dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  Canada Research Chair in Quantitative Magnetic Resonance Imaging  (J. C. -A. ), the  Canadian Institute of Health Research  ( CIHR FDN-143263 ), the  Canada Foundation for Innovation (32454) , the  Fonds de Recherche du Quebec -Sante(28826) , the  Fonds de Recherche du Quebec -Nature et Technologies  ( 2015-PR-182754 ), the  Natural Sciences and Engineering Research Council of Canada  ( 435897-2013 ), the  Quebec BioImaging Network  and the  Association Francaise contre les Myopathies , the  Institut pour la Recherche sur la Moelle epiniere et l'Encephale , and the " Investissements d'avenir"  ( ANR-10-IAIHU-06 ).
A rather complete list of the available software can be found at: http://www. esrf. eu/Instrumentation/ software/data-analysis/Links/xafs. Typical widely used computer programs are GNXAS  [33] , FEFF  [34, 35]  and EXCURV  [36]. EXCURV is a program, which simulates EXAFS spectra using rapid curved-wave theory. GNXAS package is based on multiple-scattering (MS) calculations and a fitting procedure of the raw experimental data, also allowing multiple edge fittings and a non-Gaussian distribution models for the atoms pair distribution. FEFF allows MS calculations of both EXAFS and XANES spectra for atomic clusters. The code yields scattering amplitudes and phases used in many modern XAFS analysis codes. It is also linked to the IFEFFIT package  [37, 38] , a suite of interactive code for XAFS analysis, combining high-quality and well-tested XAFS analysis algorithms, tools for general data manipulation and graphical display of data.
Two more considerations should be made on EXAFS data analysis. The first is that XAS (and therefore the results obtained by an EXAFS analysis) is a bulk technique and thus all the atoms irradiated by the beam contribute to the overall XAS spectrum. The same is true in the case of a multicomponent system (for instance two phases in equilibrium of a polymorphic species). Each component or phase gives its contributions. An example to disclose the simple component of a species, such as in the case of gold nanoparticles and its precursors, appeared  [39]. Alternatively, an efficient use of chemometry has been proposed for the analysis of XAS data in such cases  [40]. This approach has interesting implication for the interpretation of spectra recorded during an operando acquisition and an example will be presented in the next section.
5. ThechemometricapproachtotheinterpretationofXASdata Due to the increasing performance of many synchrotron beamlines specialized in in situ XAS studies, extremely large dataset containing many tens or hundreds of spectra associates to a single experiment are currently collected. This huge amount of data is calling for a suitable strategy for their treatment in reasonable time. For instance, the study of the charge or the discharge process of a battery produces something like 100-300 spectra, depending on the experimental conditions (data acquisition protocol and battery discharge rate). In similar cases, the use of chemometrics may be applied  [87]. Specifically, the application of multivariate curve resolution (MCR) to large datasets of in situ XAS experiments (where the samples undergo continuous evolution during the reaction path) allows one to interpret their modification in terms of sums of pure spectra with variable concentration profiles, without needing any preexisting model or a priori information about the system.
Science Objectives The ACS scientific objectives reflect the overall TGO mission philosophy and address the specific investigations defined in  Zurek et al. (2011). These scientific objectives have driven the ACS development through the flow down of measurement requirements to their implementation in the instrument concept definition and later throughout its fabrication (see Table  1 ). The scientific objectives of ACS can be broadly separated into three main categories: (1) first and foremost, the instrument is intended to provide "Breakthrough" science by contributing to the establishment of the most precise detection or upper limit values for a list of trace gases that could be potentially found in the Martian atmosphere, (2) ACS shall also contribute in a unique way to a so-called "Novel" science since it will be able to deliver a self-consistent dataset enabling the study of the quasi-complete atmospheric chemistry cycle controlled by the HO x radicals as well as the study of H 2 O and its isotope HDO in their gaseous phase, addressing here the crucial topic of the fate on geological timescales of water on Mars, then (3) comes the "Climatological" science focus of ACS with the continuous delivery of key climatic parameters to constrain the environmental background in which the species targeted by the "Breakthrough" and "Novel" science objectives are observed.
Its surface is additionally polished and coated with copper (Fig. 7b ). Compared with the heritage design of RUSALKA, NIR features a higher slit in combination with a 2D array detector (instead of a linear detector on RUSALKA), which captures the dispersed light along the full dimension of the slit. We adapted for space the infrared camera module XSW-640 from Xenics (Belgium) based on a thermo-electrically cooled (Peltier element) InGaAs array of 640 × 512 pixels (Fig. 7c ). This detector features a pixel size of 20 × 20 µm 2 , and a spectral range defined by the InGaAs sensitivity domain between 0. 4 and 1. 7 µm. The slit is imaged onto the detector with a ratio of 1:1, so only a part of the detector's height (4 mm or ∼200 out of 512 pixel lines) is illuminated. The main specification of the RUSALKA-SOIR design is that the AOTF spectral response function (SRF) width must be aligned with the free spectral range of the echelle spectrometer.
Measurement Modes The Fourier spectrometer subsystems allow for numerous settings and parameters, however few optimal combinations were selected and verified during calibrations. This parameter set is implemented in the embedded software of TIRVIM. The interferometer pendulum can swing within its full range, resulting in a MOPD = 5 cm. Alternatively, a reduced swing with MOPD = 0. 8-1 cm can be employed for measuring weaker signals. The full swing is devoted to Sun observations, and the reduced one is devoted to nadir observations. Also, the interferometer mirrors can move with a slower or faster motion. The slow mode results in a reference channel frequency of ∼10 kHz, and the fast one in ∼80 kHz. With the change of the interferometer speed, the bandpass of the main tract is modified accordingly.
Solar Occultations For solar occultation the detection limits and quantification of known molecules is based on the latest knowledge of calibration for each ACS channel: 1. For ACS-NIR, λ/ λ = 25,000 and SNR = 4000 for an integration time of 1 ms and with 32 accumulations. 2. For ACS-MIR λ/ λ = 50,000 and SNR = 2500. This is the SNR for all secondary grating positions for an integration time of 3 ms and for 32 accumulations. 3. For ACS-TIRVIM the estimated detection limits are based on SNR = 50 in the range of 3-10 µm and spectral resolution of ν = 0. 13 cm -1 (∼1 nm). The detection limits were estimated using look-up-tables built for spherical geometry in the range of 0. 7-1. 7 (NIR), 2. 3-10 µm (MIR+TIRVIM) obtained with a line-byline radiative transfer model. The model was based on the HITRAN 2012 spectroscopic database  (Rothman et al.
2013) , using broadening coefficients in CO 2 from Lavrentieva et al. (2014, 2015)  for H 2 O and HDO, and  Sung and Varanasi (2005)  for CO. For CO 2 the self-broadening was applied. To model the Martian atmosphere, we used an average temperature-pressure profile extracted from the Martian Climate Database  (MCD 5. 2; Millour et al. 2015) , corresponding to the equatorial region for a surface pressure of 6. 1 mbar (b) Zoom on a selected region: single and average measured spectra compared to a model solar spectrum  (Fiorenza and Formisano 2005)  (so-called "zero level" on Mars). The final transmittance has been calculated for a set of altitudes from 10 to 80 km for minor species, and from 10 to 160 km for the abundant molecules, CO 2 , CO, and H 2 O. The sensitivity in solar occultation critically depends on the dust loading.
Planning Each of the three channels can generate a wealth of information, and the total number of measurements will be limited by the available downlink volume. Nevertheless, very flexible instrument settings allow matching main science goals and downlink possibilities. All instruments have onboard averaging: with NIR it is possible to sample from 1 to 10 wavelength ranges, during nadir, NIR and TIRVIM can operate intermittently (with a duty factor), MIR allows for reducing the data volume by windowing the detector. For example it is possible to downlink not the whole MIR frame of 640 by 512 pixels, which corresponds to ∼200 nm wavelength interval (see  Figs. 19, 20) , but only a stripe with a single diffraction order, e. g. 640 by 10 pixels (a factor of 50 less data), with a spectrum length of ∼20 nm.
This would allow studying a specific species within a narrow spectral band and to make use of larger number of occultations for its denser mapping even with a low data budget. It must be noted that the increase of accumulation results in a better signal to noise ratio and allows for data budget economy, but degrades the vertical resolution on the limb. The nominal data allocation for ACS is 1. 6 Gbit/day, however the overall mission budget will variable (preliminary estimated as a factor of 0. 2-5) with Earth-Mars distances along the year because of different downlink speed. Use of two Russian deep space antennas is agreed and will increase initial allocations. Final numbers, and the sharing between the TGO instruments are yet not figured. Also, the number of ACS occultations per day will be defined by sharing policy with NOMAD, since the co-alignment between instruments is not reached (see Sect.
30 ). Some measurements may be concentrated around a specific location to study short time-scale phenomena and/or local time variations. Joint or close time/place observations with other TGO instruments (NOMAD and CaSSIS) will be planned as well as aligned observations with other missions, Mars Express, MRO, MAVEN. The data sharing within the ACS channels will be a subject of discussion and prioritizing at each mission stage. Table  6  presents a possible measurement plan for one day (12 orbits, 24 occultations) with nominal data allocation. Here we consider only 12 occultations since NOMAD instrument requires separate pointing and thus might use the other 12 occurrences. TIRVIM observations in the science orbit will include not only occultations and nadir observations of Mars, both day and night sides, but also absolute calibration (twice per day) by pointing its scanner to open space or towards the internal black body.
Acknowledgements ExoMars is a space mission by  ESA  and  Roscosmos. The development and fabrication of ACS was funded by  Roscosmos  with contributions from  LATMOS (France)  funded by  CNES. Science operations are funded by  Roscosmos  and  ESA. AAF, NII , DAB, JLB, DSB, ML acknowledge the grant # 14. W03. 31. 0017   of Ministry for Education and Science of Russian Federation  for science support of the ACS experiment. AT, AVS, and BEM acknowledge support from  Russian Science Foundation  (grant  RSF 16-12-10453 ), which enabled assessment of measurement characteristics of the instrument and the associated modeling. LMZ acknowledges RSF funding (grant  RSF   16-42-01103 ). Other authors affiliated with IKI acknowledge FANO, contracts  0120. 06 02993 (0028-2014-0004 ) and  0120. 0303422 (0028-2014-0007 ).
Results and Discussion Bklc characterization. Bklc had previously been described as a tetratricopeptide repeat (TPR)-containing protein with homology to eukaryotic kinesin light chain proteins  10. TPR repeats are ubiquitously present in nature and play a major role in scaffolding and protein-protein interactions  12. The entire central region of Bklc (residues 30-238) displays significant sequence identity (24%) with the experimental structure of human kinesin light chain HKLC2 (PDB code 3ceq), indicating that the central core of Bklc is an all alpha-helical protein with 5 central TPRs (10 helices, Fig. 1A, B ). The additional N-terminal (residues 1-29) and C-terminal (239-256) segments of Bklc also have a high alpha-helix propensity (Fig. 1A ) and might correspond to the flanking helices that are thought to have a "capping" or solubilizing function in TPRs  12.
The analysis of the hydrophobic and polar properties of these helices shows an amphipathic character for the N-terminal helix, with a hydrophobic and a polar face (with positive mean net charge, Fig. 1B, C ). Homologues of Bklc are found in all domains of life. This is in contrast with Btubs for which all detectable homologues out of a high similarity zone (Prosthecobacter genus) are from eukaryotic organisms. More sensitive profile-based searches confirmed this tendency and we were able to collect a diverse set of distant homologues against non-redundant databases. Although the promiscuity and ubiquity of TPR repeats in nature preclude extracting a clear evolutionary path for the origin of Bklc, some consistent results are noteworthy. First, eukaryotic homologues are found in simple unicellular eukaryotes (including Ostreococcus tauri, a marine green alga and the smallest free-living eukaryote) as well as higher eukaryotes.
2A ). As a further control we tested for the reversibility of the binding (to exclude protein precipitation) by depolymerizing the Btub filaments present in the pellet and recovering the soluble fraction after depolymerization (Supernatant 2 (S2); Fig. 2B ). Bklc as well as the Btubs were recovered in the soluble fraction and consecutive co-polymerization cycles could be carried without losing Bklc. Visual inspection of the SDS PAGE bands from the sedimentation assay suggests sub-stoichiometric binding of Bklc to Btub polymers. Considering the technical difficulties to accurately measure polymer-associated stoichiometry in solution, we used the ImageJ software for densitometric quantification of gel band intensities of Btub and Bklc. A total of 10 μ M of BtubA/B was used in all the gels analyzed where BtubA and B were present in stoichiometric amounts as shown by Sontag and colleagues  8.
Ultracentrifugation experiments were performed in 25 mM Tris-HCl buffer (pH 7. 5) containing 150 mM NaCl. The following parameters were calculated with Sednterp software and used for the results analysis: partial specific volumes 0. 729 ml. g -1 for Bklc, 0. 979 ml. g -1 for lipid, viscosity 0. 01023 poise, and density 1. 0053 g. ml -1. Sedimentation velocity data analysis was performed by continuous size distribution analysis c(s) using Sedfit 15. 01 software  22. Prosthecobacter cultures. Prosthecobacter vanneervenii strain 12252 was purchased from DSMZ (Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, www. dsmz. de) and grown in liquid MMB medium as described in DSMZ recipe 628 (https://www. dsmz. de/microorganisms/ medium/pdf/DSMZ_Medium628. pdf). Cultures were grown at 26 °C in an orbital shaker and reached a maximal optical density OD 600nm = 0.
Densitometric quantification of gel bands. The SDS-PAGE gels to be analyzed were scanned and loaded into ImageJ software (Rasband, W. S. , ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http:// imagej. nih. gov/ij/, 1997-2016). The Images were converted to 32 bit grayscale images and rectangles were drawn around the area of the lanes to analyze and the band intensity was plotted per lane. The peaks corresponding to the band intensities were quantified and the ratios of Btub:Bklc were calculated on an average of 3 independent gels from three independent experiments. Small unilamellar vesicle preparation. Small unilamellar vesicles (SUVs) were prepared by reverse phase evaporation  24  from a chloroform solution made of an 8:2 mixture of 1-palmitoyl-2-oleoyl phosphatidylcholine (POPC) and 1-palmitoyl-2-oleoylglycero-3-phosphoglycerol (POPG) to a final concentration of 10 mM lipids.
The excess of solution was manually blotted using a Whatman filter paper and the grid was plunge-frozen in liquid ethane using a Leica EM-CPC. Electron microscopy imaging and data processing. Data acquisition was performed on a 200 kV field emission gun JEOL 2200FS electron microscope equipped with a Gatan US1000 slow scan CCD camera and an in-column energy-filter. Zero-loss (slit: 20 eV) high magnification images were acquired at pixel sizes between 7 and 2 Å at the specimen level with nominal defocus between 1 and 4 μ m depending on the experiment. Tilt-series dataset were collected under zero-loss conditions (slit: 25 eV) at a pixel size of 7 Å at the specimen level with nominal defocus varying from 5 to 10 μ m depending on the experiment using an in-house developed software. For data processing of the Btub fibers Tilt-series were aligned and reconstructed using TomoJ v2.
32  26. The alignments were performed using the local minima algorithm  27  and reconstruction was generated using OS-SART-GPU (iterations: 100, relaxation coefficient: 0. 01, volume update: every 4 images). The periodicity of the helical filament repeats was calculated on the electron densities of the 3D reconstructed filaments that were semi-automatically segmented with Amira which corresponds to threshold-based method. The computation was performed in Matlab using a non-linear least-squares regression algorithm nlinfit  28. The animations showing the filament densities presented in the Supplementary Data have been generated with Amira. Bioinformatic analysis. TPR predictions were done using the TPRpred program  29  and the secondary structure and closest structural homologues were inferred from Phyre 2  30.
The pdb entry 3CEQ of the Human kinesin light chain 2 was used to obtain the Bklc model with highest confidence over the largest sequence coverage with highest sequence identity and the model in Fig. 1B  was done using Pymol (http://www. pymol. org). The amphipathic nature and hydrophobic characteristics of the N-terminal helix were predicted using the HELIquest software  31. Blast 32 searches using Bklc from Prosthecobacter vanneervenii (Uniprot id: A8Y5U5_9BACT) as query were performed over several databases, including: Uniprot, non-redundant and IMG complete genomes and metagenomic databases. For all searches, an inclusion e-value threshold of 10 -10 was used. Blast outputs showed a high rate of false positives, due in part to sequence divergence but also to variable number of TPR motifs associated with diverse protein functions.
Acknowledgements We would like to thank  Jose Manuel Andreu  and  Martin Pilhofer  for providing the plasmids used in this work and  Patricia Duchambon  for her assistance and access to equipment. We are grateful to  Martin Pilhofer  and  Pedro Alzari  for critical reading of the manuscript and discussions. We also thank  Bruno Baron ,  Sebastien Brulé ,  Patrick England  from the  Molecular Biophysics facility (PFBM) at the Institut Pasteur (Paris)  for  AUC  and CD measurements and discussions, as well as the  PICT-IBiSA  for providing access to the chemical imaging facility. This work was supported by the  Institut Curie , the  Institut Pasteur , the  CNRS, INSERM, DIM MALINF  and the  French National Research Agency ,  ANR  ( HetTub 12-PDOC-0021-01 ;  ANR-10-LBX-0038 ,  ANR-10-IDEX-0001-02 PSL ).
In the following sections, we briefly describe the REVEALS method, the KK10 and HYDE models and their scenarios, present the methodology we used to first evaluate REVEALS for the present day based on remotely-sensed land cover data and then evaluate the KK10 and HYDE reconstructions of land cover in northern and central Europe over four periods during the Holocene. A major aim of this comparison between data and models is to identify areas and periods of agreement and disagreement between the pollen-based REVEALS reconstructions and the ALCC scenarios and to provide an overall recommendation as to which ALCC model appears to be more realistic.
Materials and Methods We synthesized pollen data from northwestern Europe into a land cover reconstruction and compared it to ALCC scenarios at five time windows over the middle and late Holocene. These time windows were selected to represent contrasting land use conditions at key periods during the Holocene for which it has been hypothesized that ALCC had an influence on climate  [33]. The temporal range of the time windows was a function of the overall number of fossil pollen samples available in the source dataset that were included in the window  [40] ; the REVEALS methodology requires a large number of samples to produce a robust reconstruction  [37]. The most recent window was used for calibration of the REVEALS reconstruction and covers the period from the youngest sample in each sediment core, i.
e. , the core top, to 100 calibrated years before present (cal. yr. BP, i. e. , years before AD 1950; hereafter BP). We evaluate the ALCC scenarios for four preindustrial time windows: 6200-5700 BP (4250-3750 BC), 3200-2700 BP (1250-750 BC), 700-350 BP (AD 1250-1600) and 350-100 BP (AD 1600-1850). To further ensure that enough fossil pollen samples were present in each time window, the REVEALS reconstructions were performed on a 1 • geographic grid. We aggregated the ALCC scenarios to the same grid resolution as the REVEALS reconstructions and filtered these datasets to include only those grid cells where the pollen data were present. The driving variables, processes and output of the ALCC and REVEALS methods are summarized in Figure  1.
The REVEALS Pollen-Based Land Cover Reconstructions To perform the REVEALS reconstruction, we aggregated pollen counts from all samples in a corresponding time window for each pollen record. Samples came from sediments collected in lakes of a range of sizes and from peatlands; see  [40]  for details on the selection of the pollen records. The number of pollen records available for the reconstruction varied depending on the 1 • grid and time window. The REVEALS program estimates the mean regional vegetation composition of each of 25 taxa with its standard error in each grid cell for each time window whenever data are available. Pollen productivity estimates (PPEs) and fall speeds of pollen (FSP) used in the REVEALS runs are found in  [47]. The grid-based REVEALS taxon estimates are then classified and converted to the estimates for 10 plant functional types (PFTs), hereafter referred to as the "grid-based" REVEALS PFT estimate (GB REVEALS PFT).
Comparison between REVEALS and the ALCC Scenarios To perform an evaluation of the quality of the REVEALS method, we compared the first REVEALS time window (0-100 BP) with the mean of the HYDE scenario over the same period. The original KK10 scenario extends to AD 1850, and for studies covering the most recent period, it has been merged with HYDE  [5] , so KK10 was not used in the evaluation of the modern time window. For the four periods covering the rest of the late Holocene, we compared both the KK10 and HYDE datasets with the REVEALS reconstructions. Comparison of the ALCC scenarios with the REVEALS reconstructions required a transformation of the spatial and temporal resolution of the ALCC model output. While the ALCC scenarios were available at 5 arc-minute resolution, the REVEALS reconstructions were prepared for 1 • grid cells where sufficient pollen data were available.