dataesr/scientific-paragraphs-categorization huggingface dataset:
1a ). We next evaluated the neutralization potency of S2X259 using authentic SARS-CoV-2-Nluc as well as murine leukemia virus (MLV) and vesicular stomatitis virus (VSV) pseudotyping systems. S2X259 potently neutralized authentic SARS-CoV-2, SARS-CoV-2 S MLV and SARS-CoV-2 S VSV with respective half-maximal inhibitory concentrations of 144 ng/mL, 87 ng/mL and 213 ng/mL (Fig. 1d-e  and Extended Data Fig. 2 ). We subsequently evaluated S2X259mediated neutralization against VSV pseudotypes harboring SARS-CoV-2 S from the B. 1. 1. 7, B1. 351, P. 1, and B. 1. 427/B. 1. 429 lineages of concern and the N439K or the Y453F mutation identified in circulating isolates  6, [25] [26] [27]. S2X259 neutralized all of these mutants without significant change in potency, in line with the identical binding affinities determined for each VOC RBD (Fig.
S2X259 was administered via intraperitoneal injection 48h before intranasal challenge and the lungs of the animals were collected 4 days later for the quantification of viral RNA and replicating virus. Despite the lack of FcγRs activation in vitro, which has been previously ascribed with a key role for protection against SARS-CoV-2  42, 43  , S2X259 administration at 4 mg/kg reduced the amount of viral RNA detected in the lungs by 1 order of magnitude compared to hamsters receiving a control mAb (Extended Data Fig  9a, c ). Furthermore, greater than 2 orders of magnitude reduction in infectious virus was observed in the lungs of hamster administered with S2X259 at 4 mg/kg in comparison to the control group, (Extended Data Fig. 9b, d ). We next assessed the prophylactic activity of S2X259 alone and in combination with S309 against B.
Sarbecovirus sequence analysis and SARS-CoV-2 mutant counts Alignment and phylogenetic tree of the strains within the sarbecovirus subgenus was generated using MEGA 7. 0. The viral sequences were obtained from GISAID EpiCoV project (https://www. gisaid. org/). Analysis was performed on sequences submitted to GISAID up to April 2 nd , 2021. The spike protein sequences were either obtained directly from the protein dump provided by GISAID or, for the latest submitted sequences that were not incorporated yet in the protein dump at the day of data retrieval, from the genomic sequences with exonerate 54 2 2. 4. 0--haf93ef1_3 (https://quay. io/repository/biocontainers/exonerate?tab=tags ) using protein to DNA alignment with parameters -m protein2dna --refine full --minintron 999999 --percent 20 and using accession YP_009724390.
Binding to cell surface expressed Sarbecovirus S proteins by Flow Cytometry ExpiCHO-S cells were seeded at 6 x 10 6 cells/mL in a volume of 5 mL in a 50 mL bioreactor. prepared at 2 µg/ml and added onto cells after two washing steps. Cells were then washed twice and resuspended in wash buffer for data acquisition at ZE5 cytometer (Biorad).
Affinity determination by Surface Plasmon Resonance (SPR) SPR binding measurements were performed using a Biacore T200 instrument using either anti-AviTag pAb (for capturing S proteins) or StrepTactin XT (for capturing RBDs) covalently immobilized on CM5 chips. Running buffer was Cytiva HBS-EP+ (pH 7. 4). All measurements were performed at 25 °C. S2X259 Fab concentrations were 11, 33, 100, and 300 nM run as single-cycle kinetics. Double reference-subtracted data were fit to a 1:1 binding model using Biacore Evaluation software, which yields an "apparent KD" for the S-binding data because the kinetics also reflect S conformational dynamics. For SARS-CoV-2 S the dissociation rate was too slow to fit, so the KD,app is reported as an upper limit. The KD above 1 µM is approximate and was determined from a fit where Rmax was set to a constant based on theoretical Rmax.
Affinity determination using bio-layer interferometry Biotinylated RBD (WT, N501Y, K417N-E484K-N501Y, or K417T-E484K-N501Y) were immobilized at 5 ng/µL in undiluted 10X Kinetics Buffer (Pall) to SA sensors until a load level of 1. 1nm. A 1:3 dilution series of Fab in undiluted kinetics buffer starting at 10nM was used for 600 seconds association prior to 600 second dissociation to determine protein-protein affinity. The data were baseline subtracted and the plots fitted using the Pall FortéBio/Sartorius analysis software (version 12. 0). Data were plotted in Prism.
CryoEM sample preparation, data collection and data processing Recombinantly expressed and purified fab S2X259 and SARS-CoV-2 S hexapro were incubated at 1 mg/ml with a 1. 2 molar excess of fab at 4˚C during 1 hr. Three microliters of the complex mixture were loaded onto freshly glow discharged R 2/2 UltrAuFoil grids (200 mesh) or lacey grids covered with a thin layer of manually added carbon, prior to plunge freezing using a vitrobot MarkIV (ThermoFisher Scientific) with a blot force of 0 and 7-7. 5 sec blot time (for the UltrAuFoil grids) or with a blot force of -1 and 2. 5 sec blot time (for the lacey thin carbon grids) at 100 % humidity and 21˚C. Data were acquired on a FEI Titan Krios transmission electron microscope operated at 300 kV and equipped with a Gatan K2 Summit direct detector and Gatan Quantum GIF energy filter, operated in zero-loss mode with a slit width of 20 eV.
Automated data collection was carried out using Leginon 66 at a nominal magnification of 130,000x with a super-resolution pixel size of 0. 525 Å. The dose rate was adjusted to 8 counts/pixel/s, and each movie was fractionated in 50 frames of 200 msec. Two datasets were collected from UltrAuFoil grids with the stage tilted 30° and 55° to circumvent particle preferential orientation. The third dataset was collected on lacey grids covered with a thin layer of carbon. The three datasets, with a total of 6786 micrographs, were collected with a defocus range comprised between -0. 8 and -2 μm. For each dataset, movie frame alignment, estimation of the microscope contrast-transfer function parameters, particle picking and extraction were carried out using Warp 67. Particle images were extracted with a box size of 800 pixels 2 and binned to 400 yielding a pixel size of 1.
05 Å. The three datasets were merged and two rounds of reference-free 2D classification were performed using cryoSPARC  68. Subsequently, one round of 3D classification with 50 iterations, using PDB 6VXX as initial model, was carried out using Relion 69, 70 without imposing symmetry. 3D refinements were carried out using non-uniform refinement  71. Particle images from each dataset were subjected to Bayesian polishing 72 using Relion before merging them to perform another round of non-uniform refinement in cryoSPARC followed by per-particle defocus refinement and again non-uniform refinement. To improve the density of the S/S2X259 interface, the particles were symmetry-expanded and subjected to a Relion focus 3D classification without refining angles and shifts using a soft mask encompassing the RBD and S2X259 variable domains.
Spike encoding plasmids HKU3 (QND76020. 1), Rs3367 (AGZ48818. 1), YN2013 (AIA62330. 1), Rs4874 (ATO98205. 1), Rs4255 (ATO98193. 1), Rs4231 (ATO98157. 1), Rs4084 (ATO98132. 1), ZXC21 (AVP78042. 1), ZC45 (AVP78031. 1), Rp/Shaanxi2011 (AGC74165. 1), Rm1/2004 (ABD75332. 1), Rf1-2004 (ABD75323. 1), BM48-31/BGR/2008 (YP_003858584. 1), RaTG13 (QHR63300. 2), PC4-127 (AAU93318. 1), SARS-CoV2 (YP_009724390. 1), LYRa3 (AHX37569. 1), Cp/Yunnan2011 (AGC74176. 1), SARS-CoV Urbani (AAP13441. 1), As6526 (ATO98108. 1), BtKY72 (APO40579. 1), RmYN02 (EPI_ISL_412977), Pangolin_Guangdong-2019 (EPI_ISL_410721), Pangolin-Guanxi-2017 (EPI_ISL_410539), HKU3-12 (ADE34812. 1) were diluted in cold OptiPRO SFM (Life Technologies, 12309-050), mixed with ExpiFectamine CHO Reagent (Life Technologies, A29130) and added to the cells.
Signaling was quantified by the luciferase signal produced as a result of NFAT pathway activation. Luminescence was measured after 20 hours of incubation at 37˚C with 5% CO2 with a luminometer using the Bio-Glo-TM Luciferase Assay Reagent according to the manufacturer's instructions (Promega). In vivo mAb testing using a Syrian hamster model KU LEUVEN R&D has developed and validated a SARS-CoV-2 Syrian Golden hamster infection model 41, 44. The SARS-CoV-2 Wuhan (BetaCov/Belgium/GHB-03021/2020-EPI ISL 109 407976|2020-02-03) and B. 1. 351 (hCoV105 19/Belgium/rega-1920/2021; EPI_ISL_896474, 2021-01-11) isolates used in this study, were recovered from nasopharyngeal swabs taken from a RT-qPCR confirmed asymptomatic patient who returned from Wuhan, China in February 2020 and from a patient with respiratory symptoms returned to Belgium in January 2021, respectively.
PEG 8000, 0. 1 M Tris (base)/bicine pH 8. 5, 0. 02 M Sodium chloride, 0. 01 M MES pH 6 and 3% (v/v) Jeffamine ED-2003. Crystals were flash frozen in liquid nitrogen. Data were collected at Beamline 9-2 of the Stanford Synchrotron Radiation Lightsource facility in Stanford, CA. Data were processed with the XDS software package 61 for a final dataset of 2. 65 Å in space group P21.
PEG 8000, 0. 1 M Tris (base)/bicine pH 8. 5, 0. 02 M Sodium chloride, 0. 01 M MES pH 6 and 3% (v/v) Jeffamine ED-2003. Crystals were flash frozen in liquid nitrogen. Data were collected at Beamline 9-2 of the Stanford Synchrotron Radiation Lightsource facility in Stanford, CA. Data were processed with the XDS software package 61 for a final dataset of 2. 65 Å in space group P21. The RBD-S2X259-S2H97 Fab complex structure was solved by molecular replacement using phaser 62 from a starting model consisting of SARS-CoV-2 RBD (PDB ID: 7JX3) and homology models of the S2X259 and S2H97 Fabs built using the Molecular Operating Environment (MOE) software package from the Chemical Computing Group (https://www. chemcomp. com ). Several subsequent rounds of model building and refinement were performed using Coot 63 , ISOLDE 64 , Refmac5 65 , and MOE ( https://www.
In the neighbourhood of i-TED two C-fibre C 6 D 6 detectors were set up. The latter detectors are used as reference along the whole work. The two C 6 D 6 detectors were oriented at an angle of 60 • with the vertical, with their faces pointing to the capture sample placed at a distance of 10 cm. The two photomultiplier tube (PMT) readout channels are connected to the n TOF digital acquisition system  [19]. The acquisition system of i-TED is described in Sec. 2. 2. The i-TED data was stored in a PC located in the experimental hall and remotely controlled via Ethernet connection. Samples of gold and iron were placed in the neutron beam for these PoC measurements. Gold is commonly employed as reference for the normalization of the capture yield via the Saturated Resonance method  [2]. However, in this case gold was only measured to validate the TOF calibration and response of both detection systems.
The simulations have been made with the Geant4 [29] toolkit (v10. 6) using the officially released QGSP BIC HP Physics List  [30]  which contains the standard Electromagnetic Package. The general view of each of the i-TED modules as implemented in Geant4 is displayed in Fig. 10 (a). A critical parameter of this system is the distance between the two detection planes, so-called focal distance, which was set to 15 mm for all the simulations in this work. According to a previous work  [16] , this value ensures a good balance between efficiency an angular resolution. Each of the four i-TED modules was placed at a different distance (25, 50, 75 and 100 mm) from the sample to study its impact in the signal-to-background ratio (SBR). A realistic study of the expected background suppression using i-TED requires an accurate modelling of both the capture cascades and the background events.
Machine Learning: algorithms and methodology The background suppression with i-TED can be simplified as a problem of binary classification in the framework of ML techniques. In this work, the performance of several state-of-the-art ML algorithms included in Scikitlearn Python module  [34]  has been evaluated: k-Nearest neighbors, Logistic Regression, Support Vector Classifier (SVC), Gaussian Naive Bayes (NB), Random Forest, XG-Boost Classifier and Keras. To train the ML algorithms in the discrimination of capture and background events, the same number (50k -100k) of (n,γ) and background events were selected from the MC output individually for each i-TED module. Each MC event (S&A coincidence) contains the same nine features determined with the detector in a real measurement: 3D coordinates of the γ-ray interactions in the two PSDs  (6) , energy deposited in the S-and A-planes  (2)  and time difference between both interactions in scatter and absorber  (1).
ML-based background suppression on i-TED prototype data The same XGBoost classifier described in Sec. 3. 2. 1 has been tested using experimental data measured during the commissioning of the aforedescribed i-TED prototype at n TOF-EAR1. In a first step, our aim is to demonstrate experimentally the higher (n,γ) efficiency and the similar background suppression of ML-and imaging-based methods. The training of the ML algorithm was carried out using two separate measurements. The same number (8 kEvents) of capture and background events were extracted from measurements of 197 Au and nat Pb samples, respectively. For the case of 197 Au, events were selected from the 4. 9 eV saturated resonance, where the background represents less than 0. 1% of the events. On the other hand, neutron capture in Pb is negligible and this sample can be considered a pure neutron and γ-ray scatter, both contributing to the overall background at n TOF  [12].
Acknowledgment This work has been carried out in the framework of a project funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Consolidator Grant  project  HYMNS , with grant agreement n • 681740). The authors acknowledge support from the  Spanish Ministerio de Ciencia e Innovación  under grants  PID2019-104714GB-C21 ,  FPA2017-83946-C2-1-P ,  FIS2015-71688-ERC  and  CSIC  for funding PIE-201750I26. We would like to thank the crew at the  Electronics Laboratory of IFIC , in particular  Manuel Lopez Redondo  and  Jorge Nácher Arándiga  for their excellent and efficient work.
Study context Data for this study were collected from an international business school based in France. The school offers a bachelor's degree in international management, as well as the Grande Ecole Program (PGE), which offers several opportunities: apprenticeships, study abroad programs, and international internships. The PGE consists of two periods: the fundamental cycle (ESC1), which lasts 12 months, and the entrepreneurial cycle (ESC2 ESC3), which lasts 24 to 36 months. The school is characterized by its international profile and cultural diversity, the latter of which is due to its partnerships and exchanges with universities from North and Central Africa. Among its student population, 51 % are French and 49% are from different nationalities belonging to Africa, Asia, and Europe. Moreover, 55% of the school faculty is multinational.
Data analysis Data reliability and validity was analyzed using the statistical software package SPSS; the model then was measured using a covariance-based approach to structural equation modeling (CB-SEM) in AMOS v26, which is an extension to SPSS. SEM has been widely relied upon in business research, where complex relationships comprising of latent variables are tested for their interaction with the main variable  (Byrne, 2010; Arbuckle, 2012; Sarstedt, Ringle, Smith, Reams, & Hair, 2014). In the current study, SEM was used in two steps: first, the fit of the measurement model was assessed (confirmatory factor analysis), then the hypothesized paths of the proposed structural model were assessed  (Kline, 2005; Byrne, 2010; Hair et al. , 2014). Common method bias might affect the empirical results and conclusions in cross-sectional studies.
Structural model Following the confirmation of the measurement model, the structural model was measured using AMOS v26 to validate the hypothesized paths. The structural model was assessed, thoroughly explaining the path coefficients and squared multiple correlations (R 2 ). The overall goodness-of-fit statistics (see Table  4 ) showed the structural model fits the data quite well: x2/df = 2. 135, p = 0. 000, SRMR = 0. 054, RMSEA = 0. 05 (0. 05, 0. 06), TLI = 0. 95, CFI = 0. 96  (Hair et al. , 2014). Having assessed the fit indices for the measurement model and the structural model, the estimated coefficients for the causal relationships between the constructs in the model were examined next.
As a large number of genes were controlled by Crp-or GR-related effect, both factors were regarded as exhibiting global regulations. In total, 468 genes were identified to be activated (396 genes) or repressed (72 genes) by Crp directly or indirectly (Fig. 3C , Data S2). These Crp-controlled genes included the entire gene list in each of the transcriptional unites of the 33 polycistrons, as exemplified by all the genes in the lsrACDBFG operon and in the ddpXABCDF operon, suggesting the high quality of the global data that we obtained and the high sensitivity of the selection criteria we applied. Approximately half of the 468 genes belonged to the previously reported Crp regulon, either being documented in public databases  (Gama-Castro, et al. , 2016)  or being identified by the in vitro ROMA assay  (Zheng, et al.
Heatmap visualization and KEGG pathway-enriched analysis Both tools for heatmap visualization and KEGG pathwayenriched analysis of the transcriptome data were conducted with OmicShare online platform (www. omicshare. c om/tools) (GENE DENOVO). For heatmap, ordered transcriptional levels (RPKM) of selected genes were visualized by the 'Heatmap' tool, normalized using z-score at row, and clustered if necessary. For pathway analysis, gene sequences of NCM3722 (NZ_CP011495. 1 and NZ_CP011496. 1) were locally blast with sequences from KEGG GENES Database (www. kegg. jp/kegg/genes. html) to obtain matched genes in the database. The matched genes were next mapped with KEGG PATHWAY Database (www. kegg. jp/kegg/pathway. html) to obtain the relationship of gene-pathway. Our selected genes in each subgroup were categorized and enriched by the 'Pathway Enrichment Analysis' tool, using a gene-pathway corresponding document as the background set.
5 × TBE (For Crp), 1 × TG buffer [48 mM Tris, 39 mM glycine (pH 8. 9)] (For Cra), or 1 × running buffer [20 mM BisTris/borate (pH 7. 0)] (For Fur) at 120 V for 60 min (For Crp and Cra) or 220 V for 40 min (For Fur). After electrophoresis, the gel was stained in corresponding buffer with GelRed dye and visualized by Gel Doc XR+ (Bio-Rad). Two to six independent repeats were performed for each target gene. Prediction of Crp, Cra and Fur binding sites in silico were conducted with MEME and FIMO program in MEME suite 5. 1. 0 (Bailey et al. , 2009). Consensus of Crp and Cra binding sequences were generated by WebLogo 3  (Schneider and Stephens, 1990).
5 h, washed 3 times with TBST, subsequently blotting with the goat anti-rabbit IgG and goat anti-mouse IgG HRP-conjugated secondary antibody (Abmart, Berkeley Heights, NJ, USA) for 1. 5 h, and washed three times with TBST. Following a treatment with Super ECL Detection Reagent (Yeasen, Shanghai, China), the membranes were exposed in Gel Doc XR+ (Bio-Rad, Hercules, CA, USA) to obtain images of target proteins. Signals corresponding to each protein were quantified with ImageJ (NIH) and presented as a relative amount to the level of that in Glc. Results are averages from two independent assays. 5 0 -RACE assay The 5 0 end of yobD mRNA transcript was identified as described previously following the instruction of SMAR-Ter RACE 5 0 /3 0 Kit (Clontech)  (Li, et al. , 2019)  with RACE-ready cDNA from 1.
ª 2021 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology. , Microbial Biotechnology, 14, 1084-1106 of these 102 genes belonged to the Cra regulon, being identified either by global methods as in vitro SELEX system and in vivo ChIP-exo technique, or by individual evidences documented in the public database.
The survey This study used data from the Study on global AGEing and adult health (SAGE). SAGE data can be downloaded at http://www. who. int/healthinfo/sage/en/. Six countries (i. e. , China, Ghana, India, Mexico, Russia, and South Africa) participated in the survey in 2007-2010 and, at the time of the survey, all countries were LMICs. More details on the SAGE methodology have been previously published  [23]. To summarize, nationally representative samples were obtained using a multistage clustered sampling of households. Samples included individuals aged ≥18 years with an oversampling of the age group ≥50 years. Face-to-face interviews were conducted by trained staff, and questionnaires were translated following standard procedures to allow comparability between countries. The survey response rate ranged from 53% in Mexico to 93% in China.
Strengths and limitations The strengths of this study are the large sample size and the use of nationally representative data from six LMICs. However, the study results should be interpreted in light of the limitations. First, perceived stress was assessed using two questions only, and a more complex measure may have allowed more detailed statistical analyses. Second, this was a cross-sectional study, and it was thus not possible to determine the causality or the temporality of the association between far visual impairment and perceived stress. Finally, we only had information on visual impairment and not individual eye diseases that led to visual impairment. Future studies should assess how different eye diseases are associated with perceived stress.
Measurements All patient and visit variables were retrieved via structured data forms from the ED electronic medical records (EMR). During ED registration, families were asked to provide the race and ethnicity of their child; families could choose to decline to answer. We classified race/ethnicity as "White Non-Hispanic, " "Black non-Hispanic, " "Hispanic, " and "other" (including Asian, American Indian, Alaska Native, Native Hawaiian and other Pacific Islander, and self-labeled "other"; these categories were aggregated due to low numbers in the ED. ) Pain measurements were performed by a nurse in triage and later by nurses or clinical assistants while in the emergency department. To measure pain in children ages 2-4, the FLACC Scale (Face, Legs, Activity, Cry Consolability) was utilized  (21).
Analyses Both cohorts were analyzed independently with the same analytic steps. Data were reported as means for continuous variables and proportions for categorical variables. We performed bivariate analyses to study the association of race/ethnicity on any analgesic treatment, only non-opioid analgesia and opioid analgesia overall as well as stratified by pain score. We also performed bivariate analyses to study the association of the covariates with opioid analgesia and any analgesic treatment by using chi square for categorial variables and t-test for continuous variables. Finally, we performed multivariable logistic regressions to adjust for confounding by the covariates listed above. Results from the multivariable model included adjusted odds ratios (aOR). Patients with missing data were excluded from a Each visit for an acute limb fracture was considered independently; if a given patient had more than one fracture during the time period, the receipt of medications would be considered separately.