dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022; Edgar et al. , 2020; Fedo et al. , 2022; Stack et al. , 2019). The Murray formation has been interpreted to be deposited within a fluvial/lacustrine environment, and the thickness of the deposits indicates these wet conditions must have persisted for at least 10 6 years  (Edgar et al. , 2020). Analysis of orbital visible to near-infrared reflectance spectroscopic data revealed the presence of hematite, hydrated silica, phyllosilicates, and sulfates throughout the Murray formation  (Fraeman et al. , 2016; Milliken et al. , 2010). Only one member of the Murray formation outcrops within GT: the Jura member, which is also exposed in VRR and suggest these sediments were syndepositional. The Jura member is dominated by fine-grained rocks (primarily mudstones), with rarer instances of coarser-grained rocks.
Convective grain motion becomes especially critical when investigating clay mineral-rich samples, like those in the GT region, as these tend to aggregate and maintain a set orientation. On Earth, this preferred orientation of clay grains can be alleviated with additional sample preparation techniques (e. g. , oriented grain mounts as well as methods ensuring random orientation). However, these techniques are not yet possible on Mars. Therefore, the CheMin setup provides a sophisticated alternate approach for XRD of clay-rich samples on a planetary body. During a CheMin analysis, photons are diffracted from mineral lattice planes, generating a 2-dimensional diffraction pattern on the energy-sensitive CCD detector. Diffraction and fluorescence data are processed onboard every ∼30 min of analysis, which is referred to as a minor frame.
, 2019; McAdam et al. , 2022). Thus, the definitive presence of Fe-carbonate abundances above CheMin's detection limit is unique to GT. While Fe-carbonate might be expected as a secondary product in lacustrine sedimentary rocks that contain Fe-rich primary igneous minerals  (Bristow et al. , 2021) , the detection of a 9. 2 Å phase is novel and identifying this phase has been a challenge. Bristow et al. (2021)  laid out geochemical, mineralogical, and sedimentological criteria to help constrain the identification of this phase and detailed an exhaustive search of the American Mineralogist Crystal Structure Database. With more data in hand, we build on previous work by  Bristow et al. (2021)  and detail why the well-crystalline minerals (i. e. , jahnsite, aluminocoquimbite, rhomboclase, inesite, and willhendersonite) returned from this search are poor candidates for the phase with a 9.
Australia Telescope Compact Array We observed Abell 3266 with the ATCA between 2020 October and 2021 October, for a total of approximately 66 h (project ID C3389, P. I. Riseley). Observations were performed using the Compact Array Broad-band Backend (CABB;  Wilson et al. 2011 )    Four complementary array configurations were used, with baselines between 30 m and 6 km in length, providing sensitivity to a broad range of angular scales and a nominal resolution of around 6 arcsec. We co v ered Abell 3266 using a mosaic of four pointings, spaced at 50 per cent of the primary beam (PB) full width at halfmaximum (FWHM), in order to ensure that the entire cluster volume could be probed in full polarization while minimizing the effects of off-axis instrumental leakage. This paper presents our continuum results; polarisation data will be presented in a future paper.
Our observations are summarized in Table  1. Our flux density scale was set using a 10-15 min scan of PKS B1934 -638 on each day. We applied standard calibration and radio frequency interference (RFI) flagging techniques using the MIRIAD software package  (Sault, Teuben & Wright 1995 ). A total of around 40 per cent of the data were flagged due to the presence of strong RFI, which dominates specific channel ranges and occurs primarily at the low-frequency end of the band. All imaging was carried out using the DDFACET software package  (Tasse et al. 2018 ). We employed robust = 0 weighting  (Briggs 1995 )  to strike a balance between sensitivity and resolution. As Abell 3266 hosts a variety of extended radio sources, we used the subspace deconvolution ( SSD ;  Tasse et al. 2018 )  algorithm in order to more correctly model this diffuse radio emission.
The native resolution of our robust = 0 ATCA mosaic is around 4. 5 arcsec; to match the resolution of our ASKAP mosaics, we generated our science images at 9-and 15-arcsec resolution by varying the robust parameter appropriately ( robust = 0 for our 9-arcsec image, robust = + 0. 7 for our 15-arcsec image) before convolving with an elliptical Gaussian to achieve the desired resolution. We adopt a representative 5 per cent calibration uncertainty in our ATCA mosaic of Abell 3266. The right-hand panel of Fig. 2  shows our 6-arcsec ATCA image of Abell 3266; Fig. A1  of the supplementary material illustrates the dramatic impro v ement in image fidelity enabled by applying these next-generation algorithms to ATCA data processing.
While the flux density of source D2 drops precipitously at these frequencies, and as such our modelling likely o v erestimates the 1. 4-GHz flux density, we estimate an upper limit to the 1. 4-GHz flux density of S 1. 4 GHz = 1. 35 ± 0. 14 mJy. Equation ( 1 ) yields an upper limit radio luminosity of P 1. 4 GHz = (1. 36 ± 0. 13) × 10 22 W Hz -1. 8 Currently available at ht tps://github. com/synchrofit /synchrofit.
The spectral properties of these sources likely trace the rich dynamical history of the substructures within Abell 3266. Ho we ver, further work is required to fully unpack the history of Abell 3266 and its constituent radio galaxies, and the answers to a number of questions remain elusive. Finally, our results have demonstrated the depth and breadth of the detailed physics of cluster non-thermal phenomena and radio galaxies that can be extracted when applying next-generation techniques to exquisite multiwavelength data from the SKA Pathfinder and Precursor instruments. The coming dawn of the SKA era promises a revolution in our understanding of these rich and complex environments.
DATA AVA I L A B I L I T Y The images underlying this paper will be shared on reasonable request to the corresponding author. Raw visibilities from the ATCA are available via the Australia Telescope Online Archive (ATOA; ht tps://atoa. atnf. csiro. au ). Visibilities and pipeline-processed ASKAP images are available from CASDA ( https:// research. csiro. au/ casda/ ). The XMM-Newton data used in this paper are available through the Science Data Archive ( ht tps://www. cosmos. esa. int /web/xmm-newto n/xsa ).
In this context, the use of HPLC modeling software is highly recommended for accurate/rapid determination. In the present work, we propose an alternative mathematical approach based on a simple linear regression, which can be easily applied with an Excel sheet. It involves two (or more) gradient experiments with varying gradient times. Besides the need to consider a linear model, this approach can only work properly if the retention factors at the initial composition are sufficiently large. Therefore, we checked the method's applicability to a large variety of compounds including small molecules, peptides, and large proteins. The validity of the linear model was checked using a commercial HPLC modeling software (Osiris, Euradif, France). Finally, a simple Excel tool was developed for rapid and accurate calculation of all LSS parameters (the tool is particularly adapted for proteins, as shown in this work), and is freely available on our website.
Instrumentation and chromatographic conditions for the analysis of small molecules and peptides The experiments relative to peptides and small molecules were performed on a UHPLC 1290 Infinity system from Agilent Technologies (Waldbronn, Germany). The binary system was equipped with two high-pressure solvent delivery pumps, an autosampler with a flow-through needle injector, and a thermostatic column compartment with low dispersion preheaters. The system was hyphenated to a quadrupole-TOF high-resolution mass spectrometer (model G6560B) from Agilent Technologies. The measured dwell volume and overall extra-column volumes were 0. 17 and 0. 031 ml, respectively. Instrument control and data acquisition were performed by Mass Hunter software (Agilent Technologies). Mass spectrometry data were acquired in positive ion mode for peptides and basic compounds, and negative mode for acidic compounds.
Instrumentation and chromatographic conditions for the analysis of proteins The experiments relative to proteins and monoclonal antibodies were performed on an Acquity UPLC system (Waters). The system was equipped with a binary solvent delivery pump, an autosampler, a UV detector with a 0. 5 μl flow cell (wavelength of 214 nm, 20 Hz data acquisition rate, and fast time constant), and an injector equipped with a 2 μl injection loop (weak solvent was a mixture of H 2 O:ACN 90:10, strong solvent was pure ACN). The overall extra-column volume was about 13 μl as measured from the injection seat of the auto-sampler to the detector cell. The measured dwell volume was 0. 1 ml. Data acquisition and instrument control were performed by Empower Pro 2 software (Waters). The chromatographic column was a Waters bioresolve RP mAb polyphenyl (100 × 2.
We conduct a detailed analysis of 12 years of Fermi-LAT data that incorporates various large-scale gamma-ray emission components and we quantify the systematic uncertainty associated with the imperfect knowledge of the astrophysical gamma-ray sources. We find that the dynamical response caused by the LMC passage can alter the constraints on the velocity-averaged annihilation cross section for weak scale particle DM at a level comparable to the existing observational uncertainty of the MW halo's density profile and total mass.
J -factor all-sky maps for the Milky Way In order to produce J -factor maps of the MW that incorporate deformations induced by the dynamics of the MW-LMC system, we take the density from the present-day snapshots of the simulations in  Lilleengen et al. (2022)  and  Donaldson et al. (2022) , and measure the square of the DM density   along lines of sight on a HEALPix grid  (Górski et al. 2005)  with  side = 64, resulting in 49,152 lines of sight. Along these lines of sight, the density is evaluated in the centre of 1 kpc-sized bins out to 100 kpc. This choice of resolution is not a limitation of the BFE, which can faithfully reproduce structure on ∼ 100 pc scales, but rather motivated by the chosen resolution for the Fermi-LAT gamma-ray data set (see Sec. 3. 1). For these mock observations, the Sun is placed at a distance of 8.
2005)  with  side = 64 -resulting in a mean pixel spacing of ≈ 0. 9 • -as well as 30 logarithmically spaced energy bins. Due to the scarcity of photon events at the highest energies of the LAT's sensitivity range, we rebin the LAT data above 7 GeV as well as all astrophysical background and signal all-sky maps (see Sec. 3. 3) into larger macro bins. Hence, the high-energy range is included within the analysis framework by creating the following two macro bins:  [7, 20]  GeV and [20, 500] GeV. 3 Erkal19, monopole term 0. 01 0. 10 1. 00 J/J tot /∆Ω sr -1 Erkal19, full basis function expansion 0. 01 0. 10 1. 00 J/J tot /∆Ω sr -1 relative difference -0. 3-0. 2-0. 1 0. 0 0. 1 0. 2 0. 3 J monopole -J full J monopole +J full.
To this end, we adopt the weighted Poisson log-likelihood prescription developed by the Fermi-LAT collaboration in  Abdollahi et al. (2020) , which reads ln L 𝑤 ( µ| n) = ∑︁ 𝑖, 𝑝 𝑤 𝑖 𝑝 𝑛 𝑖 𝑝 ln 𝜇 𝑖 𝑝 -𝜇 𝑖 𝑝. (4) Per energy bin, each template pixel is assigned a weight    whose value we obtain via the Fermi Science Tools calling its routines gteffbkg, gtalphabkg and gtwtsmap. These weights are essentially obtained via integration in space and energy of the provided model or Fermi-LAT data 4. Since we aim to incorporate the effect of instrumental systematic uncertainties, we rely on a "data-driven" weight calculation approach, that is, we utilise the LAT data themselves to derive the weights. Thus, pixels are penalised in particularly bright parts of the gamma-ray sky (point-like/extended sources, diffuse emission along the MW's disc) taking into account potential sources of systematic errors like contamination of the data sample by charged cosmic-ray events, calibration of the instrument's point spread function (PSF) or spectral mismodelling of large-scale diffuse sources.
Fitting procedure and region-of-interest optimization We employ the following general analysis rundown and reasoning to statistically soundly and robustly assess the implications on DM pair-annihilation in the MW halo from Fermi-LAT data: (i) Generating a baseline gamma-ray sky model from a fit of the full set of astrophysical background templates to the all-sky data. (ii) Including the signal component, preparing the MW halo analysis by shrinking the total region of interest (ROI) to a smaller fraction of the sky that yields a good agreement between the TS-distribution (Eq. 5) with respect to LAT data and baseline model as input data n. (iii) Setting upper limits on the DM pair-annihilation cross section with respect to the optimised ROI and a particular scenario of signal templates.
A decisive characteristic of the dedicated LMC analysis is the need for an additional astrophysical background component quantifying the expected gamma-ray emission due to cosmic-ray interactions with gas and radiation fields in the LMC. To this end, we include four separate templates adopted from the set of extended templates being part of the 4FGL-DR2 catalogue. The components LMC-Galaxy, LMC-North, LMC-FarWest and LMC-30DorWest, representing the cosmic-ray induced gammaray emission of the LMC, have been derived in a previous study of the Fermi-LAT collaboration  (Ackermann et al. 2016 ) based on a six-year data set. These models are the result of a convolution of gas column density maps of the LMC reported in  Lande et al. (2012)  and a data-driven approach to extract regions of significant extended gamma-ray emission associated with the LMC.
3. Both panels in Fig. 4  have been obtained from 200 Poisson realisations of the baseline fit. On one hand, the left panel of this figure shows that the TS-distribution on LAT data stays within the 68% containment band of the scatter of the baseline fit's TS-distribution. On the other hand, the width of its median's parabola deviates from its analogue on LAT data. This qualitatively indicates that the chosen ROI may contain further gamma-ray emission components which are not or not fully accounted for in the selected set of astrophysical background contributors. Hence, this ROI may feature further emission components that are degenerate with the DM signal. Consequently, the 95% C. L. upper limits fall within the 68% containment band derived from mock data for most of the scanned DM masses except for light DM below ≲ 10 GeV.
The solid (dotted) lines denote the MW halo profile obtained from a set of  -body simulations in Ref. Donaldson et al. (2022)  adopting as initial profiles for the MW an NFW  (Hernquist)  profile and an NFW  (Hernquist)  profile for the LMC. The dashed lines represent the halo profiles from the simulation in Ref. Erkal et al. (2019 Erkal et al. ( , 2020)). In each case, the utilised J-factor map is comprised of both the MW and LMC halo. The upper limits have been derived with respect to our benchmark IE model Lorimer I. The all-sky map in the upper left corner illustrates the optimised size of the analysis ROI of ℓ ∈ [-167   (Buckley et al. 2015)  with five years of data are displayed with different shades of blue. We comment on the comparison to our bounds in Sec. 6.
In this case, constraints from the outer MW halo are stronger than the gamma-ray limits from dwarf spheroidal galaxies. The variance induced by the uncertainty of the outer MW halo profile is hence less pronounced than the impact of background modelling uncertainties in dwarf spheroidal galaxy studies. We stress that we assumed a very conservative approach in the selection of the region of interest for the analysis by requiring a strict compatibility between the statistical expectations derived from Poisson realisations of the baseline fit and the true Fermi-LAT data. This method yields reliable and robust limits but reduces the potential constraining power of the full data set and limits the accessible dynamically generated features of the MW-LMC system. As indicated by the right panel of Fig.
We adopt the IGRB spectrum associated with the selected LAT data's event class and type (see Sec. 3. 1) provided by the Fermi Science Tools 9. We stress that these spectra are only valid in combination with the official diffuse background model of the Fermi-LAT collaboration. Since our fit model (Eq. 3) renormalises each background component per energy bin, this restriction is, however, irrelevant for our cause. • resolved point-like and extended gamma-ray sources: Based on a 10-year data set, the Fermi-LAT collaboration has published an extensive catalogue, 4FGL-DR2,  (Abdollahi et al. 2020; Ballet et al. 2020 ) of all resolved and localised point-like or extended gamma-ray emitters in or outside of the MW. We include all of these sources with their respective properties reported in the catalogue in our analysis.
ACKNOWLEDGEMENTS The work of C. E. is supported by the  "Agence Nationale de la Recherche ", grant n. ANR-19-CE31-0005-01  (PI: F. Calore). M. S. P is partially supported by grant Segal  ANR-19-CE31-0017  of the  French Agence Nationale de la Recherche  (https://secularevolution. org). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Software: astropy (The Astropy Collaboration et al. 2013Collaboration et al. , 2018Collaboration et al. , 2022)), clumpy (version 3) Charbonnier et al. (2012); Bonnivard et al. (2016); Hütten et al. (2019),  fermi science tools (Fermi Science Support Development Team 2019 ), healpy (Górski et al. 2005; Zonca et al. 2019), iminuit (Dembinski et al.
These 146 proteins with significant differential expression encompassed 137 up-regulated and 9 down-regulated in VvMSA-RNAi versus wild-type. We first annotated the DEPs using BLAST to search for significant sequence homology and Uniprot to unravel conserved functional domains. As shown in Table  1 , this approach allowed us to classify the proteins in eight functional groups: (  1  The sequences of the DEPs were also submitted to functional enrichment analysis for protein-protein interactions using the network's STRING database. This analysis suggested that 142 out of the 146 grape proteins could interact with other protein partners. Gene Ontology (GO) enrichment analysis of the DEPs provided more details for their localization as cellular components (Figure  2 ), molecular functions (Figure  3 ), and involvement in biological processes (Figure  S2 ).
The latter classification turned out to be particularly exhaustive, suggesting involvement in more than hundred biological processes (Figure  S2 ). The classification by cellular components confirmed the annotated DEPS as nuclear proteins, their localization in distinct nuclear subdomains, nucleoprotein-and protein-protein complexes (Figure  2 ). Interestingly, the most concise GO classification was generated when using the criterion of molecular function: only twelve molecular functions were suggested so far, mainly of DNA, RNA and cyclic compound binding, as well as of catalytic and transferase activity (Figure  3 ). We first annotated the DEPs using BLAST to search for significant sequence homology and Uniprot to unravel conserved functional domains. As shown in Table  1 , this approach allowed us to classify the proteins in eight functional groups: The sequences of the DEPs were also submitted to functional enrichment analysis for protein-protein interactions using the network's STRING database.
Peak List Generation and Database Search Mascot Distiller 2. 4. 3. 3 (Matrix Science, Boston, MA, USA) was used to generate Mascot generic format peak lists. Deisotoping and peak picking were not performed between 112. 5 and 121. 5 m/z (the range containing iTRAQ reporter ions), and the higherenergy collisional dissociation and collision-induced dissociation spectra were merged by summing the two scans from the same precursor  [74, 75]. For each nuclear protein fraction (NaCl-nuclesolic; H 2 SO 4 -chromatin loosely bound; SDS-chromatin tightly bound) all relevant Mascot generic format peak lists were concatenated and searched, using Mascot Server 2. 3. 02 (Matrix Science), against the grape protein database of Genoscope (http://www. genoscope. cns. fr/externe/GenomeBrowser/Vitis/; accessed on 1 July 2021) combined with 54,898 entries in Uniprot annotated to Vitis vinifera.
That database was concatenated to its reversed decoyed FASTA database. The concatenated database contained a total of 162,488 proteins and 260 common MS contaminants. Methylthio (C), iTRAQ 8-plex labeling at the N terminus and lysine were set as fixed modifications, and variable modifications consisted of methionine oxidation, deamidation of asparagine and glutamine and iTRAQ 8-plex labeling of tyrosine. The isotope and impurity correction factors used for each iTRAQ label were those provided by the manufacturer. Precursor and fragment tolerances were set at 10 ppm and 0. 8 Da, respectively. The enzyme specificity was set to trypsin with an allowance of up to one missed cleavage. Using Mascot internal export scripts, the transformed Mascot DAT files into XML files were parsed with in-house scripts so that peptide sequences, scores and intensities of the individual reporter ion channels were reported.
5% TBST), blocked for 30 min with 2% TBST, rinsed in 0. 05% TBST for 5 min, incubated for 1 h with the primary antibody diluted in 0. 5% TBST, washed three times, 10 min each, in 0. 05% TBST, incubated for 1 h with the secondary peroxidaseconjugated antibody diluted in 0. 5% TBST, washed three times, 10 min each, in 0. 05% TBST, and three more times, 5 min each in double distilled water, immediately overlaid with an ex tempore prepared mix of equal amounts of the Western blotting detection reagents A and B (ECL™ Prime, Amersham, Chicago, IL, USA; ref. RPN2232), at final volume 0. 1 mL/cm 2 , incubated for 5 min, in darkness. All steps were carried out at room temperature. After exposure in a CCD camera (Amersham Imager AI600, GE Healtcare, Buckinghamshire, UK), the obtained ECL images were analyzed by using the software ImageQuant TL (GE Healthcare, Buckinghamshire, UK).
The ECL signal intensity of each band was quantified after background subtraction and the raw quantitative data were directly exported for further statistical analysis by unpaired t-test using GraphPad Prism, Version 5. The data were visualized in a heatmap generated by the Morpheus software (https://software. broadinstitute. org/morpheus; accessed on 1 May 2021). References of PAGE and WB materials, user manuals, list of antibodies and their working dilutions are provided in Tables  S1  and S2.
Acknowledgments: We are grateful to  Enrico Martinoia  from the  University of Zurich  and  Joelle Roche  from the  University of Poitiers  for their kind support,  Paolo Nanni  from the  FGCZ  for his help in the LS-MS/MS experiments,  Cécile Gaillard  for the regeneration of grape transgenic plantlets,  Florence Thibault  for help in microscopy, and the trainees  Guillaume Cornut  and  Vianney Souday  for having participated in bioinformatic analyses.