dataesr/scientific-paragraphs-categorization huggingface dataset:
Ratio approach The calibration data used to correct inter-scan motion artifacts comprised rapid low-resolution, coil-combined magnitude images acquired immediately prior to each high resolution VFA acquisition. These images, {x k } K k=1 , assumed to have been rigidly co-registered to the same space, can be written as the product of a common image r and a net sensitivity field {s k } K k=1. The relative sensitivity,  k,ref can be computed with respect to one of the calibration acquisitions, used as a reference: 𝜅 k,ref = x k x ref = s k r s ref r = s k s ref = Δ k,ref. ( 5 ) Dividing each VFA acquisition by its relative sensitivity Δ k,ref results in a common modulation, s ref , which, although less homogeneous than the body coil used by Papp et al. , more faithfully restores the validity of assuming common modulation when computing R 1.
Participants One participant (female, 31 years) was scanned at 3T (MAGNETOM Prisma, Siemens) using a body coil for transmission and either the body coil or a 32-channel head array coil for reception. Three additional participants (2 female and 1 male; 32-41 years) were scanned at 7T (MAGNETOM Terra, Siemens) using an eight-channel transmit, 32-channel receive head array coil (Nova Medical) in a quadrature-like ("TrueForm") mode. All data were acquired with approval from the UCL research ethics committee.
MPM datasets MPM data were acquired using a multi-echo spoiled gradient echo sequence with flip angles of 6 • (PD-weighted, "PDw") and 26 • (T1-weighted, "T1w"), a TR of 19. 5 ms and an RF spoiling increment of 117 • with a total dephasing gradient moment per TR of 6. Eight echoes were acquired with TE ranging from 2. 56 to 15. 02 ms in steps of 1. 78 ms using a bandwidth of 651 Hz/pixel. Data were acquired with a nominal 1 mm isotropic resolution over a field of view of 160 mm right-left and 192 mm in the anterior-posterior and superior-inferior directions. Elliptical sampling and partial Fourier, with factor 6/8 in each phase-encoded direction, were used to accelerate the acquisition, leading to a scan time of 5 min per volume. A B + 1 map was estimated by acquiring a series of spin and stimulated echoes using previously described 3T and 7T protocols.
6, 7 These data were acquired with 4-mm isotropic resolution resulting in a total acquisition time of 3 min 48 s, and a further 1 min for B 0 mapping. For inter-scan motion correction, additional single echo acquisitions were acquired prior to each VFA acquisition to facilitate estimation of the relative receive field across positions. These data were acquired with a flip angle of 6 • , TE = 2. 4 ms, TR = 6. 5 ms, a bandwidth of 488 Hz/pixel and no acceleration schemes. At 3T, these data were acquired, receiving sequentially on the array and body coils, with 8-mm isotropic resolution leading to a scan time of 6 s per volume. To capture the greater spatial variation in the net sensitivity field at 7T, the resolution was increased to 4-mm isotropic leading to a scan times of 18 s per volume, but acquired only on the array coil due to the absence of a body coil.
R 1 analysis R 1 maps were computed using the hMRI toolbox,  8  which extrapolates the VFA signals to a TE of 0 ms, uses the small flip angle approximation  3  and corrects for imperfect spoiling. 9 The estimation always used the B + 1 map acquired in the space of the PDw acquisition. Maps were computed with and without inter-scan motion using all possible PDw and T1w combinations. To ease comparisons, all maps were constructed in the space of the first PDw volume. Rigid transformations between all volumes (calibration data and VFA acquisitions) and the first PDw volume were estimated using SPM12 (Wellcome Centre for Human Neuroimaging) having first corrected for intensity nonuniformity and skull-stripped the images. R 1 maps were computed with and without the proposed inter-scan motion correction schemes.
Validity of assumptions Equation (  5 ) assumes that the calibration data are insensitive to changes in the transmit field across positions such that  k,ref = s 1 ∕s 2. Here we test the validity of this assumption via simulation. Under the small flip angle approximation and allowing for position-specific transmit and receive fields, the calibration images can be written as: x k = s k 𝜌R 1 f T k 𝛼 c TR c f 2 T k 𝛼 2 c 2 + R 1 TR c = s k r. ( 7 ) Here k indexes the repetition of the acquisition, that is, the calibration data for each high resolution VFA acquisition, and c denotes the calibration-specific sequence settings. Considering the ratio method for simplicity,  1,2 can then be written more fully as: 𝜅 1,2 = Δ 1,2 f T 1 f 2 T 2 𝛼 2 c + f T 1 2TR c R 1 f T 2 f 2 T 1 𝛼 2 c + f T 2 2TR c R 1 = Δ 1,2 𝜕𝜅 𝜕Δ 1,2.
DISCUSSION We have introduced methods for correcting inter-scan motion artfacts in quantitative MRI that do not rely on the availability or spatial homogeneity of a body coil. The approaches are based on estimating the F I G U R E 4 Results at 7T. The first column shows the ground-truth map R1 for the three participants sorted based on the magnitude of inter-scan motion. Uncorrected and corrected R 1 maps are shown in the middle with the corresponding (normaliszd) error maps with respect to the ground-truth on the right. Correction is only applied for net receive sensitivity modulation and not for transmit field effects. Rows 1 to 3 of the figure correspond to datasets 1, 2, and 3 as reported in Table  1. The net motion is summarised as the root-sum-of-squares, across the three orthogonal axes, of the translations or rotations independently.
DATA AVAILABILITY STATEMENT The code used to fit the generative model is available at https://github. com/balbasty/multi-bias. A modified version of the hMRI toolbox that integrates this approach and enables B + 1 correction on a per-contrast basis is available at https://github. com/balbasty/hMRI-toolbox. The ratio approach can be performed natively with the hMRI toolbox: https://github. com/hMRI-group/hMRI-toolbox. The source code to reproduce the simulation figures is available at: https://github. com/fil-physics/Publication-Code.
nsp, non-structural protein; RdRP, RNA-dependent RNA polymerase. d, Heat map of identities at the protein level of representative human, bat and pangolin sarbecoviruses compared to human SARS-CoV-2 lineage B (NC_045512). Spike protein has been divided into functional domains, and the sequences are ordered according to percentage of identity of the RBD domain. The asterisk marks the absence of a functional ORF10 in Thai bat RacCS203 (accession number MW251308). The heat map was created using the gplots package in R (version 3. 6. 3). AA, amino acid. sequon. The crystals indeed showed clear electron density for the first N-acetylglucosamine residue of glycan attached to N370. The main chain at residue T372 makes a hydrogen bond with the glycan residue, altering the conformation of the main chain downstream, which results in partial unwinding of helix H4 in RBD 236.
Briefly, 2,000 complete Betacoronavirus genomes were downloaded from the GenBank and GISAID databases and then clustered to a 95% sequence identity using CD-HIT-EST (ref. 59 ). Overall, 185 representative sequences of all betacoronaviruses were used for further analysis. Owing to the high diversity in the genus Betacoronavirus, the full genomes belonging to the subgenera Sarbecovirus, Nobecovirus, Merbecovirus and Embecovirus were separately aligned using MAFFT multiple sequence alignment software and used to design 13-mer spiked primers for each cluster. The genomic position of the full set of primers was extracted from some representative subgenera sequences and close primers were removed. Finally, 416 spiked primers were synthesized by Eurofins Genomics.
Genome assembly and finishing Raw reads from the enrichment-based sequencing were processed with an in-house bioinformatics pipeline (Microseek; Bigot T. et al. , unpublished observations) comprising quality check and trimming, read normalization, de novo assembly, and ORF prediction of contigs and singletons, followed by three levels of taxonomic assignation  61. Sequences identified as Sarbecovirus were then mapped onto appropriate reference sequences using CLC Genomics Workbench 20. 0 (Qiagen). Trimmed reads from the amplicon sequencing were mapped to the SARS-CoV-2 genome first, and then mapped again (refined mapping) to the closest genome relative. When needed, complete genomes were obtained by conventional PCR and Sanger sequencing. Briefly, viral RNA was reverse transcribed using SuperScript IV reverse transcriptase (Invitrogen) and cDNA was subsequently used to fill the gaps in the genomes using Phusion High Fidelity DNA polymerase (New England Biolabs) and specific primers flanking the missing regions.
Recombination and phylogenetic analyses Identification of recombination events occurring during the evolutionary history of bat sarbecoviruses was performed using the IDPlot package  62  , a web-based workflow that includes multiple sequence alignment and phylogeny-based breakpoint prediction using the GARD algorithm from the HyPhy genetic analysis suite  63. First, a comprehensive analysis comprising 106 sequences and covering all non-human Sarbecovirus and Sarbecovirus-related complete genomes available in GenBank and GISAID databases was performed, including prototype strains of SARS-CoV-2 isolated in 2019. Special attention was paid to including bat-borne and pangolin-borne viral sequences to maximize the ability to capture a large diversity of the sarbecoviruses. Then, a reduced set of 36 sequences was chosen because of their phylogenetic proximity to SARS-CoV-2, and the GARD algorithm was run to identify recombination breakpoints in Laotian and representative human, bat and pangolin sarbecoviruses.
Generation of lentiviral pseudoviruses The synthetic genes encoding spike in BANAL-236 and in SARS-CoV-2 (strain BetaCoV/France/IDF0372/2020, GISAID accession number EPI_ISL_406596) were cloned into the pVAX1 vector with a cytoplasmic tail truncation of 19 amino acids. Pseudotyped lentiviral particles were prepared using HEK-293T cells (ATCC CRL-3216) seeded in 10-cm dishes. HEK-293T cells were co-transfected with 5 µg of spike-encoding plasmid, 10 µg of lentiviral backbone plasmid expressing the firefly luciferase (pHAGE-CMV-Luc2-IRES-ZsGreen-W) and 3. 3 µg of each lentiviral helper plasmid expressing HIV Gag-Pol (HDM-Hgpm2), Tat (HDM-tat1b) and Rev (pRC-CMV-Rev1b) using calcium phosphate precipitation  64. The medium was replaced 5 h post-transfection by 6 ml of DMEM without fetal calf serum (FCS) and phenol red.
Biolayer interferometry Purified Avi-tagged RBD was biotinylated using a BirA biotin-protein ligase kit according to the manufacturer's instructions (Avidity). The biotinylated RBDs at 100 nM were immobilized to SA sensors. A 1:2 dilution series of hACE2 starting at 100 nM in PBS-BSA buffer was used in cycles of 200-s association followed by 200-s dissociation steps to determine protein-protein affinity. The data were baseline-subtracted and the plots were fitted using the Pall FortéBio/Sartorius analysis software (version 12. 0). Data were plotted in Prism 9. 1. 0.
Crystallization and data collection Crystals of the complex BANAL-236 RBD-hACE2 were obtained at 4 °C in sitting drops by mixing 200 nl of the protein complex at 8 mg ml -1 with 200 nl of reservoir solution containing 0. 2 M lithium sulfate, 0. 1 M Tris 8. 5, 30% w/v PEG 4000. The crystals were soaked in reservoir solution containing 20% glycerol as a cryoprotectant before being flash-frozen in liquid nitrogen. X-ray diffraction data were collected on the beamline PROXIMA 1 at the SOLEIL synchrotron (St Aubin, France) and reduced using the XDS package 68. The structure of the complex was determined by molecular replacement with Phaser software 69 using the coordinates of the SARS-CoV-2 RBD in complex with hACE2 as a search template (Protein Data Bank (PDB) 6M0J). The model was manually corrected in COOT (ref.
MD simulations of RBD-hACE2 complexes Generation of homology models. Homology models of the complexes between the RBDs of BANAL-236 and BANAL-52/103 and hACE2 were constructed using the X-ray structure of the complex between the SARS-CoV-2 RBD and hACE2 (PDB code 6M0J; resolution 2. 45 Å) as a template using MODELLER version 10. 1 (ref. 72 ). These models included the fragments S19-D615 and T333-G526 of hACE2 and RBD, respectively, which were the regions resolved in the template. In these regions, BANAL-236 and BANAL-52/103 RBDs have a sequence identity to SARS-CoV-2 RBD equal to 96. 9% and 97. 4%, respectively. The alignment reported in Extended Data Fig. 3  was used. The zinc and chloride atoms in the template were retained during homology modelling; N-acetylglucosamine (NAG) and water residues were removed.
Seven disulfide bonds were detected by MODELLER in the template (three in hACE2 and four in the RBD) and enforced in the generation of the homology models using CHARMM-like distance and dihedral angles restraints. For each construct, 100 homology models were built and ranked on the basis of the normalized DOPE score  73. The top three scoring models of each complex were used as starting points of three independent MD simulations, as described in the following section. Set-up, equilibration and production of the MD simulations. The six homology models described in the previous section along with the X-ray structure of the SARS-CoV-2 RBD-hACE2 complex (PDB code 6M0J) were used as input to the CHARMM-GUI server  74. In the case of the X-ray structure, for consistency with the homology models, the zinc and chloride atoms in 6M0J were retained whereas the NAG and water residues were removed.
4e ). To estimate the binding energy between RBD and hACE2, we used the InterfaceAnalyzer tool in ROSETTA version 3. 11 (ref. 83 ; Extended Data Fig. 6a ) and the AnalyseComplex tool in FoldX version 4 (ref. 84 ; Extended Data Fig. 6b ). To identify relevant interactions at the RBD-hACE2 interface that could contribute to the binding affinity of the complex, we quantified the frequency of formation of inter-subunit salt bridges (Fig. 3b ) and hydrogen bonds (Extended Data Fig. 7 ) during the course of the MD simulations. For each frame of the trajectories, we used PLUMED to calculate the distances between the side-chain charged groups of aspartic acids (OD1/OD2), glutamic acids (OE1/OE2), lysines (NZ) and arginines (NH1/NH2). An inter-subunit salt bridge was then defined as formed if the distance between groups with opposite charge was lower than 0.
32 nm. We confirmed this calculation using the Salt Bridges tool available in VMD (ref. 85 ). To monitor the formation of inter-subunit hydrogen bonds, we used the Hydrogen Bond Analysis module of the MDAnalysis library version 1. 0. 0 (ref. 86 ). A donor-acceptor distance and angular cutoff of 0. 3 nm and 150° were used to define the formation of a hydrogen bond. We confirmed this calculation using the Hydrogen Bonds tool available in VMD.
Acknowledgements We thank  S. Mohamed Ali ,  N. Da Rocha ,  A. Brisebarre ,  T. Xaybounsou  and  S. Chonephetsarath  for their help at the bench;  A. Haouz  and the staff of the protein crystallogenesis facility at  Institut Pasteur  for help with crystallization trials; P. England from the  Molecular Biophysics facility at Institut Pasteur  for his support and access to the BLI equipment;  P. Legrand  from the PX1 beamline at Synchrotron SOLEIL for data collection support; and  P. Guardado-Calvo  for discussion and support with BLI experiments. NGS was performed with the help of the  Biomics Platform, Center for Technological Resources and Research, Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ),  Inclusive Blockchain Insurance using Space Assets  and the  Illumina   COVID-19  Projects' offer.
The work was granted access to the  High Performance Computing resources of the Institute for Development and Resources in Intensive Scientific Computing  under the allocation 2020-101592 made by  Grand Équipement National de Calcul Intensif. We thank the  Ministry of Health  and the  Ministry of Natural Resources and Environments, Lao People's Democratic Republic , for their authorization of the field work and the  Faculty of Environmental Science  for its authorization of the field research collaboration. The work was supported by an  Institut Pasteur 'Covid Taskforce'  and in part by the  H2020  project  101003589  (RECOVER) and  Labex IBEID  ( ANR-10-LABX62-IBEID ) grants. Field and laboratory work at IPL was also supported by a  UK embassy grant  (grant no. INT 2021/LOV C19 02 ) and a  Luxembourg Development special grant  (grant no.
Data availability Sequence data that support the findings of this study have been deposited in the GenBank and GISAID (https://www. gisaid. org/)databases with the following accession numbers: MZ937000/EPI_ISL_4302644 (BANAL-52), MZ937001/EPI_ISL_4302645 (BANAL-103), MZ937002/ EPI_ISL_4302646 (BANAL-116), MZ937003/EPI_ISL_4302647 (BANAL-236) and MZ937004/EPI_ISL_4302648 (BANAL-247). Raw sarbecovirus NGS reads and Sanger sequencing. ab1 files have been deposited in the National Center for Biotechnology Information Sequence Read Archive repository under the BioProject code PRJNA796968 (SAMN24959173-SAMN24959177). The crystal structure presented in this manuscript has been deposited in the PDB with the accession code 7PKI. The GROMACS topology and input files as well as the analysis scripts used are freely available on PLUMED-NEST (www.
Code availability version 10. 1 (https://salilab. org/modeller/) and GROMACS version 2020. 4 (http://www. gromacs. org) were used for MD simulations. XDS (version 5 February 2021), CCP4 (v 7. 0), COOT (version 0. 8. 6) and PHENIX (version 1. 19. 2-4158) software programs were used for X-ray diffraction data processing, model building and refinement of the complex between the BANAL-236 RBD and hACE2. PyMOL (version 2. 4. 2) was used for structural image rendering. PLUMED version 2. 7 (https://www. plumed. org), ROSETTA version 3. 11 (https://www. rosettacommons. org/), FoldX version 4 (http://foldxsuite. crg. eu/), MDAnalysis version 1. 0. 0 (https://www. mdanalysis. org/), MDTraj version 1. 9. 5 (https://www. mdtraj. org/1. 9. 5/index. html) and in-house scripts available at https://github.
com/maxbonomi/bat-MD were used for MD simulations. hyphy 2. 5. 31, PhyML and MAFFT implemented through NGPphylogeny (https://ngphylogeny. fr/) and Simplot 3. 5. 1 were used for phylogenetic and recombination analyses. MSSPE 1 was used for Betacoronavirus primer enrichment design and is available at https:// github. com/chiulab/MSSPE-design. MICROSEEK is an in-house pipeline that uses ALIENTRIMMER version 2. 0 (https://gitlab. pasteur. fr/GIPhy/ AlienTrimmer) for read trimming/clipping, BBNORM from BBMAP version 38. 86 package (https://sourceforge. net/projects/bbmap/) for coverage normalization, MEGAHIT version 1. 2. 9 (https://github. com/ voutcn/megahit) for assembly, an in-house ORF-finder (https://figshare. com/articles/code/translateReads_py/7588592), and then DIAMOND version 2. 0. 4 (https://github.
, 2021). Following recent advances addressing costs of invasive alien species at different regional scales  (Bradshaw et al. , 2021; Crystal-Ornelas et al. , 2021; Haubrock et al. , 2021c; Kourantidou et al. , 2021; Liu et al. , 2021)  and across taxonomic groups  (Cuthbert et al. , 2021) , we aim to better understand costs of fish invasions. To provide a necessary baseline for the economic impact of this taxon, we have therefore characterised, for the first time, the current status of knowledge on the global costs of invasive alien fishes using the InvaCost database  (Diagne et al. , 2020a). This database contains detailed information on reported costs (e. g. types of costs, sectors affected, regional attributes, reliability of cost estimates, etc. ) over the last 60 years, associated with ~ 1000 invasive alien species from all ecosystem types worldwide (i.
e. impacts occurring outside their native range). Invasive alien species included in the InvaCost database are thus those that spread outside of their geographic range of origin  (Blackburn et al. , 2011 ) and have a negative economic impact that was quantified in monetary terms. Our aims were to describe the reported global costs associated with invasive alien fish species, to explore the structure of these costs, and to identify gaps and potential biases in the estimation of past and current economic impacts. ). This version of the database compiles 13,123 cost entries reported from both English and non-English sources in a sufficiently detailed manner to allow a large-scale synthesis of the costs associated with invasive alien species at different spatial, taxonomic and temporal scales  (Diagne et al.
, 2020a; Angulo et al. , 2021). These cost data were primarily retrieved using a series of search strings entered into the Web of Science platform (https://webofknowledge. com/), Google Scholar database (https://scholar. google. com/) and the Google search engine (https://www. google. com/) to identify and collate relevant references on invasion costs. Local stakeholders and experts on invasions were also contacted as part of the search process. All references were thoroughly evaluated to identify their relevance and to extract information on costs. In the invasive alien species literature, there is a wide variety of costing practices which have an associated risk of misunderstandings and causing discrepancies among reported costs  (Diagne et al. , 2021). These may include, for example, differences in discounting across studies or in cost estimation methodologies.
Despite the obvious challenges of standardizing heterogeneous costs, InvaCost is the most comprehensive database on the economic costs of IAS that has largely succeeded in resolving the problems associated with standardisation over time and across countries where they have been reported  (Diagne et al. , 2020b). In addition, this database is public and regularly updated with either corrections if mistakes are detected and/or new data as they become available. With regard to monetary units, all costs published in the literature and included in the database were converted to 2017 US$ values (see  Diagne et al. , 2020a  and Supplementary Material 1 for detailed information). The database used for this analysis includes information on monetary costs across taxonomic, regional and sectoral descriptors, and allows for a distinction between observed (i.
Cost data sourcing and filtering We filtered the InvaCost database to retain costs related to fishes belonging to the classes Cephalaspidomorphi and Actinopterygii; these were the only fish taxa in the database with reported costs, but also included an entry listed as "Osteichthyes" (see  Pimentel et al. , 2000). Because the available information did not allow us to distinguish this entry among ray-finned fish (Actinopterygii) and lobe-finned fish (Sarcopterygii), it was kept as a "diverse" entry. In total, we identified 177 entries, from which 7 were excluded as no starting and / or ending year for the listed costs could be identified. After expansion, these entries resulted in 384 annualized cost entries (see expansion process below). Cost entries that were not attributable to single species, sectors or cost types within these classes were classified as "Diverse/Unspecified".
This means that costs covering several years (e. g. US$10 million between 2001 and 2010) are divided according to their duration (i. e. US$1 million for each year between 2001 and 2010). Finally, the cumulative costs of the invasion were estimated based on their classification in the following cost descriptors (i. e. columns) included in the database (Supplementary Material 1): (i) Method_reliability: indicating the perceived reliability of cost estimates based on the publication type and estimation method. Costs are considered to be of low reliability in those cases where they were derived from grey literature and/or are lacking documented, repeatable or traceable methods. On the contrary, costs are considered of high reliability if they come from peer-reviewed articles, official documents, or grey literature but with a fully documented, repeatable and traceable method  (Diagne et al.