dataesr/scientific-paragraphs-categorization huggingface dataset:
For 3x-FLAG peptide eluted material, in-solution trypsin digests were performed by incubating samples with 1 g of sequence-grade trypsin (Promega) at 37 • C in 1× TBS followed by lyophilization until dry. Digested peptides were passed over ZipTips (Millipore) and eluted with 0. 1% TFA/50% acetonitrile. Peptides were analyzed by an Orbitrap LTQ mass spectrometer (Thermo Scientific) with result searches performed using the program MASCOT (Matrix Science). To identify protein interactors, peptide counts for sample versus control were submitted to the Crapome web server  (28). Proteins identified that had a fold change greater than two, and at least two peptides detected, were considered to be enriched.
Gene ontology & network analysis For gene ontology analysis, lists of interacting proteins were submitted to the DAVID (Database for Annotation, Visualization and Integrated Discovery version 6. 8) web server  (29). Gene ontology terms with a P-value < 0. 05 were then summarized using the REVIGO (reduce and visualize gene ontologies) (30) server using a similarity cutoff of 0. 46. Network visualizations were generated using Cytoscape software with protein-interaction data from the APID (Agile Protein Interactomes DataServer) database  (31, 32).
NMR spectroscopy and chemical shift assignment NMR spectra were recorded at 298 K using a Bruker Avance III 700 MHz or 800 MHz spectrometers equipped with a triple resonance gradient standard probe or cryoprobe, respectively. Topspin versions 2. 1, 3. 2 and 3. 5 (Bruker BioSpin) were used for data collection. Spectra processing used NMRPipe  (35)  followed by analysis with Sparky 3 (T. D. Goddard and D. G. Kneller, University of California) or NMRviewJ 8. 0 (One Moon Scientific). Spectra for the assignment of backbone 1 H N , 1 H ␣ , 13 C', 13 C ␣ , 13 C ␤ and 15 N H nuclei of full-length FKBP25 were collected on a 200 M 13 C, 15 N-labeled sample in 20 mM sodium phosphate (pH 6. 5) with 150 mM NaCl, and 10% D 2 O added for the lock. NMR spectra include 2D 15 N-HSQC, 3D HNCO, 3D HNCACO, 3D HNCA.
N relaxation Amide 15 N T1 and T2 relaxation data were acquired at 700 MHz and 298 K by measuring a series of 2D 15 N-HSQC spectra on 300 M full-length 15 N-FKBP25 with variable delays  (38). The relaxation delays for T1 spectra were 10, 40, 80, 140,250, 400, 600, 800, 100, 1200 and 1500 ms, with repeat measurements at 10, 140, 250, 600 and 1000 ms for error estimation. Resulting delays for the T2 spectra were 12, 24, 36, 49, 61, 73, 85 and 97 ms, with repeat measurements at 12 and 73 ms for error estimation. T2 measurements were also performed on 100 M 15 N-FKBP25 bound to 100 M dsRNA-23bp using the same parameters. Steady-state heteronuclear { 1 H} 15 N-NOE spectra were recorded with and without 3 s of 1 H saturation and represents an average of two separate samples. Relaxation rates and error calculations were determined using NMRViewJ  (39).
The location of residues from the BTHB with annotated crosspeaks are indicated (top panel; atomic coordinates from PDB ID 2KFV)  (23). Annotations with a small letter n correspond to the asparagine sidechain amide crosspeaks. Dotted boxes indicate peaks that broaden below detection upon addition of ligand. Full spectra are in Supplementary Figure  S4. domain. We based this ligand on the stem portion of the b2NRE ligand, and replaced the loop with two G-C basepairs. This ligand (dsRNA-8) was bound by the same residues sensitive to addition of the parent b2NRE stemloop, as well as the longer dsRNA-23 bp (Figure  5  and Supplementary Figure  S5 ). Specificity is also retained in the 8 bp ligand since the DNA version, dsDNA-8, did not result in any noticeable interaction. We then tested BTHB binding with the single-stranded oligonucleotides ssRNA-8 and ssDNA-8, neither of which showed signs of interaction.
The GO enrichments categories of chromatin regulation, DNA repair and some cytoskeletal processes in our Bio-ID data provide some direction for these future investigations. While there are several reports of cyclophilin and parvulin isomerases being linked to mRNA splicing (62), FKBP25 is so far the only FKBP family member that has demonstrated direct binding to RNA, and is the only prolyl isomerase shown to bind dsRNA. Interestingly, two cyclophilin prolyl isomerases are able to bind ssRNA due to an auxiliary RNA recognition motif (RRM) domain. The RRM domain of the nuclear human CYP33/PPIE directly binds to single-stranded AU-rich RNA sequences  (63, 64) , such as mRNA, but does not bind to rRNA or tRNA  (65). An overlapping interaction surface on the RRM domain also recognizes the third plant homeodomain (PHD3) of mixed lineage leukemia (MLL) protein, and the binding of MLL is thought to displace RNA  (63).
ACKNOWLEDGEMENTS We thank  Brune Vialet  for synthesis of the RNA oligonucleotides. Naoto Yamaguchi  and  Philippe Bouvet  kindly provided plasmids. Access to NMR spectrometers, equipment and technical assistance were possible thanks to  Axelle Grélard ,  Estelle Morvan  and the structural biology platform at the  Institut Européen de Chimie et Biologie  ( UMS 3033 ). NMR access was partially supported by the  Centre Nationale de la Recherche Scientifique [IR-RMN-THC Fr3050]. European e-Infrastructure  projects are acknowledged for the use of their web portals supported by  FP7 WeNMR  [project#  261572 ]; and  H2020 West-Life  [project#  675858 ]. FUNDING  French Canada Research Fund  [C. J. N. and C. D. M. ];  Fondation ARC for Cancer Research   [PDF20111204271  to S. K. U. ];  Canadian Breast Cancer Foundation BC/Yukon Branch  [to C.
e. 5-25 mm) and the mid-wave infrared (MIR i. e. 2-5 mm), and under different geometries (nadir or limb observation viewing). Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/jqsrt Because each of these spectral regions and geometries has its advantages and disadvantages, the possibilities to combine several types of measurements in a synergistic way have been studied in order to better exploit the data available in the near future, in particular to assess the near-surface processes. The challenge is to better capture CO and CH 4 information as close as possible to the associated sources, for improving the understanding, quantification or monitoring of sources and sinks of molecular species in the Martian atmosphere. CO is a non-condensable gas and its seasonal behaviour can be used to constrain the expected behaviour of other non-condensable gases.
The CO seasonal-spatial distribution on Mars is in fact very similar to that of argon, the reference non-condensable gas on Mars  [24, 25]. Strong deviations of the (to be measured) methane distribution from the one of relatively passive tracers would represent additional indications for the presence of active local sources of CH 4. Indeed, the Martian atmosphere contains several non-condensable gases, such as Ar, N 2 and CO. All these gases are expected to show similar behaviour: seasonal accumulation in polar regions during winter; dilution and transport to low latitudes in late winter and spring; depletion below average value during summer at some latitudes; hydrostatic instability and vertical mixing. A synthetic dataset of spectra was created for various scenarios. Different parameters were chosen to get a statistical sample of spectra representative of the expected measurements.
Database of spectra The purpose of this study is to investigate the improvement induced by considering the complementarity of different instruments and the analysis is based on simulated spectra. However, in order to investigate realistic payload configurations and because the ExoMars Trace Gas Orbiter (EMTGO) mission is the latest mission toward Mars embarking spectroscopic instruments, we have considered two instruments whose characteristics are very close to those of the real instruments on board the EMTGO mission, i. e. NOMAD and ACS. In this section, we will describe the radiative transfer modular program used to simulate the spectra and to retrieve the trace species abundances. The two instruments of interest will be described and selected scenarios will be presented.
SO spectra were simulated at 14 different tangent altitudes above the surface height: þ 1 km; þ 3 km; from þ 5 km up to þ 25 km by step of 5 km, from þ 27. 5 km up to þ 40 km by step of 2. 5 km and one last at þ 45 km. Two different abundance levels for CO and CH 4 have been defined: low and high. The CO VMR were defined using the PFS measured values at the selected location and constant over the whole altitude range  [13]. The values of CO VMR are 321 ppmv in the case of low VMR and of 1362 ppmv in the case of high VMR. The two different VMRs of CH 4 used were 10 and 60 ppbv, corresponding to values reported in the literature  [18, 19, 50] , the last one being possibly unrealistic according to the Curiosity measurements  [51]. These values have been used to create a database of simulated spectra.
Atmospheric states The BIRA-IASB three-dimensional general circulation model (GCM) was used to provide data representative of typical atmospheric conditions of the neutral atmosphere of Mars. This includes fields such as temperature, pressure, winds and dust, as well as surface variables (e. g. temperature, pressure, CO 2 ice or water ice surface). The GEM-Mars GCM is able to reproduce the basic state of the atmosphere including the general circulation dynamics, temperature and seasonal cycles of pressure, CO 2 , H 2 O and O 3. The description and evaluation of the GCM can be found in  [49]. In this study, global annual profiles from the surface up to 130 km were considered for temperature, pressure, CO 2 , H 2 O and O 3. GEM-Mars has also the capability to provide vertical profiles of dust extinction.
Simulations with ASIMUT-ALVL All simulations of the spectra have been performed using the ASIMUT-ALVL radiative code developed at BIRA-IASB  [52]. Initially developed for Earth observation missions (IASI and ACE-FTS), the code was later adapted for planetary atmospheres, in particular for Venus  [44]  and Mars  [53]. ASIMUT-ALVL is a modular program for radiative transfer calculations in planetary atmospheres. This code has been developed with the objective to be as general as possible, accepting different instrument types (Fourier transform spectrometers, grating spectrometers, AOTF combined with an echelle grating) and different geometries (nadir, ground-based, solar occultation/limb). The different radiation contributions such as the Sun contribution (direct or reflected on the surface), the surface emission contribution and the thermal atmospheric emission contribution are taken into account.
More details about this algorithm can be found on the website of BIRA-IASB (http://planetary. aeronomie. be/en/asimut_documentation/html/index. html). In the following paper, the expressions "pass" and "FEN" will be often used. The passes represent the different steps during the retrieval process. FEN is the acronym for "fenêtre" which means "window" in French. The FEN represent the different spectral windows used to perform the retrieval. To obtain a statistically representative sample, random noise figures have been added to the 36 (28 þ 8) simulated spectra. Sets of 50 spectra were created in nadir and 10 in solar occultation for each of the scenarios. This led to a dataset of 680 (28 Â 10 þ8 Â 50) spectra for each instrument considered. Examples of these spectra are shown in Fig. 1  and Fig.
Retrievals ASIMUT-ALVL has been used to perform the retrievals as well. The main retrieval module is based on the optimal estimation method (OEM)  [54]  coupled to the analytical calculation of the Jacobians. This code allows to fit simultaneously or sequentially different parts of one or more spectra, to fit the surface temperature, T surf , to fit column/vertical profiles for molecular species and for aerosols and to fully characterize the outputs (averaging kernels, errors, degree of freedom, etc. ).
Methodology Retrievals have been performed on 680 spectra simulated as described in the previous sections. In order to test the impact of the synergies, 4 different types of runs have been executed: In non-synergy: ○ Retrievals from "GA only" spectra ○ Retrievals from "FTS only" spectra In synergy: ○ Level 1/ Level 1 (L1/ L1) retrievals combining FTS and GA spectra in the same retrieval ○ Level 2/Level 1 (L2/L1) retrievals combining sequentially the retrieved information from one instrument with the spectra from the other one. Here, L1 data represent calibrated spectra recorded by one of the instruments, while L2 data are physical quantities retrieved from one spectrum or one set of spectra (temperature, VMR profiles, etc. ). It is important to define precisely how the synergies occur. To perform a L1/L1 synergy, ASIMUT-ALVL will consider the spectra from both instruments simultaneously and retrieve the molecular abundances from both of them considered together, i.
e. information will be extracted from the two sets of spectra during the same retrieval procedure. In the case of the L2/L1, the choice was made to use the FTS spectra in a first pass to retrieve the CO 2 and H 2 O VMR profiles, as well as the surface temperature (T surf ) in nadir taking advantage of the broad spectral range offered by this instrument. In a second pass, the data obtained during the first pass will be used as input and CO and CH 4 VMR profiles will then be retrieved from the GA spectrometer spectra only, taking advantage of the higher spectral resolution and SNR of this latter instrument. The runs have been performed starting from spectra simulated with the same a priori profiles (considered as the truth) as described in the next section. Depending on the geometry, the first guess profile was chosen randomly for solar occultation and fixed for nadir observations.
Conclusion The goal of this theoretical study was to determine the impact of synergies between different instruments on board the same platform orbiting around Mars. This work has been undertaken with a particular focus on the ExoMars TGO 2016 mission, accommodating two IR instruments that might operate in a complementary way. A database of synthetic spectra was created taking into account the expected characteristics of two representative instruments, based on the known specifications of the EMTGO instruments available at the time of this study. The systematic retrievals that have been performed show a positive impact of synergies in the quantification of CO while no noticeable improvement is expected in the detection/quantification of CH 4. These results can be explained based on spectroscopic and instrument related arguments.
The assumption used in this study is excluding this additional uncertainty. The instruments are in Martian orbit by now and more specific cases could be considered, especially taking into account the effect of aerosols or non-constant vertical profiles. More sophisticated inversions would lead to more time-consuming simulations and more complicated retrievals. However, without a better knowledge of the actual performances of the two instruments (particularly once the final orbit around Mars is reached) the expected results would most likely demonstrate the usefulness of synergistic retrievals from real NOMAD and ACS spectra (both spectrally and geometrically). The advantages presented here are theoretical and real data might lead to different results. The Monte Carlo approach chosen in this work was a way to convince us of the validity and robustness of our implementation of the optimal estimation method for a multi-instrument, multi-geometry and multi-spectral inversion of Martian spectra.
Acknowledgements This work was funded by the  SIROCCO  project under ESA contract number  4000107088  (with NOVELTIS as prime contractor). The study was proposed and supervised by  A. G. Straume-Lindner  and  O. Witasse. The research was performed as part of the " Interuniversity Attraction Poles" program  financed by the  Belgian government (Planet TOPERS)  and a  BRAIN  research grant  BR/143/ A2/SCOOP. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the Grant agreement no. 607177 CrossDrive and from the  European Union 's  Horizon 2020 Programme  ( H2020-Compet-08-2014 ) under grant agreement  UPWARDS-633127.
Underfill threshold was set to 2% for triggering of precursor for MS2. Dynamic exclusion was activated for 30 s. Mass spectrometric raw data were processed and analyzed using Proteome Discoverer 2. 1 (Thermo Scientific) with Mascot search algorithm against mouse SwissProt database. Perseus 1. 5. 6. 0 was used for further data analysis  57. IPA software (QIAGEN Redwood City) was used for network and pathway analysis. In-gel digestion of protein bands and mass spectrometry. SDS-PAGE protein bands were excised and simultaneously reduced and alkylated with 10 mM TCEP and 40 mM 2-choloracetamide for 1 h at room temperature. Proteins were digested with 13 ng/μl of sequencing grade trypsin (Promega) overnight at 37 °C and peptides desalted using in-house made microC18 columns (3 M empore). Peptides were resuspended in 0.
0 m/z quadrupole isolation width). All raw data were analyzed with MaxQuant v1. 5. 3. 25  58  and searched against the human UniProt database with default settings including phosphorylation of S, T, and Y as a variable modification and match between runs enabled. Nucleotide measurements. Adenine nucleotides from HEK293T perchlorate extracts were measured by LC-MS on an ABISCIEX 5500 QTRAP mass spectrometer  16. AEC was calculated from ratios of [AMP], [ADP], and [ATP] (Equation  1 ): AEC ¼ ATP ½ þ 0:5 ADP ½ ð Þ ATP ½ þ ADP ½ þ AMP ½ ð1Þ Statistical analysis. The data are presented as mean values ± s. e. m. of at least three independent experiments. The unpaired two-tailed Student's t-test was used for all comparisons unless stated. Data availability. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  59  partner repository with the data set identifier PXD006365.
Acknowledgements This work was supported by grants from the  Australian Research Council (ARC)  and the  National Health and Medical Research Council (NHMRC). G. R. S. is a Canada Research Chair in Metabolism and Obesity and the J. Bruce Duncan Chair in Metabolic Diseases. B. E. K. is an  NHMRC  Research Fellow. J. S. O. is an  ARC   Future Fellow. Supported in part by the  Victorian Government's Operational Infrastructure Support Program.
We use this formation because its base is the only mapped surface effectively preserved from erosion since deposition. We use a digitized geological map and an ASTER DEM (70 m resolution) to extrapolate the basal surface of the Lannemezan Formation using ArcGIS software and we estimate its current slope at 0. 5 •. Figure  9a  shows area and slope data for the Lannemezan megafan compared to data compilations from active and inactive alluvial fans and megafans from the Alps, the Andes and the Himalayas  (Horton and DeCelles, 2002; Guzzetti et al. , 1997). The discrepancy of the Lannemezan t=300 ky t=500 ky data with the scaling law suggests an estimated ∼ 0. 4 • tilt, which is simulated by uplift increasing linearly from 0 at the northern boundary to 2 mm yr -1 at the southern boundary of the model.
(B) Scaling relationship between drainage area and fan slope for alluvial systems of the Alps; data from  Guzzetti et al. (1997; crosses) ,  Crosta and Frattini (2004; triangles)  and  Champagnac et al. (2008;  black circle = Valensole, grey square = Chambaran). The Lannemezan megafan-Neste system (red circle) lies slightly out of the relation (fit: S f = 10. 4 A b (-0. 51±0. 05) ). Quaternary tilt (vertical arrow) of the Lannemezan megafan surface is estimated at 0. 13 • , the difference between present-day slope and slope predicted by the power-law fit. Modified after  Champagnac et al. (2008 Champagnac et al. ( ). al. , 2014;; Abrahami, 2015)  but compares well to older systems (e. g. Campanian-Maastrichtian Hams Fork formation in Utah; DeCelles and Cavazza, 1999) and is consistent with the Lannemezan megafan building phase encompassing the early middle Miocene to Pliocene (i.
Acknowledgements. This study was supported by  French National Research Agency ANR  ( Project   PYRAMID ,  ANR-11-BS56-0031 ) and forms part of  Margaux Mouchené 's PhD thesis funded by the  French Ministry of Higher Education (MESR). ISTerre is part of  Labex OSUG@2020  ( ANR10 LABX56 ). We thank  Hugh Sinclair  and an anonymous referee for constructive comments that helped clarify the paper. Edited by: S. Castelltort Reviewed by:  H. Sinclair  and one anonymous referee.
FP was financed by the French National Institute for Agricultural Research (INRA) (100%). JEP were financed by the Ministry of Education and Research (100%). The funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Ole Alvseike, Lars Erik Gangsei and Ellen-Margrethe Hovland are employed in Animalia-Norwegian Meat and Poultry Research Center. Animalia is a Norwegian non-profit organization mainly financed by the national purchase tax, supervised by the Agricultural Marketing Board, and organized by the Norwegian Meat cooperative.
predicted values. For responses with significant overall effect post hoc multiple comparison analyses were conducted by testing contrasts, defined prior to the analysis, using Scheffe's method, which accounts for the multiple testing within each response variable. To assess the adequacy of the number of animals included in the study, minimum effect sizes were calculated for the load of intestinal lesions that were required to reveal significant differences between two sample means. Calculations were based on the observed standard deviations of the parameters (log-transformed), various levels of power (20%, 50% and 80%), and the significance level of 5%. Body weight and food intake were analyzed by two-way ANOVA, using diet and gender as independent factors. Box plots indicate the median, and the 10 th , 25 th , 75 th and 90 th percentiles as vertical boxes with error bars, and dots indicate data points.
Acknowledgments We like to thank  Frøydis Bjerke ,  Andre Christian Backer  and  Per Håkon Bjørnstad  from  Animalia (Oslo, Norway) ,  Anne G. Tofteberg ,  Silje Skavhellen ,  Daniel E. Osen , and  Trine Thorkildsen  for the help during the production of the experimental diets at the pilot facility. We also like to thank  Jens Petter Wold  and  Frank Lundby  from  Nofima (Ås, Norway ) for the LF-NMR analysis of salmon, and  Nathalie Naud  from the  University of Toulouse (France)  for the analyses of fecal water.
(2006) ;  Akhmetzhanov et al. (2011 Akhmetzhanov et al. ( , 2012)). This work was supported in part by the project FunFit ANR-13-BSV7-0011 † Corresponding author. E-mail: ivanyegorov@gmail. com 1 BIOCORE project team, INRIA Sophia Antipolis, 2004, route des Lucioles -BP 93, 06902 Sophia Antipolis Cedex, France In this paper, one-season dynamics of a within-host cohort of spore-producing biotrophic fungi is considered. Their within-host multiplication and outer transmission are implemented by inner growth and free-living (spore) forms, respectively. For many plant pathogenic fungi exploiting leaf tissues as hosts, within-host multiplication relies on mycelial growth within foliar lesions, while transmission is achieved by producing and releasing asexual spores. We assume that there is no conversion between these two forms.