dataesr/scientific-paragraphs-categorization huggingface dataset:
The two historical control animals were male and infected three weeks before the study with P4J15 LS. All animals were then exposed to a total dose of 10 5 TCID 50 of Omicron XBB. 1. 5 SARS-CoV-2 virus produced in Vero-ACE2-TMPRSS2 (NIH/BEI reference: NR-59105) via the combination of intranasal and intratracheal routes with sample collection and testing performed as previously described. 39  Tracheal swabs, nasopharyngeal swabs, and bronchoalveolar lavages were performed on all NHPs collected throughout the study to monitor levels of both genomic and subgenomic RNA for the SARS-COV-2 virus as previously described. 40  All animals and data points were included in the analysis. The NHP sample size was selected based on the large, 1-to 2-log reduction in viral RNA anticipated in the trachea, nasopharyngeal, and/or BAL with an effective therapy that can provide statistically significant differences between treated and untreated NHPs.
Hamster challenge model SARS-CoV-2 infection KU LEUVEN R&D has developed and validated a SARS-CoV-2 Syrian Golden hamster infection model that is suitable for the evaluation of potential antiviral activity of novel antibodies. [41] [42] [43]  The SARS-CoV-2 strain used in this study was the Omicron BA. 5 variant (BA. 5. 2. 1) [EPI_ISL_14782497] was isolated from nasopharyngeal swabs taken from patient in Belgium. The variant was isolated in Vero E6 then passage 2 virus on Calu-3 cells was used for the study described here and the genetic sequence of the stock was confirmed by deep sequencing analysis. The titer of the virus stock was determined by end-point dilution on Vero E6 cells by the Reed and Muench method. Live virus-related work was conducted in the highcontainment A3 and BSL3+ facilities of the KU Leuven Rega Institute (3CAPS) under licenses AMV 30112018 SBB 219 2018 0892 and AMV 23102017 SBB 219 20170589 according to institutional guidelines.
Falcon IV gain references were collected just before data collection. Data was collected using TFS EPU v2. 12. 1 using aberration-free image shift protocol (AFIS), recording 4 micrographs per ice hole. A total of 24 814 micrographs in EER format were recorded at magnification of 165kx, corresponding to the 0. 83 Å pixel size at the specimen level, with defocus values ranging from -0. 6 to -2. 0 µm. Exposures were adjusted automatically to 50 e -/Å 2 total dose.
Cryo-EM image processing During the data acquisition phase, on-the-fly processing was employed to assess the data quality for screening purposes, utilizing cryoSPARC live v3. 3. 1. 44  Raw stacks were subjected to motion correction without binning, utilizing cryoSPARC's implementation of motion correction and contrast transfer function estimation. 45  A total of 6,769,807 particles were automatically template-picked. Following several rounds of 2D classification, 362,329 particles were selected and utilized for ab-initio reconstruction and 3D classifications. Within this dataset, multiple conformers were identified; however, after thorough validation, a subset of 95,910 particles corresponding to a P4J15 fragment bound to an XBB1 trimer was deemed reliable. Homogeneous refinement using the selected particles resulted in a 3D reconstruction at a resolution of 3.
Cryo-electron microscopy model building To generate initial models of the P4J15 Fab and XBB1 spike, various approaches were employed. These included utilizing models from the Spike trimer (PDB ID 7QO7), AlphaFold2 (implemented through ColabFold), and ModelAngelo 0. 3  47  for sequence-based generation. The cryo-EM maps were fitted with the Spike trimer using UCSF ChimeraX, serving as the starting point for further manual refinement. Manual extension and building of the docked models were carried out using Coot 0. 9. 8. 48  To refine the models, Phenix 1. 20  49  was employed. The generated figures depicting the models were prepared using UCSF ChimeraX. The numbering scheme for the full-length Spike models within the global map is based on Omicron numbering. For models containing only the RBD within the local maps, wild-type numbering is utilized.
Acknowledgements We thank the  Vaccinology and Immunotherapy Center (VIC) at the Service of Immunology and Allergy of the Lausanne University Hospital  for assistance with cell sorting;  Laura Junges  and  Laetitia Bossevot  from  IDMIT/CEA  for qPCR in NHP samples. We thank  Isabella Eckerle ,  Meriem Bekliz  and the  Virology laboratory of Geneva University Hospital  for the Omicron RNA sample and variant isolates collection, the  Geneva Genome Center  for sequencing, and  Evarist Planet  for the viral sequences mapping. We thank  Laurence Durrer ,  Rosa Schier ,  Michaël François  and  Soraya Quinche  from the  EPFL Protein Production and Structure Core Facility  for mammalian cell production and purification of proteins,  Alexander Myasnikov ,  Bertrand Beckert  and  Sergey Nazarov  from the  Dubochet Center for Imaging  (an  EPFL, UNIGE, UNIL initiative ) for cryoEM grids preparation and data collection.
D. ). The  Infectious Disease Models and Innovative Therapies research infrastructure  (IDMIT) is supported by the " Program Investissements d′Avenir"   (PIA) , managed by the  ANR  under references  ANR-11-INBS-0008  and  ANR-10-EQPX-02-01. We would also like to thank  Trudi Veldman  and members of the  CARE-IMI  work package 4 team for helpful discussions.
Data availability All data supporting the findings of this study are available within the paper and in the Source Data. The reconstructed maps of the global Omicron Spike with Fabs bound are available from the EMDB database, C1 symmetry, EMD-17819. The atomic model for the RBDup with one Fabs bound in the locally refined map is available from the PDB database, PDB-8PQ2. All plasmids made in this study are available upon request to the corresponding authors.
Data were judged to be statistically significant when p < 0. 05. In Figures, asterisks denote statistical significance as calculated using the two-tailed non-parametric Mann-Whitney U test for two groups' comparison or with Kruskal-Wallis tests with Dunn's multiple-comparison correction. Analyses were performed in GraphPad Prism (GraphPad Software, Inc. ) and Microsoft Excel. FundingThis work was supported by the Corona Accelerated R&D in Europe (CARE) project funded by the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 101005077. The JU receives support from the European Union's Horizon 2020 research and innovation program, the European Federation of Pharmaceutical Industries Associations (EFPIA), the Bill & Melinda Gates Foundation, Global Health Drug Discovery Institute and the University of Dundee.
The content of this publication only reflects the author's view and the JU is not responsible for any use that may be made of the information it contains (funding to G. P. and R. L). G. P. and D. T. received support from the CoVICIS project (grant No. 10146041) funded by the European Union Horizon Europe Program. Additional funding was provided through the Lausanne University Hospital (to G. P. ), the Swiss Vaccine Research Institute (to G. P. and NCCR TransCure to H. S. ), Swiss National Science Foundation Grants (to G. P. ) and through the EPFL COVID fund (to D. T. ) and a donation from a private foundation advised by CARIGEST S. A. (to DT and GP).
replicates 1130 0 50 100 150 200 250 % Infectivity to infect cells for two more passages in the same conditions (P1 to P3). Putative viral escapees were further selected by serial passages of 2-fold diluted supernatants pre-incubated with high concentrations of antibodies (two concentrations, each tested in duplicates). Viral RNA extracted from supernatants collected at passage 5 was deep-sequenced. b) Mutations identified across escape selection experiments are indicated in the table. c) Prevalence in GISAID sequence database is indicated for each identified mutation. BA. 2. 75. 2 or BQ. 1 Spikes were mutated accordingly with the identified residues and pseudotyped VLPs produced and tested in conventional neutralization assays. Heatmap table overviews the EC 50 value neutralizing potency of P4J15 against the different VLPs.
MRI data analyses 2. 5. 1. Anatomical MRI data Local grey matter volumes were automatically assessed on the whole brain based on the standard voxel-based morphometry (VBM) approach using the Statistical Parametric Mapping software (SPM12) (http ://www. fil. ion. ucl. ac. uk/spm/) and the Computational Anatomy Toolbox (CAT12) (http://www. neuro. uni-jena. de/cat/) a fully automatic, toolbox for performing robust and accurate VBM analyses. The CAT12 version 6 (CAT12. 6) uses a robust initial affine registration and sample-specific template based on a diffeomorphic image registration algorithm (DARTEL;  Ashburner, 2007)  which is therefore adapted to the segmentation of brains of children.
Resting-state fMRI data 2. 5. 2. 1. Pre-processing. The pre-processing of children multi-echo rs-fMRI data followed the approach detailed in  Kundu et al. (2013). Briefly, multi-echo rs-fMRI data were preprocessed using the standard parameters of multi-echo ICA (ME-ICA version 3. 2; https://github. com/ME-I CA/me-ica), an integrated strategy combining multi-echo EPI preprocessing (3 TE) and independent component analysis (ICA) to overcome the common artefacts related to head motion in child rs-fMRI data. For the denoising, independent components were used to automatically classified as "Bold" or "No-bold" signal, based on the principle of echo-time dependence which is a distinctive characteristic of BOLD T2 * (transverse susceptibility-weighted relaxation rate) signal (i. e. , BOLD signals have linearly echo-time-dependent percent signal changes;  Bandettini et al.
ROI analysis An in-house MATLAB function was used to estimate the median value of the VBM and ReHo/fALFF signals within individual Regions of Interest (ROIs) derived from the AAL2 atlas  (Rolls et al. , 2015; Tzourio-Mazoyer et al. , 2002). Individual mean values for VBM, ReHo and fALFF signals were calculated in the 6 a priori defined regions of interest (ROI) corresponding to main nodes of the IC brain network (see Fig. 1 ): ACC  (Aron et al. , 2014) , IFG  (Aron et al. , 2014) , ant-Insula  (Houdé et al. , 2010; Wu et al. , 2019) , caudate nucleus and putamen  (Vink et al. , 2005; Zandbelt and Vink, 2010). We used the AAL-2 atlas  (Rolls et al. , 2015; Tzourio-Mazoyer et al. , 2002)  for the spatial definition of the ACC, IFG, putamen and caudate nucleus. For the Insula, we identified two sub-parts of the anterior insula and defined two ROIs derived from the meta-analysis of  Houdé et al.
3 ), a structural equation model (SEM) adapted to longitudinal data  (Kievit et al. , 2018; Menu et al. , 2022). Such BLCS models can easily and accurately estimate interindividual differences in the initial levels and their influence on training-related changes  (Menu et al. , 2023). Analyses were conducted with the lavaan package (Version 6. 8;  Rosseel, 2012)  in R software, using full information maximum likelihood with robust standard errors to account for missingness and nonnormality. Model fit was assessed by standard indices  (Schermelleh-Engel et al. , 2003) , including the chi-square test, the root-mean-square error of approximation (RMSEA; acceptable fit: < 0. 08, good fit: < 0. 05), the comparative fit index (CFI; acceptable fit: 0. 95-0. 97, good fit: > 0. 97) and the standardized root-mean-square residual (SRMR; acceptable fit: 0.
47, J = 12. 06 M star at a distance of 80 pc  (Stassun et al. 2019 ). This system contains a transiting/eclipsing object for which our analysis of all collected observations indicates a planetary companion. The system was first observed by TESS (Transiting Exoplanet Survey Satellite;  Ricker et al. 2014 )  where a threshold-crossing event was detected. It is at that moment that TIC 335590096 received the TOI-4860 designation, with the planet candidate being referred to as TOI-4860. 01. Several ground-based photometric observatories later confirmed the existence of periodic transit-like features. All photometric data are described in Section 2. 1. In addition, spectra were collected to determine the stellar parameters and measure radial velocities (Section 2. 2 ). We describe our analysis of all data in Section 2.
Description of the photometric data TOI-4860 was observed with TESS in sector 10 with 30-min cadence spanning 2019 March 26 to 2019 April 22 and sectors 36 and 46 with 10-min cadence, respectively, spanning the periods 2021 March 7 to 2021 April 2 and 2021 December 2 to 2021 December 30. The full-frame images were first processed by the Quick Look Pipeline  (Huang et al. 2020 )  resulting in the detection of a threshold-crossing event and the assignment of a TESS Object of Interest (TOI) number on 2021 December 21. The data were later reprocessed by the TESS-SPOC pipeline (Science Processing Operations Center;  Jenkins et al. 2016 )  and we make use of these to search for TTVs. We retrieved these data from the NASA Mikulski Archive for Space Telescopes (MAST) with the LIGHTKURVE package (Lightkurve Collaboration 2018 ).
We obtained ground-based seeing-limited photometric data with the TRAPPIST-South/North (TRAnsiting Planets and PlanetesImals Small Telescope;  Gillon et al. 2011 ; Jehin et al. 2011 ; Barkaoui et al. 2019 ) , SPECULOOS (Search for Planets EClipsing ULtracOOl Stars;  Burdanov et al. 2018 ; Delrez et al. 2018 ; Sebastian et al. 2021 ) , and MuSCAT3 (Multicolor Simultaneous Camera for studying Atmospheres of Transiting exoplanets;  Narita et al. 2020 )  facilities. Two transits were obtained with TRAPPIST-South on 2022 March 27 and 2022 April 22 in a custom blue-blocking band and the Sloan-z band, respectively, and we obtained one transit with TRAPPIST-North in the I + z band on 2023 January 21. We observed one transit simultaneously in the Sloan-g , Sloan-i , and Sloan-r bands, as well as using the SPeculoos' Infra-Red photometric Imager for Transits camera (SPIRIT), which is an InGaAs-based instrument, with a custom wide-pass filter called zYJ (Pedersen et al.
, in preparation) using all four SPECULOOS-South telescopes on 2023 January 27, and one transit in the Sloan-z band on 2023 January 30. The data reduction and photometric measurements of the TRAPPIST and SPECULOOS data were done with a custom pipeline built with PROSE  (Garcia et al. 2022  ), a PYTHON framework designed for image processing. MuSCAT3 observed one transit epoch on 2023 April 18 in four photometric bands simultaneously ( Sloan-g , Sloan-i , Sloanr , and Sloan-z s ). All observations are reported in the journal of observations in Table  A1  (Supplementary data).
Spectroscopic data and stellar parameter determination We collected an optical spectrum of TOI-4860 with the MagE spectrograph (Magellan Echellette;  Marshall et al. 2008  ) on the 6. 5m Magellan Baade Telescope on 2023 February 3 ( UT ) under clear and stable conditions. v We used the 0. 7-arcsec slit to gather a 3500-10 000 Å spectrum with R ∼ 4100. We collected two exposures of 530 s each, totalling 17. 7 min on source, followed by a 2-s ThAr e xposure. We observ ed the spectrophotometric standard LTT 3218  (Hamuy et al. 1994 ; Vernet et al. 2010 ; Moehler et al. 2014  ) earlier that night for flux calibration and did not apply any telluric correction. Relying on a standard set of afternoon MagE calibrations, we reduced the data with PYPEIT  (Prochaska et al. 2020a , b ). The final spectrum has a maximum Signal to Noise Ration (SNR) of 149 at 7584 Å and a median SNR of 61 o v er the full 3500-10 000 Å range.
R E S U LT S A N D D I S C U S S I O N Our joint analysis of the ground-based photometry and radial velocities confirms that candidate TOI-4860. 01 is consistent with a transiting exoplanet. All transits we collected have similar depths, and the depths of odd transits are similar to those of even transits. All transits are consistent with the TESS transits as well (Fig. 1  ). To further confirm the planetary nature of this signal, we searched for contaminating light. Gaia measurements demonstrate that TOI-4860 is a high proper-motion system. A search of the Digitised Sky Survey data base 1 shows that there is no known contaminating background source near the target at the moment (Fig. A4 , Supplementary data). In addition, mid-to-late M dwarfs suffer less from this kind of false positive on account of their spectral types.
g. Huang, Wu & Triaud 2016 ). This w ork ed for Sun-like stars, but a more judicious definition would be to use the scaled distance a b / R instead, which makes TOI-4860 b dynamically and irradiatively similar to other warm Jupiters. Warm Jupiters are e xpected to hav e follo wed a dif ferent migratory pathway from hot Jupiters, and a large fraction ( > 50 per cent ) of them host additional planets in their system  (Huang et al. 2016 ; Wu, Rice & Wang 2023 ). They also tend to occupy orbits that are aligned with the stellar equator  (Triaud 2018 ; Albrecht, Dawson & Winn 2022 ; Rice et al. 2022 ; Attia et al. 2023 ). MNRASL 00, L1 (2023) photometry and high-resolution imaging data for TOI-4860 are available on ExoFOP (Exoplanet Follow-up Observations Programme) at ht tps://exofop. ipac. calt ech.
edu/tess/t arget. php?id = 335590096. These data are freely accessible to ExoFOP members immediately and are publicly available following a 1-yr proprietary period. SPECULOOS and TRAPPIST data are also publicly available at the European Southern Observatory (ESO) archive a year after obtention. The MagE Spectrograph User Manual is available at ht tps://www. lco. cl/t echnical-documentation/t he-mage-spectrog raph-user-manual/. The SPEXTOOL User's Manual can be obtained from http://irtf web. if a. hawaii. edu/ ∼ spex/observer/.
BACKGROUND AND AIMS: Primary sclerosing cholangitis (PSC) is a probable autoimmune liver disease characterized by persistent and progressive biliary inflammation that leads to biliary infection, cirrhosis, or cholangiocarcinoma. Genomewide omics data are scarce regarding this severe disease. METHODS: MEDLINE database gene prioritization by text mining (biliary inflammation, biliary fibrosis, biliary stasis) was integrated in distinct omics data: (1) PSC liver transcriptome training and validation cohorts, (2) farnesoid X receptor (FXR) mice liver transcriptome subjected to an FXR agonist or FXR knockout mice; (3) liver single-cell transcriptome of the Abcb4À/À mice model of PSC. RESULTS: A liver molecular network highlighted the involvement of nuclear receptor subfamily 0 group B member 2 (NR0B2) and its associated nuclear receptor FXR in a metabolic cascade that may influence the immune response.
NR0B2 upregulation in PSC liver was independent of gender, age, body mass index, liver fibrosis, and PSC complications. Heterogeneity of NR0B2 upregulation was found in cholangiocyte cell types in which the NR0B2-based cell fate decision revealed the involvement of several metabolic pathways for detoxification (sulfur, glutathione derivative, and monocarboxylic acid metabolisms). Genes potentially implicated in carcinogenesis were also discovered on this cholangiocyte trajectory: GSTA3, inhibitor of DNA binding 2, and above all, TMEM45A, a transmembrane molecule from the Golgi apparatus considered as oncogenic in several cancers. CONCLUSION: By revisiting PSC through PubMed data mining, we evidenced the early cholangiocyte deregulation of NR0B2, highlighting a metabolic and premalignant reprogramming of the cholangiocyte cell type.
Results Biomedical Text Mining to Discover Genes Related to PSC Text mining gene prioritization was performed by querying the MEDLINE database  12  based on keywords associated with PSC  1  (Figure  1 ). The Génie algorithm enables the retrieval of all literature attached to all genes in a genome and to their orthologs according to a selected topic. 13 It is based on Naive Bayes classifiers and has the advantage of including the genes of different species. 9 ollowing the review by Lazaridis and LaRusso,  1  3 Medical Subject Heading terms for the characterization of PSC were used in the "Génie" algorithm: "biliary inflammation", "biliary fibrosis", and "biliary stasis", giving 3 lists of genes. The intersection of these 3 gene sets gave rise to a list of 525 ranked genes (workflow, Figure  1  and Table  A1 ).
Validation of PSC-Related Genes We tested the set of PSC-related genes on the transcriptome study GSE61256 7 (Figure  1 ) which included liver biopsy samples from patients with PSC (n ¼ 14) or primary biliary cholangitis (PBC) (n ¼ 11), obesity (n ¼ 24), NAFLD (n ¼ 23), nonalcoholic steatohepatitis (n ¼ 24), and normal controls (n ¼ 38). This data set was not found using the following 3 keywords: "biliary inflammation", "biliary fibrosis", and "biliary stasis" in the Gene Expression Omnibus database. After gene annotation with the corresponding platform, data from GSE61256 were reduced in dimension with 507 of 525 presenting PSC-related genes. Unsupervised principal component analysis revealed a clear clustering of PSC samples as compared to the others and particularly compared to PBC, a disease that affects the small biliary ducts (P value ¼ 6.
Characterization of PSC-Related Genes in the Mouse Phenotype Database During "Génie" algorithm text mining on PSC, the option of a gene paralog was implemented during the literature search as it could take advantage of combining results obtained in humans and in animal models. Scientific experiments performed on mice models have made a significant contribution to understanding the pathophysiology of PSC through bile acid (BA) homeostasis. 15 Functional enrichment using upregulated PSC-related genes was performed on the Mouse Phenotype database and indicated an abnormal physiology of the liver and hepatobiliary system (Figure  2D ). On the phenotype network for the hepatobiliary system, we were able to observe some of the leading genes upregulated in PSC such as FOXP3, TNFRSF12A, TNFRSF1A, LCN2, and NR0B2 (Figure  2E ).
Hepatic Regulation of the Immune Response and Intracellular Receptor Signaling Under FXR Dependency NR1H4 (or FXR) was the highest ranked (Table  1 ) among the PSC-related genes. FXR is a nuclear receptor (NR) implicated in BA metabolism. 16 The GSE54557 transcriptome data set includes the gene expression of Mus musculus liver with or without liver FXR knockout (KO), reversed or not by FXR agonist treatment. 16 Data from GSE61256 were reduced in dimension with the 525 PSCrelated genes, and the Pavlidis Template Matching algorithm  17  was used to analyze FXR-dependent regulations (Figure  3A  and Table  A3 ). Ninety-one genes from the 525 PSC-related genes were regulated through FXR in the mouse liver samples. In turn, this set of genes allowed stratification of the samples of FXR liver KO from wildtype samples (WT), and reversion by the FXR agonist was only obtained only in the latter (P value ¼ 1.
NR0B2 Was Regulated in the Livers of PSC Patients Under FXR Dependency In order to evaluate FXR dependency in the PSC-related human liver expression profile, we then crossed the PSC human liver signature (GSE61256) (Table  A2  and Figure  2 ) with the mouse liver FXR functional signature (GSE54557) (Table  A3  and Figure  3 ). The overlaps of this analysis determined a set of 34 common genes (Figure  4A ). A  machine learning model based on leave-one-out cross-validation (pamr) was supervised between the human liver PSC samples and other liver samples (GSE61256) and, thus, define a predictive molecular ranking (Table  A2 ) with a minimal misclassification error of 8. 9% between group samples (Figure  4B ). Within this optimum molecular ranking to predict human PSC liver, we observed 8 molecules subject to FXR-dependent regulation (in pink, Figure  4C ), that is, NR0B2, CYP7A1, DGAT2, NR1H4 (alias FXR), TNFRSF1A, BCHE, KLF6, and CEBPA.