dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple sequence alignment Sequences of the AroQ family were acquired by three rounds of PSI-BLAST  35  using residues 1-95 of EcCM (the chorismate mutase (CM) domain of the E. coli CMprephenate dehrdratase) as the intial query (e-score cutoff 10 -4 ). For alignment, we created a position-specific amino acid profile from 3D alignment of four CM atomic structures (PDB IDs 1ECM, 2D8E, 3NVT, and 1YBZ) and iteratively aligned nearest neighbor sequences from the PSI-BLAST using MUSCLE  36  , each time updating the profile. The resulting multiple alignment was subject to minor hand adjustment using standard rules and trimmed sequentially (1) to retain positions present in EcCM, (2) to remove sequences with less than 82 positions, (3) to remove sequences with more than 30% gaps, and to remove excess sequences with more than 90% identity to each other.
Acknowledgments We thank  M. Weigt ,  R. Monasson ,  S. Cocco ,  F. Zamponi ,  A. F. Bitbol ,  Y. Meir ,  N. S. Wingreen  and members of the  Ranganathan and Rivoire laboratories  for discussions. This work was supported by grant  FRM AJE20160635870  (O. R. ), grant  ANR 17-CE30-0021-02  (O. R. ),  NIH  grant  RO1GM12345  (R. R. ), a  Data Science Discovery Award  from the  University of Chicago  (R. R. ) and a collaboration grant from the  France-Chicago Center  (R. R. and O. R. ).
B. Triple-axis spectroscopy Moving on to the magnetic excitations, we present our neutron spectroscopy data in Figs. 6 7 8. Figure  6  shows a series of constant-energy ( E ) cuts parallel to the (00l ), (h00), and (hh0) high-symmetry directions, and Figs. 7  and 8  show intensity maps recorded at different energies in the h0l and hhl reciprocal lattice planes, respectively. In the h0l plane, the intensity is localized around the magnetic Bragg peak positions at low energies. The intensity is highest at 10 meV, decreasing at lower energies until no signal is visible below about 5 meV (see Fig. 6 , middle panels). Above 10 meV, the intensity disperses outwards with increasing energy to form rings which eventually merge into rods of scattering running along the (00l ) direction. In the hhl plane, rods of scattering along (00l ) are observed at all energies studied, with some intensity modulation along the rods at the lower energies.
At higher energies the rods split into two, which then move apart. By 55 meV, the signal is almost unresolvable from the background. As we now show, the features just described are consistent with the scattering from spin-wave excitations of antiferromagnetically ordered Mn local moments. In order to analyze the neutron inelastic scattering data  [25] , first, the NPLOT MATLAB scripts library  [32]  was used to smooth the background and manually remove spurions and powder rings. Second, a suite of custom PYTHON programs (built using packages including those described in  [33] [34] [35] ) took care of visualization, interpolation, making one-dimensional cuts through the data along high-symmetry lines, and intensity peak fitting at different E. Examples of constant energy cuts are shown in Fig.
6. The high-symmetry lines used for cuts were = (0, 0, l ), = (h, 0, 0), and = (h, h, 0). Additionally, the intensity maxima as a function of energy were found at the and A points in order to anchor the turning points of the dispersion. Third, the extracted peaks were assembled into the spin-wave dispersion along high-symmetry paths in reciprocal space shown in Fig. 9. Following the approach used in Refs. [6, 36, 37]  for structurally related AMnX 2 compounds, we modeled the magnetic interactions in YbMnSb 2 with a Heisenberg effective spin Hamiltonian of the form H = i, j J i j S i • S j - i D S z i 2 , ( 1 ) and calculated the spin-wave spectrum using linear spin-wave theory (LSWT) for spins S i localized on the Mn ions. The parameters of H were obtained from a global fit to the measured dispersion curves using both custom PYTHON programs (orthogonal distance regression) and the SPINW MATLAB library  [38].
Third, the Mn-X -Mn bond angles associated with the the J 1 and J 2 superexchange paths both increase slightly, from 65. 92 • to 67. 10 • for J 1 and 100. 60 • to 102. 82 • for J 2 , going from YbMnBi 2 to YbMnSb 2. These changes will tend to reduce the AFM superexchange contribution to J 1 and increase it for J 2 , consistent with what is observed, although the effect is smaller than the overall increase in J 1 and J 2 associated with the decrease in a. Finally, and perhaps most interesting, the distance between Mn atoms in the c direction is almost the same for all the compounds listed in Table  I , and yet SJ c is around five times FIG. 6. Examples of one-dimensional, constant-energy cuts along high-symmetry directions. The (10l) cuts and cuts parallel to (h00) were from the h0l plane dataset, while the cuts parallel to (hh0) were from the hhl dataset.
This suggests that YbMnSb 2 is a promising system with which to investigate the interplay between magnetism and topological band electrons. Note added. Recently, a related study appeared in which the spin dynamics in YbMnSb 2 was measured by time-offlight inelastic neutron scattering  [39]. The spin Hamiltonian parameters obtained in that work are broadly in agreement with ours. The authors of Ref. [39]  also present data on the damping of the spin-wave modes.
ACKNOWLEDGMENTS We thank  Igor Mazin  for insights on the exchange interactions, and all those involved in the ILL VISA project that made it possible to perform the neutron scattering experiments remotely during the coronavirus pandemic in 2020-2021. We also benefited from the  ILL crystal alignment  and structural characterization instruments CYCLOPS (experiment No. EASY-671) and Orient Express (experiment No. EASY-667 ). Y. F. G. and A. T. B. acknowledge support from the  Oxford-ShanghaiTech  collaboration project. Y. F. G. was supported by the  Double First-Class Initiative Fund  of Shang-haiTech University. S. M. T. was supported by a scholarship from the  Rhodes Trust. J. -R. S. acknowledges support from the  Singapore National Science Scholarship, Agency for Science Technology and Research , and also from the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program synergy  grant ( HERO , Grant No.
Trial Design and Participants The full methodology and study design details for HELIOS-A have been described previously  [35]. In summary, HELIOS-A (NCT03759379) was a phase 3, global (57 sites in 22 countries), randomized, open-label study of vutrisiran in patients with ATTRv amyloidosis with polyneuropathy. The study protocol and amendments were approved by relevant Institutional Review Boards or Independent Ethics Committees. Written informed consent was obtained from each participant. The study was conducted in accordance with all applicable regulatory requirements, the current guidelines of Good Clinical Practice, and principles originating from the Declaration of Helsinki. Eligible patients in HELIOS-A were aged 18-85 years with a documented TTR variant and diagnosis of ATTRv amyloidosis, neuropathy (baseline Neuropathy Impairment Score of 5-130), a polyneuropathy disability (PND) score of B IIIb, adequate liver and renal function, and a Karnofsky Performance Status (KPS) score of C 60%.
KPS was only assessed at baseline in the APOLLO study; therefore, the KPS data for the external placebo group that are compared with the vutrisiran group month 18 data are derived from the patisiran Global open-label extension (OLE) study baseline. KPS was recorded at Global OLE baseline for all patients entering the Global OLE who were initially treated with placebo during APOLLO (APOLLOplacebo group). The mean treatment duration from APOLLO baseline to Global OLE baseline was 18. 8 months.
The similarities between HELIOS-A and APOLLO with respect to inclusion and exclusion criteria, endpoints, and the timing of endpoints assessment also supported the use of the APOLLO placebo group as an external control in HELIOS-A. A further potential limitation of this analysis is that measures of QOL specifically related to cardiac involvement were not assessed in HELIOS-A. While HELIOS-A recruited patients based on neuropathy impairment, approximately one-third of randomized patients also had evidence of cardiac involvement  [35] , and thus may have experienced benefit from vutrisiran treatment across cardiology-specific QOL domains that were not assessed here. The safety and efficacy of vutrisiran in patients with ATTR amyloidosis with cardiomyopathy are being assessed in the ongoing HELIOS-B study (NCT04153149), which includes the Kansas City Cardiomyopathy Questionnaire, a measure of patients' perception of health status and QOL.
These data are consistent with the positive effect of vutrisiran on polyneuropathy, measured by mNIS ? 7, observed in HELIOS-A compared with the external placebo group, and support the benefit of RNAi therapeutics for the treatment of patients with ATTRv amyloidosis with polyneuropathy. writing of the report. All authors had full access to all the data in the study and took final responsibility for the decision to submit the manuscript for publication. Alnylam Pharmaceuticals Inc. funded the journal's rapid service publication fee.
John Vest contributed to study design/methodology, data interpretation, drafting, and critical review of the manuscript. Prior Presentation. Some data from this manuscript have been presented at the Peripheral Nerve Society Annual Meeting, Miami, FL, USA, May 14-17, 2022. Disclosures. Senda Ajroud-Driss reports participating on Advisory Boards for Amylyx Pharmaceuticals, Biogen Inc. , and Orphazyme. John L. Berk   Compliance with Ethics Guidelines. The HELIOS-A study (NCT03759379) was conducted in accordance with the International Conference of Harmonisation guidelines for Good Clinical Practice, local regulatory requirements, and the principles of the Declaration of Helsinki. The study was approved by ethics committees or institutional review board for each study site (listed in the Supplementary Material), and all patients provided written informed consent.
ACKNOWLEDGMENTS The authors would like to thank the members of the  HELIOS-A Collaborators Study Group  for their work on the study. A full list of the members of the HELIOS-A  Collaborators Study  Group is provided in the Supplementary Material. The authors would also like to thank the patients and their families for their participation in the HELIOS-A study. Funding. This study was funded by  Alnylam Pharmaceuticals Inc. The funder collaborated with authors during study design, data collection, data analysis, data interpretation, and.
The identification of multiple simultaneous orientations of small molecule inhibitors binding to a protein target is a common challenge. It has recently been reported that the conformational heterogeneity of ligands is widely underreported in the Protein Data Bank, which is likely to impede optimal exploitation to improve affinity of these ligands. Significantly less is even known about multiple binding orientations for fragments (<300 Da), although this information would be essential for subsequent fragment optimisation using growing, linking or merging and rational structure-based design. Here, we use recently reported fragment hits for the SARS-CoV-2 non-structural protein 1 (nsp1) N-terminal domain to propose a general procedure for unambiguously identifying binding orientations of 2-dimensional fragments containing either sulphur or chloro substituents within the wavelength range of most tunable beamlines.
By measuring datasets at two energies, using a tunable beamline operating in vacuum and optimised for data collection at very low X-ray energies, we show that the anomalous signal can be used to identify multiple orientations in small fragments containing sulphur and/or chloro substituents or to verify recently reported conformations. Although in this specific case we identified the positions of sulphur and chlorine in fragments bound to their protein target, we are confident that this work can be further expanded to additional atoms or ions which often occur in fragments. Finally, our improvements in the understanding of binding orientations will also serve to improve the rational optimisation of SARS-CoV-2 nsp1 fragment hits.
Results Data collection and refinement statistics are summarised in Tables  1  and 2. The very high I/σI in the highest resolution shell (3. 0 to 19. 7) can in part be explained by the high resolution of the crystals but also with the low background during data collection. This is due to the complete absence of X-ray scattering in vacuum, which is typically a major cause of the background noise recorded at other macromolecular crystallography beamlines. First, we measured X-ray fluorescence spectra of the sample to identify chemical ions of interest that might be in the crystal or potentially be bound to the protein. The emission spectrum measured at excitation with 4. 5 keV exemplified by the nsp1-11C6 complex shows clear peaks assigned to K a lines of sulphur and chlorine at 2. 308 and 2.
622 keV, respectively (Figure  S1 ). To validate our system of locating the chlorine and sulphur atoms in anomalous difference Fourier maps at two distinct energies, the anomalous difference Fourier maps of sulphur from methionine and cysteine side chains present in nsp1 were visually inspected. There are two methionines and one cysteine in the N-terminal domain of nsp1. The anomalous signals originating from the sulphur atoms in these residues are visible in both 4. 5 keV and 2. 75 keV anomalous difference Fourier maps except for nsp1-11C6, for which the anomalous signal of sulphur in Met9 was not detected at 2. 75 keV. This is possibly because of the low completeness of the specific data set and the flexibility of Met9 as the first residue at the N-terminus. The signal levels of the sulphur atoms in these residues for all four datasets are listed in Table  3.
The overlapping local electron density maps for these residues are exemplified using the nsp1-7H2 complex at two diffraction energies (Figure  2 ). Interestingly, the first amino acid Met9 was previously placed in a somewhat uncertain position due to its side chain flexibility and therefore lack of electron density for certain side chain atoms. However, the anomalous signal of the sulphur atom clearly reveals its correct localisation (Figure  2A ). There is only a single position for the Cys51 side chain, supported by the spherical electron density of the anomalous signal (Figure  2B ). The two alternative conformations of the Met85 side chain built in the previous complex (PDB entry 8AYW) structure are also clearly supported by the elongated anomalous sulphur signal (Figure  2C ). These results not only validate the reliability of our approach but also show the significance of anomalous difference Fourier maps in revealing missing details in native (non-anomalous) electron-density maps.
Based on their intensity we could also estimate the occupancy for each orientation with a preference of the sulphur atom in the thiophene ring pointing towards the protein surface and establishing hydrogen bond interaction with Lys47  [6]  rather than pointing towards the solvent. As both sulphur difference peaks are located on the same ring, our approach is powerful enough to pinpoint individual atoms on a five-membered ring system with a distance of only 2. 6 Å between them. A further improvement of our approach could be to measure a third dataset at a shorter wavelength to further improve data resolution. Based on our work we recommend the following procedure for data collection of planar fragments with ambiguous or weak electron density due to low occupancy or the presence of multiple orientations.
First collect data at the highest possible resolution. If doubts exist about the correct orientation or conformation of the fitted fragment making rational fragment optimisation a challenge, use fragment analogues with detectable substituents such as sulphur and chlorine by measuring anomalous difference data above and below the absorption edge of the atom at a tunable beamline to clearly locate it orientation. If radiation damage is not an issue, data collection can include a native dataset at high resolution measured on the same crystal. Although currently limited to sulphur and chlorine-containing fragments, work is currently in progress to expand our approach to other elements frequently present in fragments.
Data Collection, Structure Determination and Refinement The long-wavelength diffraction experiments were carried out at DLS, beamline I23. Each sample was subjected to X-rays at energies of 4. 50 keV and 2. 75 keV (corresponding to wavelengths of 2. 755 Å and 4. 509 Å), above and below the chlorine K absorption edge of 2. 82 keV but both being above the sulphur K absorption edge of 2. 47 keV. Diffraction experiments were performed in vacuum using the semi-cylindrical PILATUS 12M detector. During the measurements the temperature of protein crystals mounted on copper sample holders was estimated to be at ca. 80 K.
Additionally, only pixels with a cloud top temperature above 263 K are included; in situ measurements of Arctic clouds have shown that these clouds are predominantly composed of liquid water  (de Boer et al. , 2009). This filtering to remove ice reduces the dataset to 31% of its original size. DARDAR, which is produced by combining lidar data from CALIOP and radar data from CloudSat  (Delanoë and Hogan, 2010; Ceccaldi et al. , 2013)  is used to analyse the efficacy of these filters to restrict the analysis to liquid clouds. Due to known issues with surface clutter affecting DARDAR retrievals, cases with cloud top heights below 720 m are not included. Figure  2  shows the DARDAR-retrieved cloud top phase fractions as a function of MODIS temperature for the set of MODIS pixels filtered to remove ice.
CC BY 4. 0 License. the retrieval to be classified as 'mixed' phase  (Bühl et al. , 2013). Restricting this study to pixels where DARDAR only registers supercooled liquid clouds would also reduce the volume of data available for analysis to less than 5% of the MODIS pixels co-located with the trajectories due to the DARDAR's nadir-only sampling. As such, the method used here for filtering out icecontaining pixels is deemed sufficient for this work. The potential effects of biases introduced by ice undetected by MODIS will be discussed more in Section 4. DARDAR flags 'Supercooled', 'Liquid' and 'Supercooled_and_ice' are considered to be 'Supercooled', 'Liquid' and 'Mixed', respectively. Phase fraction is calculated by dividing the number of successful retrievals for a given phase flag by the total number of DARDAR retrievals for a 25 km by 25 km pixel.
Top-of-atmosphere (TOA) albedo data were obtained from hourly Clouds and the Earth's Radiant Energy System (CERES) SYN1deg L3 dataset NASA/LARC/SD/ASDC (2017). These data were regridded from the original 1 x 1 • resolution to the same 25 x 25 km grid and projection as the MODIS data. Previous studies have shown that CERES TOA albedo measurements perform well over ocean (e. g. Sun et al. , 2006; Kato et al. , 2018) , but is biased relative to in-situ measurements if sea ice is present (e. g. Riihelä et al. , 2017; Huang et al. , 2022). Sea ice data were obtained from from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2 product  (DiGirolamo et al. , 2022). These data are produced in a 25 km resolution polar stereographic grid. A binary mask is created such that if there is a non-zero sea ice concentration, that pixel is considered sea ice, and ocean pixels are entirely ice-free (zero detected sea ice concentration).
The total aerosol optical depth at 550 nm (AOD) reanalysis data from the Copernicus Atmosphere Monitoring Services (CAMS;  Inness et al. 2019) , was used as an indicative measure of aerosol conditions in the region. These were regridded in a similar manner to the meteorological reanalysis data. The marine cold air outbreak index (MCAO;  Kolstad and Bracegirdle, 2008)  is an important indicator of cloud formation and behaviour at high latitudes. It measures the stability of the boundary layer and is calculated as the difference between the potential temperature at 800 hPa and the sea surface temperature  (Fletcher et al. , 2016a) , with positive values indicating higher instability. This metric is particularly suitable for the air moving off the ice edge as it highlights the difference in temperature of the relatively warm ocean with the cool overlying air masses.
Acknowledgements. RMW and EG were supported by funding from the  Royal Society  ( University Research Fellowship   URF/R1/191602 ). TG acknowledges funding by the  German Research Foundation (Deutsche Forschungsgemeinschaft ,  DFG , project " CDNC4ACI ",  GZ QU 311/27-1 ). We thank  George Horner  for his insight on generating the composite trajectories. 21 https://doi. org/10. 5194/egusphere-2023-734 Preprint. Discussion started: 2 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Data availability. The MODIS data were obtained from NASA Goddard Space Flight Center (https://modis. gsfc. nasa. gov/data/). The ERA5 data were obtained from the Climate Data Store (https://cds. climate. copernicus. eu/), and the CAMS reanalysis from the Atmospheric Data Store (https://ads. atmosphere. copernicus. eu/). The sea ice data were obtained from the National Snow and Ice Data Center (https://nsidc. org/data/). The CERES data were downloaded from the NASA Langley Research Center (https://ceres. larc. nasa. gov/data/).
2 and 4. 1, are postponed to Section 5. For the reader's convenience, Appendix A collects the notations inherited from  [JPT23]. Acknowledgments This work was supported by the French Agence Nationale de la Recherche, grant NONSTOPS (ANR-17-CE40-0006-01, ANR-17-CE40-0006-02, ANR-17-CE40-0006-03) and the CY Initiative of Excellence through the grant Investissements d'Avenir ANR-16-IDEX-0008. It was partly developed during VJ's stay at the CY Advanced Studies, whose support is gratefully acknowledged. VJ also acknowledges the support of NSERC. The authors wish to thank Martin Fraas for useful discussions.
Area rescaling We seek to provide estimates for the uncertainties of the n p (z) determination as a function of the projected Euclid data volume; thus we must extrapolate from the 402 deg 2 of the Flagship area to the larger spectroscopic overlap areas, and also attempt to characterise the impact of sample variance. Euclid is projected to overlap with DESI over 9015 deg 2 , and with both DESI and BOSS over 6005 deg 2. To rescale the determined uncertainties on mean redshifts, we divided the Flagship lightcone into multiple subdivisions (see Fig. 4 ) and re-ran our analysis on each of these subdivisions independently. This allowed us to approximate the relationship between the tracer-target overlap area and the uncertainties on z. We then fitted these relationships with power laws, and extrapolated to estimate the uncertainties from the full Euclid, BOSS, and DESI overlap area.
Clustering redshift measurements We measured clustering redshifts using the YAW software package between scales r min = 100 kpc and r max = 1000 kpc (following the scales used by KiDS; van den Busch et al. 2020), considering separately each of the four simulated Euclid photometric redshift catalogues presented by Euclid Collaboration (2021b). We performed the analysis independently for the full Flagship region (with photo-z), and for each subregion (shown in Fig. 4 ). In Fig. 5  we compare the clustering redshift measurements obtained by YAW (shown with purple error bars) to the true redshift distribution of the 'mean Rubin' photometric catalogue on Flagship. The measurements clearly trace the true distribution, and showcase the capability of clustering redshifts to retrieve redshift distributions for photometric catalogues.