dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was funded by a research project financed by the  Swedish Research Council VR (Vetenskapsrådet)  on "Quantification of the bio-geophysical and biogeochemical forcings from anthropogenic de-forestation on regional  Holocene climate in Europe, LandClim II ". Financial support from the  Linnaeus University's Faculty of Health  and  Life Science  is acknowledged for  Marie-José Gaillard ,  Anna-Kari Trondman , and  Esther Githumbi. This is a contribution to the strategic research areas  MERGE  (ModElling the Regional and Global Earth system) and the  Past Global Change (PAGES)  project and its working group LandCover6k (http://pastglobalchanges. org/landcover6k), which in turn received support from the  Swiss National Science Foundation , the  Swiss Academy of Sciences , the  US National Science Foundation , and the  Chinese Academy of Sciences.
Data availability All data files reported in this work which were used for calculations, and figures are available for public download at https://doi. pangaea. de/10. 1594/PANGAEA. 937075?format=html#download (Fyfe,  Ralph M; Githumbi, Esther; Trondman, Anna-Kari; Mazier, Florence; Nielsen, Anne Birgitte; Poska, Anneli; Sugita, Shinya; Woodbridge, Jessie; LandClimII contributors; Gaillard, Marie-José (2021): A full Holocene record of transient gridded vegetation cover in Europe. PANGAEA, https://doi. org/10. 1594/PANGAEA. 931856). The data and the DOI number are subject to future updates and only refer to this version of the paper. The data available in Pangaea includes: 1) REVEALS reconstructions and their associated standard errors for the 25 time windows; 2) Metadata of the 1128 pollen records used; 3) LandClimII contributors listing the data contributors\collectors\databases.
2. 3 Model parameter setting For the purpose of this study, a new synthesis of the RPP values available for European plant taxa was performed in 2018-2019 based on the latest synthesis by Mazier et al. (2012) and additional RPP studies published since then (Appendix A-C). It provides new alternative RPP datasets for the whole of Europe, including or excluding plant taxa with dominant entomophily,with the important addition of plant taxa from the Mediterranean area (TableA1). The location of studies included in the RPP synthesis are shown in Fig. C1and related information is provided in TableC1. The selection of RPP studies, RPP values (shown in Appendix B, Tables.
Open Access Earth System Science Data have shown that inter-taxon variability in RPP values is generally lower than intra-taxon variability, lending support to application of the approach we used in the new synthesis of RPP in Europe (Appendix A-C), i. e. calculation of mean RPPs using all available RPP values that can be considered as reliable. Nevertheless, some RPP taxa still present a challenge, for example, Ericaceae, where Mediterranean tree forms have a greater number of inflorescences and hence may have a higher RPP than low-growth form Ericaceae in central and northern Europe. As we are using a single RPP dataset with the RPP of Ericaceae obtained in the Mediterranean region (more explanations below), the effect of higher pollen producing Ericaceae in the Mediterranean might result in https://doi.
During tank testing, motions of the model in each of the six degrees of freedom (DoF), i. e. , surge, sway, heave, roll, yaw, and pitch, are measured to determine how the platform responds to a range of wind and wave conditions. Generating this data allows validation of numerical models and characterisation of the dynamics of a full-scale platform. In short, model testing is often the first opportunity to carry out a simplified, but global, assessment of the moored platform and the turbine together  [1]. However, the effect that the facility used for the model tests will have on the outcomes is uncertain, even when following the same methodologies for carrying out the tests which is not often the case. Research carried out within MaRINET2  [2, 3]  identified a shortfall in the published guidance available for conducting laboratory testing of floating offshore wind turbines.
Much of the existing literature is derived form the oil and gas industry and published by the International Towing Tank Conference (ITTC) literature that deals with ocean engineering 2. To investigate the effect of the facility, a round robin testing program was executed in MaRINET2 whereby generic wave, floating wind, and tidal devices were tested in different infrastructures. The wave  [4] [5] [6]  and wind  [7]  round robin testing included many tests in waves. One of main lessons learnt from this exercise is that it is not straightforward to compare results of wave tests obtained from different basins. Linear response amplitude operators (RAOs) were found valuable for a first comparison  [7]. However, the impossibility of generating identical waves in two distinct basins combined with the fact that responses to waves are not fully linear bring an additional challenge for a cross-facility comparison.
Wind Round Robin Test Campaign The wind round robin campaign in MaRINET2 involved testing a 1/60th scale floating wind platform in four facilities around Europe: Ifremer, Centrale Nantes (ECN), University of Strathclyde (UoS), and two tests at University College Cork (UCC). The primary purpose of the second test at UCC was to test a second method of wind emulation a number of months after the original test. However, the original test plan with (and without) the thruster was also partly repeated. These latter results comprise the fifth dataset used in the present research paper. Each basin has unique design features with a range of water depths and sizes and different wave and wind generation capabilities. The platform was moored with a linear aerial mooring system, chosen as it is independent of water depth and could be installed in each of the facilities.
The model was fitted with four reflective markers for tracking the motions of the device using Qualisys, which was available in all facilities: one on each of the aft columns and two on the mast. Load cells were fitted to each of the mooring lines. The load cells and the mooring lines themselves were supplied by Ifremer and travelled with the device to each facility. Each facility supplied its own wave gauges. The round robin test plan was developed by Ifremer and included hydrostatic tests, mooring stiffness checks, decay tests in calm water (with and without moorings), test in regular waves without wind thrust, and tests in irregular waves with wind thrust. A full description of the model and the test campaign is presented by  [7]. For the remainder of this paper, the five datasets obtained from the four test facilities are referred to as A, B, C, D, and E in accordance with Table  1  and to ensure consistency with  [7].
1d, e ). However, GFP-SYP43 and VHAa1-mRFP were significantly segregated when observed by SCLIM (Fig. 1f ). A quantitative analysis based on the SCLIM data was carried out by calculating the correlation between GFP and RFP signal intensities per voxel on the TGN. The results confirmed that the extent of colocalization was low for SYP43 and VHAa1 (Fig. 1g ), indicating that TGN-localized proteins with different functions are not uniformly distributed in the same TGN. By comparing to the location of trans-Golgi cisternae marked with ST-iRFP (infra-red fluorescent protein-tagged trans-membrane domain of a rat sialyl transferase  35  ), we examined whether SYP43 and VHAa1 show any biased localization along the proximal-distal axis of the TGN, but could not observe a clear tendency (Supplementary Fig.
Signal intensity profiles of images were measured as previously reported with ImageJ 52. To visualize the TGN with a lipophilic dye, Arabidopsis root cells were treated with 2 μM FM4-64 (Thermo Fisher Scientific). The image of FM4-64 was taken after 6 min of uptake. Fluorescence from GFP, RFP, and FM4-64 was unmixed using linear unmixing algorithms of ZEISS ZEN software. Whole-mount immunolabelling of Arabidopsis roots with the CHC antibody was performed according to previous reports with minor modifications  58, 67. In brief, the seedlings 4 DAG were fixed with 4% paraformaldehyde in microtubule stabilizing buffer (MTSB: 50 mM PIPES, 5 mM EGTA, and 5 mM MgSO 4 ; pH 7. 0 with KOH) for 1 h at room temperature (RT) and then washed four times with MTSB followed by twice with distilled water. Roots of the seedlings were cut on a polyethylenimine-coated glass (Iwaki), dried at RT, and rehydrated with MTSB.
Acknowledgements This work was supported by  Grants-in-Aid for Scientific Research  from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan  (grant numbers:  25221103 ,  17H06420 , and  18H05275  to A. N. ;  18H04857  to  T. Uemura ;  17K07477  and  17H06475  to K. T. ), and by the  Asahi Glass Foundation  to T. Uemura. We thank  K. Schumacher  ( Heidelberg University, Germany ),  I. Hara-Nishimura  ( Konan University, Japan ),  T. Shimada  ( Kyoto University, Japan ),  S. Mano  ( National Institute for Basic Biology, Japan ), and  T. Demura  ( Nara Institute of Science and Technology, Japan ) for sharing materials. We also thank  Ichiro Terashima  ( The University of Tokyo ) for helpful discussions.
, 2018). The binary vector and the cloning method to insert two sgRNA were developed and described by Xing and coworkers  (Xing et al. , 2014). The chop chop software was used to design the sgRNA sequences targeting the EX1 coding sequence  (Montague et al. , 2014). The highest ranking sgRNA sequences were further analyzed with E-CRISP, and the two sequences with the highest efficacy score were chosen for the cloning  (Heigwer et al. , 2014). The abc1k1 mutant was complemented with 35S:ABC1K1-YFP-HA. The ABC1K1 coding sequence was synthetized directly in the pEarleyGate101 plant expression vector  (Earley et al. 2006 ) by GeneCust (Boynes, France) between the constitutive 35S promoter and the YFP-HA tag resulting in pEarlyGate101-ABC1K1. The pEarlyGate101-ABC1K1 plasmid was introduced by electroporation into the Agrobacterium tumefaciens C58 strain.
Real-time PCR analysis Total RNA was extracted from multiple 5-days old seedlings using the RNeasy plant mini kit (Qiagen, cat. no. 74904) and cDNAs were synthetized from 1 µg of total RNA using GoScript TM Reverse Transcriptase kit (Promega, cat. no. A5001). Quantitative RT-PCR reactions were performed using the LightCycler 480 SYBR Green I Master mix (Roche, cat. no. 04887352001) and a LightCycler ® 480 Instrument. The specific primers for each gene are listed in the supplementary table  S2. The gene expression level was quantified using the 2 -ΔΔCt method  (Livak and Schmittgen, 2001). The relative expression level of each gene was assessed using the Actin 2 gene (ACT2) as reference. All the reactions were performed in technical duplicates and the data shown come from two independent biological replicates.
Lipid profile analysis Extraction of total lipids was performed on pools of 5-days old seedlings for each genotype obtained from three independent experiments. In brief, after grinding, the lipids were extracted with 10 μl of tetrahydrofuran:methanol 50:50 (v/v) per mg of FW, the debris were separated by centrifugation (3 min, 14,000 g), and the supernatant transferred to an HPLC vial. 2. 5 μl of the sample were loaded on a reverse-phase Acquity BEH C18 column (50 × 2. 1 mm, 1. 7 μm) maintained at 60°C, and analysed by ultra-high pressure liquid chromatography coupled to atmospheric pressure chemical ionization-quadrupole time-of-flight mass spectrometry (UHPLC-APCI-QTOF-MS) as previously described  (Spicher et al. , 2016). Data were acquired using MassLynx version 4. 1  (Waters)  and further processed with TargetLynx  (Waters).
Acknowledgements This work was supported the  University of Neuchâtel  and  SNF  grants  31003A_176191  to FK and  31003A_179417  to FL. We thank  Veronique Douet  for her technical assistance. We thank the  Protein Analysis Facility, Center for Integrative Genomics, Faculty of Biology and Medecine, University of Lausanne, Switzerland  for the proteomic analysis.
The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near real time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016-2017. We find a good agreement between both datasets with an accuracy of 94% (proportion of correct classifications) and kappa of 0. 80. More accurate (+6% kappa) retrievals are obtained by excluding low quality pixels at the cost of a reduced coverage (-13% data).
The products are computed using MAJA (atmospheric correction and cloud detection) and LIS (snow detection and snow fraction calculation) software  (Hagolle et al. , 2015; Gascoin et al. , 2019). The performance of the snow detection with this processing pipeline was previously evaluated over the French Alps and Pyrenees using snow depth records at 120 stations from the Météo-France database  (Gascoin et al. , 2019). The accuracy (proportion of correct classifications) was 94 % (κ = 0. 83), with a higher false negative rate than the false positive rate. However, this evaluation was spatially limited to 10 Sentinel-2 tiles in France (a tile is 110 km by 110 km), whereas the HRSI products cover 1054 Sentinel-2 tiles over 39 countries in Europe. Any operational snow cover detection algorithm applied to optical multispectral imagery is challenged by spectral similarities between clouds and the snow cover  (Stillinger et al.
In situ data The evaluation database was prepared by merging two datasets of in situ snow depth (height of snow, HS) measurements. First, we extracted daily snow depth measurements of 1094 SYNOP data (WMO automatic weather station) covering 36 countries. Then, we selected daily data from a recent compilation of snow depth measurements in the Alps  (Matiu et al. , 2021). The latter dataset consisted of 670 stations located in France, Italy and Germany. The evaluation period spans a hydrological year from 1 Sep 2017 to 31 Aug 2018. This period was chosen to take advantage of the 5-days revisit periodicity reached by the Sentinel-2 mission in Sep 2017 and because the Alps dataset is smaller after 2018. All values were rounded to the nearest centimeter. We combined all these data sources into a single dataset totaling 26933 data points of daily snow depth measurements distributed across 36 countries in Europe (Fig.
1 ). A data point was classified as snow covered if HS was strictly greater than a threshold HS0. We tested the sensitivity of this threshold by calculating the confusion matrix between the FSC products and the reference dataset for 1 cm increments of HS0 from 0 to 10 cm  (Klein and Barnett, 2003; Gascoin et al. , 2015 Gascoin et al. , , 2019)).
Snow product We used the on-ground fractional snow cover (FSCOG) layer but the analysis would be identical with the top-of-canopy layer (FSCTOC) as the canopy adjustment does not change the snow classification (HR-S&I consortium, 2020a). Pixels with value of 205 (cloud or cloud shadow) and 255 (no data) were set to "no data". A pixel was classified as snow if 0<FSC≤100 and no-snow if FSC=0. We matched each point of the reference dataset with the nearest pixel of an overlapping FSC product that was acquired on the same day, resulting in a maximal distance of 10√2 m between the pixel center and the station. If there were more than one matching FSC product on the same day, we selected one whose nearest pixel was neither cloud nor no data. We also assessed the impact of the quality layer on the performance.
Stratification data We stratified the analysis using four external variables: tree cover density, land cover type, elevation and country of measurement. The tree cover density (TCD) was obtained from Copernicus Land Monitoring Service. It was derived using Sentinel-2 data too and is available at 20 m resolution with pixel values ranging from 0 to 100%. We used the 2015 product and partitioned the data into 10 segments of equal TCD range. The land cover was obtained from the Copernicus Global Land Service version 3  (Buchhorn et al. , 2020). We used the 2018 discrete classification map where a pixel's label is the majority label from the fractional cover map. The classes were regrouped into the following labels: closed or open forest, herbaceous vegetation or wetland, urban, water bodies, snow and ice, shrubs, moss and lichen, bare and sparse vegetation, cropland, and open sea.
Results Figure  2  shows the evaluation of the snow/no-snow detection with in-situ data, and in particular the variation of the kappa coefficient with the HS0 threshold and corresponding confusion matrices. It indicates a good overall agreement between both datasets with an accuracy of 94% and κ = 0. 80 at HS0 = 0. The kappa coefficient increases to 0. 84 if low quality retrievals are excluded. The optimal HS0 is equal to 1 cm in both cases and used for the analysis with the stratification data. The false negative rate is higher than the false positive rate (precision is 93% but recall is 78%). The exclusion of low quality data reduces the total amount of available data points by 13% and increases the recall (82%) more than the precision (94%), meaning that more false negative errors are avoided.
Conclusion This brief communication reports on the performance of the HRSI snow classification based on a year of in situ snow depth data. Although the in situ dataset is unbalanced with about four times more no-snow values than snow values, it is sufficiently large to have thousands of observations in the two categories. It is also well distributed across Europe, as we obtained hundreds of observations in many subcategories (country, land cover, elevation, and tree cover density). This dataset therefore allows drawing more robust conclusions than previously on the performance of the MAJA-LIS algorithm to detect the snow cover. We conclude that Sentinel-2-derived HRSI snow products are sufficiently reliable to study snow cover variations across the variety of European landscapes from the northernmost Arctic regions to the southern semiarid mountains, excluding the densest forest regions.
Acknowledgements This work was funded by the  European Environment Agency. We acknowledge the  Centre National d'Etudes Spatiales  in particular  N. Picot  and the  High Performance Computer team. We also thank  M. Matiu  for his comments on the manuscript. M. D. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  949516 , IVORI).
Characterization Surface topography was visualized by using scanning electron microscopy (SEM) of type "Nova NanoSEM" equipped with a field emission gun. The images were taken at 15 kV with Everhart-Thornley detector (ETD), at different magnitudes. Atomic force microscopy (AFM) of type "Bruker Dimension ICON" was used for characterization in 2D and 3D. Images were taken in SCANASYST-AIR imaging mode, by silicon tip on nitride lever, at different scales and at a resolution of 1024 lines per scan. This microscope has a local probe allowing to visualize the surface topography of a sample with a resolution of about 1 nm. Topological investigations and roughness calculation were performed by using NanoScope Analysis software.
Micro-CT Imaging One adult brain and one S32 embryo were removed from fixative by washing several times with PB saline for 2 days, dehydrated in increasing concentrations of methanol, and stained with 1% phosphotungstic acid in methanol for 4 days before micro-CT scanning with a Bruker Skyscan 1172 microtomograph (Bruker, Kontich, Belgium) located at the Marine Biology Station of the University of Santiago de Compostela in A Graña. The obtained images were digitally reconstructed with NRecon software (Bruker, Kontich, Belgium) and processed with CTAnalyzer software (Bruker, Kontich, Belgium). For displaying data, DataViewer software (Bruker, Kontich, Belgium) was used. Serial images of the three axis and 3D reconstructed images of each sample were obtained.
ACKNOWLEDGMENTS The authors would like to thank the farmers and veterinarians for their participation in the study;  Concetta Scalfaro  ( ISS ) for her help in MLVA analysis; and  Valérie Rose  ( ANSES, Ploufragan Laboratory ) and  François-Xavier Briand  ( ANSES, Ploufragan Laboratory ) for their help and expertise in the sequencing PCR products for MLVA analysis. Figure 3 was created with  BioRender. com.
Statistical analysis Main outcome: CS-AKI CS-AKI was defined according to the stage ≥1 of the Kidney Disease Improving Global Outcome (KDIGO) guidelines: within 2 days, increase in pCr of ≥26. 5 μmol/ L (≥0. 3 mg/dL) and/or ≥ 1. 5 x baseline pCr and/or urine volume < 0. 5 mL/kg/h for 6 h  [4]. Stage ≥2 CS-AKI was defined as a doubling of baseline pCr and/or urine output < 0. 5 mL/kg/h for ≥12 h  [4]. Pre-CPB pCr was baseline pCr. All pCr available measurements within the 48 postoperative hours were analyzed for the CS-AKI outcome and the worst change in pCr was considered. Urine output was assessed hourly in the ICU and every 2 to 6 h in the ward (from Day1, if permitted by the patient's condition). Among relevant clinical and biological data (Table  1 ), the need for RRT during the hospital stay was collected.
Last, among patients developing CS-AKI, we assessed the ability of the biomarkers measured at H6 or earlier to discriminate between persistent and transient CS-AKI. Only for this very specific analysis, the KDIGO stage of CS-AKI determined from H48 to H60 were compared to that determined before H48. Transient CS-AKI was defined as a decrease in the KDIGO stage over time. Otherwise, CS-AKI was considered persistent  [15]. Between-groups comparisons relied on chi-squared and Mann-Whitney tests. A p < 0. 05 was considered significant. Analysis was performed on anonymous data with MedCalc™15. 8 (MedCalc Software bv, Ostend, Belgium). No data imputation was performed. This manuscript is in accordance with the STARD statements for the reporting of studies of diagnostic accuracy (Supplemental Table  1 )  [16].
Acknowledgements The authors are grateful to  Mrs. Laurence Pacaud  and  Mrs. Laurence Larmet  (research nurses of the  Department of Anaesthesia & Critical Care Medicine of Laënnec University Hospital, Nantes, France ), for their important contribution to this work. We also acknowledge the  laboratory staff of Laënnec University Hospital , the Anaesthesia nurses and the medical and nurse staff of the  department of Anaesthesia and Critical Care Medicine of Laënnec University Hospital.
Radar Data Processing The description of Doppler processing of our radar data is described in e. g. , ref. [44]  and briefed below for reader convenience. Raw records contain the digitized demodulated in-phase, I, and quadrature, Q, components of VV-and HH-polarization radar signal sampled at 40 kHz rate. Instantaneous Doppler spectra, S(ν, t), were computed over a 0. 2-s running window, where the width is chosen as a tradeoff between noise and temporal resolution. Subseuently, the instantaneous NRCS, σ(t) = S(ν, t)dν, is the running signal variance, < I(t) 2 + Q(t) 2 >, which is proportional to the received power multiplied by a calibration factor. The Doppler velocity of a target, v D , is determined directly from the instantaneous Doppler frequency, v D = f D λ r /2, obtained from instantaneous spectra, as, f D (t) = S(ν, t)νdν/σ(t), where λ r = 0.