website (https://www. aviso. altimetry. fr/, last access: 19 Novem-P. Prandi et al. :  Arctic sea surface height maps from multi-altimeter combination ber 2021) at https://doi. org/10. 24400/527896/a01-2020. 001  (Prandi, 2020). Tide gauge data used in this study were retrieved from the UHSLC data portal at http://uhslc. soest. hawaii. edu/data/ (last access: 20 November 2021)  (Caldwell et al. , 2010 , https://doi. org/10. 7289/V5V40S7W).
Conclusions In this paper, we document a new Arctic Ocean sea level dataset based on the combination of measurements from three satellite radar altimetry missions: SARAL/AltiKa, CryoSat-2 and Sentinel-3A. The processing applied to those three missions is described, as well as the optimal interpolation scheme used to create sea level anomaly fields. Combined sea level anomaly and absolute dynamic topography fields are available over 3 years (from July 2016 to April 2019) on a 25 km grid, every 3 d, for all latitudes greater than 50 • N. Temporal extensions are planned to increase the time span of the dataset. Comparisons to the DU-ACS global product suggest that despite focusing on high latitudes and ice-covered areas, the product performs well in permanently open ocean areas at lower latitudes.
This dataset already helped support the existence of newly developing water pathways north of Svalbard  (Athanase et al. , 2021). Comparisons with tide gauges available in the Arctic Ocean show that the combined product is able to capture some of the high-frequency sea level variability observed by tide gauges and generally performs better than any of our single-altimeter analyses. This unprecedented resolution may be useful for the characterization of small-scale Arctic Ocean circulation features.
Acknowledgements. The work presented here was performed within the frame of the  CNES Alti-Doppler Glaciologie  contract. Radar altimeter data were provided by  CNES (SARAL/AltiKA , Sentinel-3A) and  ESA  ( CryoSat-2 ). Review statement. This paper was edited by  Giuseppe M. R. Manzella  and reviewed by two anonymous referees.
Acknowledgments Parts of this work were supported by grants from  "Institut Carnot Santé Animale " (Influenza-2011 and FlumLi, to D. M. ), " Fond de Recherche Influenza Aviaire " ( FRIA2019 ,  PREDYT , to R. L. G. ) and " Agence Nationale de la Recherche " (grant  ANR-17-CE35-0007 , REACTIV, to C. C. ). JM was supported by a  FCPR grant  ("formation complémentaire par la recherche") awarded by the  French ministry of agriculture. We thank  Marlène Hery ,  Kathleen Auger ,  Roxane Trepier ,  Mathilde Bauducel ,  André Tiffoche  and  Jerôme Pottier  ( IERP-UE907 , Jouy-en-Josas Research Center, France DOI:  10. 15454/1. 5572427140471238E12 )for technical assistance in animal experiments. IERP Facility belongs to the  National Distributed Research Infrastructure for the Control of Animal and Zoonotic Emerging Infectious Diseases through In Vivo Investigation  ( EMERG'IN DOI: 10.
Funding This work was supported by the  Agence Nationale de la Recherche  [ ANR-17-CE35-0007 , REACTIV];  Institut Carnot Santé Animale [FlumLi] ;  Institut Carnot Santé Animale  [Influenza-2011];  Ministère de l'Agriculture et de l'Alimentation  [formation complémentaire par la recherche];  Délégation Générale de l'Alimentation  [ FRIA2019 , PREDYT].
Acknowledgments: A. C. is part of the  Pasteur-Paris University (PPU) International PhD Program , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807. We thank  Hugo Varet  and  Odile Sismeiro ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA; for RNA-sequencing and analysis.
The Radio and Plasma Waves (RPW) instrument on the ESA Solar Orbiter mission is designed to measure in situ magnetic and electric fields and waves from the continuum up to several hundred kHz. The RPW also observes solar and heliospheric radio emissions up to 16 MHz. It was switched on and its antennae were successfully deployed two days after the launch of Solar Orbiter on February 10, 2020. Since then, the instrument has acquired enough data to make it possible to assess its performance and the electromagnetic disturbances it experiences. In this article, we assess its scientific performance and present the first RPW observations. In particular, we focus on a statistical analysis of the first observations of interplanetary dust by the instrument's Thermal Noise Receiver. We also review the electro-magnetic disturbances that RPW suffers, especially those which potential users of the instrument data should be aware of before starting their research work.
2020). The design of a stacer is such that it deploys under its own power until it reaches the end of travel, with its speed and final length controlled by means of a cable and a flyweight brake. All the deployments of RPW stacers on ground, in conditions simulating the absence of gravity, showed that the typical duration of the deployment for a 6. 5 m long stacer was on the order of 12 sec. In order to ensure that the full length of the stacers was deployed in flight, we analyzed the RPW data recorded during the respective deployments. Figure  1  shows these data for PY/Ant2 antenna. In this figure, we display the Low Frequency Receiver (LFR) PY-PZ probe to probe electric potential (in red) and the Time Domain Sampler (TDS) time series of the PY maximum amplitude electric potential (in black).
2009; Meyer-Vernet et al. 2016). This is illustrated by Figure  4 , where a typical TNR spectrum observed during a dust impact on the spacecraft body is shown in red. This particular spectrum has been recorded on June 4, 2020 at 02:07:11. 91. The two spectra in black, which are typical of the shot noise acquired by RPW monopoles are measured 17 sec before and after the time indicated above. The dust spectrum can be fitted by the power law as f -3. 26±0. 23 , which is much steeper than the previous and consecutive spectra. We have analyzed all the TNR Band A data from March 1st, 2020 to March 12th, 2021, as we did for the above example shown in Figure  4. The upper panel of Figure  5  displays the power index γ resulting from a power law fitting of the form PS D A = PS D 0 × f γ. There are 2,598,489 data points in this figure covering 340 days of observations.
Acknowledgements. Solar Orbiter is a mission of international cooperation between  ESA  and  NASA , operated by  ESA. The RPW instrument has been designed and funded by  CNES ,  CNRS , the  Paris Observatory , The  Swedish National Space Agency, ESA-PRODEX  and all the participating institutes. J. Rodriguez-Pacheco  wishes to thank  FEDER/MCIU-AEI / Project  ESP2017-88436-R. R. F. Wimmer-Schweingruber  acknowledges support by the  German Space Agency (DLR)  under grant  50OT2002. V. Krupař  acknowledges the support by  NASA  under grants  18-2HSWO218 2 -0010and19 -HS R -19 2 -0143.
g. , ensuring developmental assessment comprises part of care through childhood. Although the frequency of this genotypic variant in unrelated, molecularly confirmed, individuals with NF1 is small (0. 78%  (27/ 3442)  in the NF1 Leiden Open Variation Database (LOVD) database [31] and 0. 88%, 74/8400 in University of Alabama cohort  [20] ), knowledge of this specific genotype-phenotype correlation is important for those families affected as it is possible to provide longterm reassurance about the likely low risk of malignant complications.
g. ,  32  ). The clinopyroxene in Unit 1 basalts spans a range from diopside to augite according to the IMA classification  33  , with Mg numbers up to 95 (Figs. 1c, 3a ). Major and trace element analyses are presented in Supplementary Table  1 , and additional major elements only in Supplementary Table  2. Selected grouped binary compositional plots of these analyses are presented in Fig. 3. Data for clinopyroxene in individual subunits and Unit III 34 are shown in Supplementary Fig. 2. Unit III comprises volcaniclastic-rich sediments of Eocene-Oligocene-age derived from the KPR  34  (data in Supplementary Table  3 ). In Fig. 3 , comparative data are displayed for: clinopyroxene grains from within the oldest sediments recording nascent IBM arc (Unit IV; Eocene age  35  ) overlying Unit 1 ASB basalt; MORB and MOR gabbros (MORG; references in Supplementary Material); references in Supplementary Material); 352 FAB and boninite  36  , Oman ophiolite  37  and backarc basalts (Data from PetDatabase website) (Fig.
2c-e. Grouped ternary and binary spinel compositional plots are shown in Fig. 4. Overall, the spinel in Unit 1 basalts has a unique compositional range compared with spinel in the most abundant basalt types of MOR, arcs, ocean islands and their possible progenitors, the large igneous provinces. Compositions form a continuum extending from Al-Cr-rich (pleonaste-chromite) to Ti-Fe 3+ -rich (titaniferous magnetite). In the Al-Cr-rich range, Unit 1 spinel extends to more aluminous compositions than those of MORB  38, 40  (Fig. 4a ), but have slightly lower Mg numbers (Fig. 4b, c ). A notable feature is the Unit 1 spinel is consistently offset to lower Mg number for a given Cr/(Cr + Al) (Fig. 4b ) and lower Fe 3+ /(Cr + Al + Fe 3+ ) Fig. 3  Clinopyroxene compositions in 351 Unit 1 of ASB basalt (green circles) compared with 351 Unit IV (blue triangles) recording the nascent IBM arc  35  immediately overlying the ASB basalt, mid-ocean ridge basalt and gabbro (MORB/G), 352 FAB and boninite  36  , Oman ophiolite  37  and backarcs (Data from PetDatabase website).
In situ major element compositions of minerals were obtained, both, using a Cameca SX-100 electron probe with four spectrometers at the Research School of Earth Sciences (RSES) in Australian National University (ANU), Canberra, Australian, and using JEOL-8200 electron probe at Guangzhou Institute of Geochemistry (GIG), Chinese Academy of Sciences (CAS). The operating conditions were: 15 kV of accelerating voltage, 20 nA of probe current, and 1 μm of the diameter of the electron beam. The counting times at the peaks were 10 s for Si, 20 s for Al and Mg, 30 s for Ti, Ca, Na and K, 40 s for Fe and Mn, and 60 s for Cr and P. Na, Mg, Al, Si and P were determined using Kα line obtained with a TAP crystal, then PET crystal for K, Ca, and Na, and LLIF crystal for Ni, Fe, Mn and Cr. Natural minerals were used as standards, and all data were corrected with a ZAF program.
Acknowledgements Our research is based on samples and data initially provided by the  International Ocean Discovery Program. We thank the  JOIDES Resolution crew, IODP  staff and shipboard scientific colleagues for their sustained efforts in accomplishing all of the primary goals of Expedition 351. H. L. and W. D. S. acknowledge funding from the  National Natural Science Foundation of China  ( 91958110 ), the  Strategic Priority Research Program (B) of the Chinese Academy of Sciences  ( XDB42020203 ) and  NSFC 41473029. Funding was provided by the  Australian Research Council via ANZIC  through a grant to R. J. A.
Data availability The authors declare that the data supporting the findings of this study are available within its supplementary tables in a Source Data file deposited at https://doi. org/10. 6084/ m9. figshare. 13353074. v2. Backarc clinopyroxene and spinel data for comparison are downloaded from http://www. earthchem. org/petdb. Replicate analyses of trace elements of reference standard BCR-2G are compared to the GeoReM preferred values for each element, and recommended values are from http://georem. mpch-mainz. gwdg. de/. Figure  1a  is made with GeoMapApp (www. geomapapp. org). Source data are provided with this paper.
Population Since 2015, a neuroradiologic data base (Commission Nationale de l'Informatique et des Libertés 1724786; https://www. cnil. fr/en/ home) includes, prospectively, all patients admitted to our comprehensive stroke center (CSC). The patients were managed directly in the CSC (mothership patients) or first admitted to 1 of the 4 primary stroke centers with or without IV rtPA before transfer for MT (patients experiencing drip and ship). These primary stroke centers are a distant 50-200 km from our CSC. Three hundred forty patients admitted to our stroke unit from January 2017 to December 2018 were studied. In this retrospective cohort study, patients were included if they fulfilled the 4 following inclusion criteria: 1) 18 years of age or older, 2) preadmission mRS of #2, 3) symptom onset or last time seen well to CSC admission of ,12 hours, and 4) anterior circulation occlusion (middle cerebral artery M1 or M2 segment, internal carotid artery, or tandem occlusion) visible on MR imaging at admission.
Scores and Parameters Evaluation Clinical and Imaging Evaluation. Stroke severity was assessed by the NIHSS on CSC admission by a stroke neurologist. The following data were collected prospectively with a structured questionnaire: age, sex, cardiovascular risk factors (hypertension, dyslipidemia, diabetes mellitus, and smoking), time of symptom onset, NIHSS at CSC admission and at 24 hours, vital signs before treatment, imaging findings, use of IV rtPA, and clinical outcomes. All patients underwent multimodal 1. 5T (Aera; Siemens) or 3T (Skyra; Siemens) MR imaging before treatment, with a standardized protocol. Infarct volume was estimated in milliliters on DWI using RApid processing of PerfusIon and Diffusion (RAPID; iSchemaView). 10 he ASPECTS on DWI was calculated by a neuroradiologist blinded to the results of the MT.
Statistical Analysis Continuous variables were reported as median (interquartile range [IQR]). Univariate statistical analysis was performed using the Mann-Whitney U test for continuous variables and the Fisher exact test for categoric variables. All statistical analyses were performed using MedCalc, Version 18. 10 (MedCalc Software). Patients in daytime-and nighttime-hour groups were used in a logistic regression as independent predictors of the follow-up outcome, defined as good (mRS # 2) or bad (mRS. 2) outcome. Variables either known to be potential confounding factors or identified as the most significantly different in the univariate analysis were included in the logistic regression. Regarding our sample size, we chose to limit the number of covariables to 8. A stepwise method was used with an a-to-enter and a-to-exit set at.
2 and. 001, respectively. Eight variables, age, sex, NIHSS score at CSC admission, IV rtPA, dyslipidemia, atrial fibrillation, hypertension, and CSC admission to groin puncture were thus included in the model. Finally, specificities of mothership patients and those in the drip and ship group were investigated using a subgroup analysis. For an optimal comparability, a backward method was used to compare patients in daytime and nighttime hours among mothership patients as well as those the drip and ship group on the basis of the 8 previous variables. Data were adjusted by the NIHSS score at CSC admission, atrial fibrillation, IV rtPA, and CSC admission-to-groin puncture delay for mothership group and by the NIHSS score at CSC admission, dyslipidemia, and hypertension for patients in the drip and ship group.
For zinc DRC on GluN1/GluN2B receptors, no zinc buffer was used and zinc concentrations were corrected for an estimated contaminant zinc concentration of 100 nM (ref. 80 ). For the pH and spermine sensitivity experiments, an external solution enriched in HEPES was used to insure proper pH buffering  68. Unless otherwise mentioned, NMDAR-mediated currents were induced by simultaneously applications of saturating concentrations of glutamate and glycine (100 μM each). Glutamate and glycine dose-response curves (DRC) were performed in the presence of 100 µM glycine and 100 µM glutamate, respectively. Recordings were performed at a holding potential of -60 mV and at room temperature. Data were collected and analyzed using Clampex 10. 5 and Clampfit 10. 5 (Molecular Devices), respectively, and fitted using KaleidaGraph 4.
p-azido-L-phenylalanine (AzF) was purchased from Chem-Impex International (Wood Dale, IL, USA) and prepared as 10 mM stock solutions in Barth solution with sonication. 4-Benzoyl-L-phenylalanine (BzF; also known as Bpa) was purchased from Bachem (Budendorf, Switzerland) and prepared as 260 mM stock solutions in ddH 2 0 by equal molar addition of NaOH. Structural analysis, and molecular modeling and fitting. Except for Fig. 7 , all structural illustrations of full-length NMDARs are snapshots from a modeled structure of the GluN1/GluN2B receptor captured in an inhibited state as described  47. The structural analysis presented in Fig. 6  was performed using a large structural dataset including most published full-length X-ray and cryo-EM NMDAR structures (Supplementary Table  1 ; all GluN2A structural states classified and named according to Ref.
Hence, rolling (A-B) = (Distance center-UL_A to center-UL_B) -(Distance center-LL_A to center LL_B). The distance between the LBD intra-dimer ULs (D1-D1 interface) corresponds to the mean distance between the centers of mass of the LBD ULs of subunits A-D and B-C. For iMODFit simulations, the structural model of the fulllength GluN1/GluN2A receptor (CTD excluded) was generated using Modeler, combining available structural data and a "loop-model" reconstruction module to build missing loops and linkers. The model of the 2KS state (PDB 6MMR) was then fitted into the density of the splayed-open state (EM-9152) using iMoDfit  71  and following the procedure described in Ref. 47. The collective variables used to analyze the iMODfit trajectories are the following: GluN1 and GluN2 NTD interlobe twist angle, defined as in Ref.