dataesr/scientific-paragraphs-categorization huggingface dataset:
δ 18 O seasonal amplitude High-resolution δ 18 O data allow us to explore seasonal variations. This includes 18 ice core records with sub-annual resolution, four IAEA/GNIP monthly precipitation datasets, and four daily precipitation monitoring records. In order to quantify post-deposition effects in ice cores, we calculated the ratio of the first three seasonal amplitudes by Clim. Past, 14, 923-946, 2018  www. clim-past. net/14/923/2018/ For the time-averaged values, the comparison consists of calculating the model-data differences. Red "+" symbols indicate a positive model-data difference, while blue "-" symbols correspond to a negative model-data difference. For the inter-annual standard deviations, the comparison consists of calculating the ratio of the simulated value to the corresponding grid cell data.
We have calculated the mean of the δ 18 O annual amplitude (i. e. maximum-minimum values within each year) in ice core records (triangles in Fig. 7a ) and the mean seasonal ampli- tude of precipitation time series (circles in Fig. 7a ) for comparison with ECHAM5-wiso outputs (Fig. 7 , Table  4 ). Unfortunately, a too-small number of measurements (19 daily measurements) were monitored at DDU, preventing the representation of the full seasonal cycle. The data depict the largest seasonal amplitude in the central Antarctic Plateau, reaching up to 25. 9 ‰ at Dome F. ECHAM5-wiso underestimates the seasonal amplitude (by 14 to 69 %) when compared to precipitation data, but overestimates the seasonal amplitude when compared to ice core data (from 11 to 71 %). The overestimation when comparing with ice core data is consistent with the attenuation of signal by post-deposition effects (as previously mentioned) rather than a model bias.
January); a secondary maximum is also sometimes observed and simulated in late winter (August-September). Data from Marsh station show maxima in January, April, and August, whereas the model only produces a single summer maximum value. In summary, we report no systematic bias of the seasonal temperature amplitude (Fig. 8a ). The seasonal pattern for the temperature is similar compared to δ 18 O, with minima in winter and the largest model-data mismatch in winter. Secondary minima or maxima cannot be discussed with confidence, as they have low amplitudes. We also highlight that model-data offsets are larger in winter. Note that precipitation and d seasonal cycles are described in Sect. 3. 2. 4.
The δD-δ 18 O relationship and d patterns The δ 18 O-δD linear relationship is expected to be affected by different kinetic fractionation processes, for instance those associated with changes in evaporation conditions. We first compare the δ 18 O-δD linear relationship in the available precipitation and ice core data and simulated by ECHAM5-wiso (Table  6 ). Significant correlation is observed for all observational datasets but Marsh, as expected from meteoric samples, assuming correct preservation of samples and accurate isotopic measurements. We stress that the smallest correlation coefficient is identified at Vernadsky (r = 0. 96), suggesting potential artefacts for this record. In the observations, the δD-δ 18 O slope varies across regions. While slopes higher than for the global meteoric waterline (i.
e. > 8 ‰ ‰ -1 ) are identified at DDU and in Dronning Maud Land, lower slopes are identified in the Antarctic Peninsula (6. 6 to 7. 0 ‰ ‰ -1 ) and in the central East Antarctic Plateau (6. 5 and 6. 4 ‰ ‰ -1 at Dome C and Dome F, respectively). In the model, outputs also display significant linear relationships. They show higher values of the slope than observed in the Antarctic Peninsula, at DDU, and at Dome F and lower than observed for the other regions, including Dome C. These results appear coherent with associated coastal versus inland temperature and isotopic distillation biases. Figure  6  compares the spatial patterns of the d timeaveraged model-data difference (characterised at 293 grid cells in our database; see Fig. 6a ), and the situation is contrasted with 50 % of positive and negative differences.
Regions Our study deserves to be expanded to other atmospheric models equipped with water stable isotopes and other nudged simulations using different reanalyses datasets to assess the robustness of our findings. Furthermore, obtaining more high-resolution ice core records is crucial to better assessing model skills for inter-annual variations. More measurements of precipitation, surface snow, and vapour monitoring for water isotopes would also help to better characterise deposition and post-deposition processes, their implication for model-data evaluation studies, and for an improved climatic interpretation of ice core records. Data availability. All data used in this paper are publicly available. Table  S1  in the Supplement resumes the type, location, covered period, and data citation of each record.
The isotopic timeaveraged values and standard deviations from precipitation, snow and firn-ice cores, and seasonal precipitation data (accumulation, temperature, and isotopic composition) were archived on the PAN-GAEA data library at https://doi. pangaea. de/10. 1594/PANGAEA. 891279  (Goursaud, 2018). Ice core data extracted from the Antarc-tica2k working group are available on the NOAA World Data Center for paleoclimatology (https://www. ncdc. noaa. gov/paleo-search/ study/22589,  Stenni et al. , 2018). The Supplement related to this article is available online at https://doi. org/10. 5194/cp-14-923-2018-supplement.
Structure of TFIID. Currently available structural information on the complete yeast TFIID complex is derived from lowresolution electron microscopy studies precluding docking of existing atomic models  26, 27. We developed a TFIID purification protocol from the yeast K. phaffii for structure determination by high-resolution cryo-EM. SDS-PAGE experiments (Fig. 1a ) and mass spectrometry analysis (Supplementary Data 1) confirm that the TFIID subunit composition of the phylogenetically closely related S. cerevisiae and K. phaffii are identical. To gain a better understanding of the subunit organization within TFIID, a crosslinking mass spectrometry (CXMS) analysis was performed using the homo-bifunctional, amine-reactive crosslinking reagent bis(sulfosuccinimidyl)suberate (BS3). A total of 391 unique intraprotein crosslinks (intralinks) and 488 unique inter-protein crosslinks (interlinks) were identified (Supplementary Data 2).
Samples were digested with trypsin, and the resulting peptides were fractionated by strong cation exchange (SCX) chromatography and analyzed by MS (Orbitrap Fusion). The crosslinked peptides were identified by searching the MS data against a database composed of K. p. TFIID subunit sequences using two different algorithms: pLink and in-house designed Nexus (available upon request) as described before  50. A 5% of false discovery rate (FDR) was used for both pLink and Nexus searches. The circular crosslinking map was generated using ProXL  51. Formation of promoter-bound complexes. The yeast S. cerevisiae TFIIA used for stabilization of TFIID-DNA complexes was recombinantly expressed in E. coli, purified from inclusion bodies and reconstituted as described earlier  52. Promoter DNA fragments were obtained by annealing of equimolar amounts of complementary oligonucleotides at a final concentration of 10 µM in 20 mM Tris-HCl; 2 mM MgCl 2 , 50 mM KCl by heating the mixture to 95 °C for 5 min and cooling slowly down to room temperature.
5% acrylamide gel containing 5% glycerol and 5 mM MgCl 2 in Tris-Glycine buffer. Gels were analysed using Typhoon FLA9500 imager (GE Healthcare Life Sciences). For the EM-studies 0. 4 µM TFIID was incubated with two-fold excess of TFIIA and 2. 5-3-fold excess of pGAP promoter DNA to have all TFIID molecules bound to DNA. Cryo-EM sample preparation and data acquisition. Freshly purified TFIID or assembled TFIID-TFIIA-DNA complexes were cross-linked with glutaraldehyde (final concentration 0. 1%) for 30 min on ice. After the cross-linking reaction was stopped, samples were dialyzed using VSWP MF-membrane Filters (Millipore) to remove sucrose. 3 µl of sample was applied onto a holey carbon grid (Quantifoil R2/2 and UltrAuFoil R1. 2/1. 3 300 mesh) rendered hydrophilic by a 30 s glowdischarge in air (2.
5 mA current at 1. 8 × 10-1 mbar). The grid was blotted for 2. 5 s (blot force 5) and flash-frozen in liquid ethane using Vitrobot Mark IV (FEI) at 4 °C and 95% humidity. Images were acquired on a Cs-corrected Titan Krios (FEI) microscope operating at 300 kV in nanoprobe mode using the serialEM software for automated data collection  53. Movie frames were collected in the case of holo-TFIID on a 4k × 4k Falcon 2 direct electron detector at a nominal magnification of 59,000 which yielded a pixel size of 1. 1 Å. Seven movie frames were recorded at a dose of 7 electrons per Å2 per frame corresponding to a total dose of 60 e/Å2. In the case of TFIID-TFIIA-DNA the movies were recorded on a 4k × 4k Gatan K2 summit direct electron detector in super-resolution mode at a nominal magnification of 105,000, which yielded a pixel size of 0.
55 Å. Forty movie frames were recorded at a dose of 1. 32 electrons per Å2 per frame corresponding to a total dose of 52. 8 e/Å2, but only the last 38 frames were kept for further processing. Initial reference generation. Grids containing frozen-hydrated TFIID sample were subjected to tomographic acquisition on a Cs-corrected Titan Krios (FEI) microscope operating at 300 kV in nanoprobe mode using the FEI Tomo software. Images were recorded on a Falcon 2 camera at a nominal magnification of 29,000, which resulted a pixel size of 3. 8 Å. Tomographic images were taken with a tilt from -60°to +60°with an increment of 1˚. Tomograms were reconstructed in IMOD and sub-tomograms containing single TFIID particles were extracted using the same software  54. Maximum-likelihood based sub-tomogram alignment and classification was performed in Xmipp.
Image processing. Movie frames were aligned, dose-weighted, binned by 2 and averaged using Motioncor2  55  to correct for beam-induced specimen motion and to account for radiation damage by applying an exposure-dependent filter. Nonweighted movie sums were used for Contrast Transfer Function (CTF) estimation with Gctf 56 program, while dose-weighted sums were used for all subsequent steps of image processing. After manual screening, images with poor CTF, particle aggregation or ice contamination were discarded. About 6,000 TFIID particles were picked manually using the e2boxer program of EMAN2  57  and subjected to 2D classification in Relion  58. Representative class average images showing TFIID in different orientations were then used as references for auto-picking with Gautomatch (http://www.
mrc-lmb. cam. ac. uk/kzhang/Gautomatch/) for both datasets. Several cycles of automatic picking followed by 2D and 3D classification were performed, yielding datasets of 155,620 particles for holo-TFIID. The same procedure was applied to the TFIID-TFIIA-DNA dataset along with random-phase classification (30) resulting in 180,823 particle images. These datasets were analyzed in Relion 1. 4 and Relion 2 according to standard protocols. The structures were refined using a low-pass filtered starting model obtained previously by tomography followed by sub-tomogram averaging. Global resolution estimates were determined using the FSC = 0. 143 criterion after a gold-standard refinement. Local resolution was estimated with ResMap  59. Three-dimensional classification of the entire dataset could not clearly separate distinct conformations of TFIID complex.
Acknowledgements We are grateful to  I. Berger  for providing the X-ray structure of the TAF5-6-9 before publication. This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Centre National pour la Recherche Scientifique (CNRS) , the  Association pour la Recherche sur le Cancer (ARC) , the  Ligue contre le Cancer , the  Fondation pour la Recherche Médicale (FRM) , the  ANR-15-CE11-0022-01  and the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. We acknowledge the support and the use of resources of the  French Infrastructure for Integrated Structural Biology FRISBI   ANR-10-INBS-05  and of Instruct-ERIC. This work was supported by  U.
Data availability The Mass spectrometry raw date were deposited to PRIDE with accession code PXD011092. The cryo-EM maps have been deposited in the 3D-EM database (www. ebi. ac. uk/pdbe) with accession codes EMD-0249, EMD-0250, EMD-0251, EMD-0253, EMD-0254 and EMD-0255. The model coordinates were deposited in the PDB database with accession code 6HQA.
g. ,  SPOT 4  and SPOT 5 in the past and, for example, LANDSAT-8, Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra, and Sentinel-2 today). These sensors use different spectral resolutions (between 4 and 10 bands in the VIS and NIR/SWIR), different spatial resolutions (with a ground spatial interval (GSI) of between 10 m and 1 km), and different revisit times of between 1 and 28 days. These multispectral datasets are currently used to retrieve the snow cover area (SCA) based on the optical properties of snow in the VIS and NIR/SWIR domains. However, the use of optical data from satellites is limited by cloud cover, which can mask over 50% of the pixels in winter  [2]. Directly related to the cloud cover issue, the revisit time is also an important factor to consider. The use of high spatial resolution satellites for snow cover mapping, such as SPOT 4 and SPOT 5 (28-day return time with a GSI of 20 m and 10 m, respectively), LANDSAT-8 (return time of 16 days at a GSI of 30 m), or even Sentinel-2 (return time of 5 days in constellation at 10-20 m), is limited by the revisit time.
This comparison takes advantage of the Let-it-Snow chain to use the recent Take-5 experiment (https://spot-take5. org) to assess the snow cover products. The Take 5 experiment was carried out as SPOT 4 and SPOT 5 approached the end of their life as a simulator of the future Sentinel-2 time series. By lowering their orbits, their revisit time was decreased to five days, producing data for 45 selected sites at a ground spatial resolution of 20 m (SPOT 4 Take 5) and 150 sites at 10 m (SPOT 5 Take 5). In addition to this, and in order to obtain a larger dataset for pertinent areas, the LIS product applied on LANDSAT-8 (http://tully. ups-tlse. fr/grizonnet/let-it-snow/blob/master/doc/tex/ATBD_CES-Neige. pdf) was also used as a reference. This led to the higher spatial and temporal resolution of snow maps available during this time period.
This large amount of data was selected to cover three different areas-the French Alps, the Pyrenees, and the Moroccan Atlas-which would allow the analysis to be performed using different land cover types, with particular focus on high mountains, low mountain ranges, and desert areas. A total of 181 dates, comprising 117 dates for the Alps, 30 dates for the Pyrenees, and 24 dates for Morocco, were selected to capture various snow cover scenarios. The paper is organized as follows. Section 2 presents the two main methods for retrieving SCFs from MODIS. Section 3 presents the areas that were studied and the reference datasets. Section 4 describes the methodology for the accuracy assessment, and Section 5 provides the corresponding results. A discussion of the main limitations of the different approaches can be found in Section 6.
This is especially important for elevated snow-covered surfaces in spring and summer. The general quality assessment flags are available in a new layer. Improvements made to avoid cloud/snow confusion were minor, as the updates for Collection 6 was more focused on improving snow detection in clear-sky conditions. All the specifications for the Collection 6 product can be found on the NSIDC website (https://nsidc. org/sites/nsidc. org/files/files/MODIS-snowuser-guide-C6. pdf) [19]. SCF data are no longer provided in the newer MODIS Collection 6 (hereafter C6). In order to be consistent with other products used in this study, the SCF was obtained using the linear regression method described in  [9]  and was applied to the NDSI map provided by C6 and only to pixels that had snow according to the C6 NDSI snow cover product.
MODImLAB [12] MODImLAB uses as its input the corrected reflectance after ATOPCOR correction  [12]  and after employing the fusion concept  [16, 17]  described in Section 2. 1. 2. The SU approach is applied to these data using an endmember library composed of eight endmembers derived from field measurements and manually selected MODIS reflectances. Four of these endmembers are associated with snow (classified as snow, snow and ice, ice only, and old snow), and the remaining four are associated with vegetation, rocks, pasture, and debris, corresponding to New Zealand land cover. All the pixels with an NDSI value lower than -0. 2 are considered snow-free. FCLSU is applied to each pixel that could contain snow within the whole set of eight endmembers. The final SCF of a given pixel is obtained from the sum of the abundances of the four endmembers associated with snow according to  [14].
Method for Reference SCF Map A large number of SC products with a high spatial resolution but low revisit time are available. This study used data from the SPOT 4 and SPOT 5 Take 5 experiments (see Section 1), with a revisit time of five days. SPOT 4 Take 5 surface reflectance data are produced at a spatial resolution of 20 m. SPOT 5 Take 5 has the same spectral characteristics, but a spatial resolution of 10 m. In order to expand the database, LANDSAT-8 data were also used, which have a spatial resolution of 30 m and a revisit time of 8-16 days. The LANDSAT-8 data covered the entire period of study in the Alps and Pyrenees, and these snow maps are therefore interleaved in the SPOT 4 and SPOT 5 datasets. The corresponding binary snow cover products used to calculate the reference SCF for MODIS pixels were obtained by post-processing the data produced using the Let-it-Snow processing chain (Let-it-Snow documentation can be found at http://tully.
Pre-Processing Following the workflow shown in Figure  2 , all products to be compared were re-projected to the same coordinate reference system for each area (Lambert 93 for the Alps and the Pyrenees, and UTM for Morocco). MOD10A1 data were re-projected from the sinusoidal tile to the convenient coordinate system using nearest neighbor interpolation. The grids were aligned and the spatial resolution of the binary reference map was then aggregated to 250 m to produce a reference SCF (Figure  2 ). All product pixels and reference maps were at a spatial resolution of 250 m and precisely co-registered. If a pixel used to produce a 250 m pixel was covered with clouds, this low-scale pixel would be considered cloudy and would not be taken into account in the comparison. The different cloud masks were fused so as to only compare results over cloud-free pixels as determined by each product.
Spatial Resolution The workflow in Figure  2  presents the data mainly considered for comparison, which is made at a spatial resolution of 250 m. However, MOD10A1 C5 and C6 are not available at this resolution. This can artificially introduce some biases to the comparison. In order to estimate the impact of the spatial resolution, one of the experiments aggregated the 250 m data to a coarser resolution of 500 m-the MOD10A1 native resolution-in order to perform the comparison.
Impact of Land Cover The total snow cover area (SCA; i. e. , the sum of the various snow fractions) for each land cover type (from the Corine Land Cover dataset) was computed and compared over the different dates in the Alps for the reference dataset and the different products. The following results are applicable to all the products (not shown here). On both natural grassland and bare rock, temporal variations in SCA were consistent. There were few differences, but the correlation coefficient was very high (≥0. 99), even though the difference between the two SCA values was significant, especially on bare rock. Conversely, on land containing coniferous forest or mixed forest, there were marked differences between the reference and the products (correlation coefficients of 0. 80 and 0. 87, respectively), especially for dates following snowfall.
Conclusions This paper presents a comparison of the two main approaches available for snow cover estimation (i. e. , NDSI linear regression and linear SU), looking at five products, three using the NDSI approach ( NDSI ATOPCOR and MOD10A1 C5 and C6) and two using SU (MODImLAB and LMM pure ). The assessment was carried out using high-resolution snow maps taken from the SPOT 4 and SPOT 5 Take 5 experiments and LANDSAT-8 data using the Let-it-Snow chain product. Areas of comparison were identified over the French Alps, the Pyrenees, and Morocco. Binary, fractional, and feature-based metrics were used to compare the different products with the reference maps. These results show the significant improvements of the newer MOD10A1 Collection 6 compared to the older Collection 5. However, both products were generally outperformed when using the same linear regression method but applied to atmospheric-and topographic-corrected reflectance instead of TOA reflectance.
Acknowledgments: This work has been partly supported by a grant from  Labex OSUG@2020  (Investissements d'avenir- ANR10 LABX56 ), from the  Programme National de Télédétection Spatiale (PNTS) , grant n. PNTS-2016-03 , the  ANRJCJC EBONI , the  CNES Tosca program , the  OPCC CLIM'PY Interreg Programme V-A Spain-France-Andorra  ( POCTEFA 2014-2020 ) and the  ARC 3 program of the Region Rhone Alpes. We would also like to acknowledge the  CNES  for providing the original SPOT 4 and SPOT 5 Take 5 data, and  Karl Rittger  for his pertinent remarks. and  Jean-Pierre Dedieu  provided advice on data analysis and pertinent analyze of the results. Marie Dumont ,  Mauro Dalla Mura  and  Jocelyn Chanussot  supervised  Théo Masson  in the framework of his PhD and helped to discuss the results. All authors reviewed the manuscript.
Cognitive, demographic, and clinical data from these 2,761 participants were included in our primary analyses. In addition, structured autopsies were performed on 1,410 decedents, and RNA-Seq data from the dorsolateral prefrontal cortex (DLPFC) were obtained from 507 of these. The SDS (ClinicalTrials. gov NCT01800214) is an observational cohort study of consecutively collected cases (1992-2014) from a tertiary care clinic in Toronto, Canada  [12]. For this study, we included participants clinically classified as having AD at their baseline assessment (see criteria below), and in whom Dementia Rating Scale (DRS) scores were available. Of 1,143 participants enrolled between 1992 and 2014, 285 had a primary clinical diagnosis of AD, of whom 271 had DRS scores at the time of baseline assessment.
Clinical, demographic, and psychometric data from these 271 participants were included in this study. The CNC cohort consists of patients who attended a clinical and research memory clinic specializing in the care of patients with cognitive disorders, the memory clinic at the Lariboisière Hospital, between July 2, 2008, and June 15, 2017, who had undergone CSF collection with measurement of amyloid and tau, whose entire clinical work-up was performed at the Lariboisière Hospital, and for whom a final clinical diagnosis was available. Of 2,298 patients assessed between July 2008 and June 2017, the vast majority had had a portion of their clinical work-up completed at an outside institution and were excluded from these analyses. Of the remaining 424 participants with a final clinical diagnosis, 321 also had a lumbar puncture with quantification of Aβ 40, Aβ 42, tau, and phospho-tau.