dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). High-mannose-only YU2 pseudoviruses were produced in HEK-293T cells treated with 25 μM kifunensine (Sigma-Aldrich). Pseudovirus-containing culture supernatants were harvested 2 d after transfection, and the 50% tissue culture infectious dose (TCID 50 ) of each preparation was determined using TZM-bl cells (NIH AIDS Reagent Program) as previously described  (Li et al. , 2005; Sarzotti-Kelsoe et al. , 2014). Neutralization experiments were performed twice and included 10-1074 IgG or IgA as a positive control. Neutralization of HIV-1 strains in the cross-clade reference and extended virus panels  (deCamp et al. , 2013; Scheid et al. , 2011)  was measured using TZM-bl cells as previously described  (Li et al. , 2005). IC 50 values were calculated using Prism software (v6. 0a) by fitting duplicate values using the five-parameter sigmoidal dose-response model.
ADCP assay Biotinylated purified YU2 gp140-F proteins were incubated for 1 h at room temperature with 1 µm NeutrAvidin FITC-labeled microspheres (Invitrogen, Thermo Fisher Scientific; 1. 2 µg for 3 μl of microspheres). Conjugated beads were then washed in 2% bovine serum albumin-PBS and diluted 1:10 in PBS. Purified IgG or IgA monoclonal antibodies (1. 25 µg/ml final concentration) were mixed with gp140-F-conjugated beads (1:2,500 final dilution) and incubated 1 h at 37°C under gentle agitation. Mixtures were then incubated with 1. 8 × 10 5 purified monocytes for 2 h at 37°C under gentle agitation. Cells were washed with PBS and fixed with 1% paraformadehyde-PBS. Data were acquired using a BD LSR Fortessa cytometer (BD) and analyzed using FlowJo software (v10. 7. 1). Phagocytic scores (PSs) were calculated as previously described  (Tay et al.
5% bovine serum albumin, and 2 mM EDTA) for 30 min at 37°C, washed, and incubated for 30 min at 4°C with AF647-conjugated anti-human IgG antibodies (1:400 dilution; Life Technologies) or with mouse anti-human IgA HB200 antibodies (30 µg/ml final;  Lorin and Mouquet, 2015)  for 30 min and 1 h, respectively, at 4°C. Binding reactions using HB200 were revealed after washing using 1:400-diluted Alexa Fluor 647conjugated goat anti-mouse antibodies (Thermo Fisher Scientific) for 30 min at 4°C. Cells were then fixed with 4% paraformaldehyde and stained for intracellular Gag as previously described  (Bruel et al. , 2016; Bruel et al. , 2017). Data were acquired using an Attune Nxt instrument (Life Technologies, Thermo Fisher Scientific) and analyzed using FlowJo software (v10. 7. 1).
, 2016) , and LIVE-DEAD fixable aqua dead cell marker (1:1,000 in PBS; Life Technologies, Thermo Fisher Scientific) was added for 20 min at 4°C before fixation. Data were acquired using an Attune Nxt instrument (Life Technologies, Thermo Fisher Scientific) and analyzed using FlowJo software (v10. 7. 1). The frequencies of Gag + cells among Far-Red + cells were determined. The percentage of ADCC was calculated using the following formula: 100 × (% of Gag + target cells plus NK without antibody -% of Gag + target cells plus effector with antibody)/(% of Gag + target cells plus NK without antibody). Monocyte-and neutrophil-based ADCC assays AD8-infected CEM. NKR-CCR5 cells were first stained using the CellTrace Far Red cell proliferation kit (Invitrogen, Thermo Fisher Scientific) for 30 min at 37°C.
4 × 10 4 target cells were then incubated for 5 min at room temperature with antibodies (at 15 µg/ml final concentration) and mixed 1:1 with 2, 4, or 8 × 10 5 human monocytes and neutrophils. Following a brief spindown to promote cell contacts, mixtures were incubated for 4 h at 37°C. Cells were fixed and stained for intracellular Gag using KC57-RD1 antibody (diluted 1:500; Beckman Coulter). Data were acquired using an Attune Nxt instrument (Life Technologies, Thermo Fisher Scientific) and analyzed using FlowJo software (v10. 7. 1).
Cryo-EM data collection, refinement, and modeling Single-particle cryo-EM data of the SOSIP-7-269-3BNC117 complex were acquired on a Titan Krios transmission electron microscope (Thermo Fisher Scientific) operating at 300 kV, using EPU automated image acquisition software (Thermo Fisher Scientific). Videos were collected on a Gatan K3 direct electron detector operating in Counted Super Resolution mode at a nominal magnification of 105,000× (0. 85 Å/pixel) using a defocus range of -1. 0 to -3. 0 µm. Movies were collected over a 3-s exposure and a total dose of ∼45 e-/ Å2. Data for the SOSIP-7-155 and SOSIP-7-176 complexes were collected on a Glacios electron microscope (Thermo Fisher Scientific) operated at 200 kV and equipped with a Falcon 3EC direct electron detector. Movies were collected in linear mode using a pixel size of 2.
0 Å. The defocus range was -1. 0 to -3. 0 μm, and each movie contained 38 frames with a dose of ∼60 e-/ Å2 and a 1. 67-s exposure time. Data were processed by using Relion-3. 0 (Nakane et al. , 2018) , running on a Linux workstation equipped with four GPUs following the workflow shown in Data S1 and Data S2. In brief, row movies were processed using MotionCor2  (Zheng et al. , 2017) , with a five-by-five patch-based alignment, keeping all frame and dose-weighting up to the total exposure. The contrast transfer functions (CTFs) of the dose-weighted images were determined using CTFfind-4. 1  (Rohou and Grigorieff, 2015). The images having a CTF maximal resolution worse than 4 Å (Titan Krios images) or 7-8 Å (Glacios images) were discarded. Images were then manually inspected, and those presenting important ice contamination or large carbon area were also removed.
0-Å resolution map in which both chains of the Fab variable region were well defined. A model for the V H and V L domains of 7-269 obtained from the Phyre2 server  (Kelley et al. , 2015)  was fitted into the locally refined cryo-EM density map using UCSF Chimera  (Pettersen et al. , 2004). This initial model was refined into the map using one round of rigid body morphing and simulated annealing in Phenix  (Liebschner et al. , 2019) , followed by iterative rounds of manual building and real-space and B-factor refinement in Coot  (Emsley et al. , 2010)  and Phenix  (Liebschner et al. , 2019) , with secondary structure restraints. The 7-269 variable region structure was then used, along with the SOSIP-3BNC117 structure (PDB accession no. 5V8M), as initial coordinates that were fitted into the EM map of the trimeric complex (nonuniform refinement) using UCSF Chimera  (Pettersen et al.
[PDB accession no. 5CEZ]) or an extended or short CDR H 3 (438-B11 [PDB accession no. 6UUH], BG18 [PDB accession no. 6CH7], and 2G12 [PDB accession no. 6OZC]). (G) Structural superposition of the variable domains from 7-269 and anti-HIV bNAbs harboring an intra-CDR H 3 disulfide bond (yellow sticks) and targeting the glycans-V3 (438-B11 [PDB accession no. 6UUH]), glycans-V1/2 (CAP256-VR26. 03 [PDB accession no. 4OD1]), and the CD4bs (45-VRC01. H5. F-117225 [PDB accession no. 4S1S]). (H) ELISA graphs comparing the Env binding of 7-269 and associated CDR H 3 cysteine mutant antibodies. Means ± SD of duplicate OD 405nm values are shown (representative of two independent experiments). (I) Heatmap comparing the neutralizing activity of 7-269 and associated CDR H 3 cysteine mutant antibodies as measured in the TZM-bl assay.
Acknowledgments We are grateful to all participants who consented to be part of this study. We thank  Arlene M. Hurley  (the  Rockefeller University Hospital ) and  Nathalie Jolly  from the clinical core of the  Center for Translational Sciences (Institut Pasteur)  for their assistance on the preparation of the BHUANTIVIH protocol,  Sandrine Schmutz  and  Sophie Novault  ( Unité de Technologie et Service -Cytométrie et Biomarqueurs, Institut Pasteur ) for their help with single-cell sorting, and  Laurence Ma (Biomics Platform, Institut Pasteur)  for Illumina sequencing. We thank  Natalia T. Freund  ( Tel Aviv University ) for kindly providing BG8/ BG18 expression vectors and  Rogier W. Sanders  ( University of Amsterdam ) and  John P. Moore  ( Weill Medical College of Cornell University ) for kindly providing BG505 SOSIP.
664expression vectors. We also thank the  Core Facility Cryo-electron Microscopy and Tomography of Central European Institute of Technology Masaryk University  for the initial support to the project, and the  NanoImaging Core at Institut Pasteur  for support with sample preparation and image acquisition of the data presented in this paper. The NanoImaging Core was created with the help of a grant from the  French Government 's  Investissements d'Avenir program  ( EQUIPEX CACSICE -Centre d'analyse de systèmes complexes dans les environnements complexes ,  ANR-11-EQPX-0008 ). We thank the  NIH AIDS Reagent Program (Division of AIDS, National Institute of Allergy and Infectious Diseases, NIH ) for contributing to reagents and the  Agence National de Recherche sur le SIDA et les hépatites virales (ANRS)  for an equipment grant support.
The Biomics platform is supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA. I. Fernández  and  C. Planchais  were recipients of an  ANRS postdoctoral fellowship. The J. P. Di Santo  laboratory received support from the  Vaccine Research Institute (Créteil, France). H. Mouquet received core grants from the  Institut Pasteur ,  Institut national de la santé et de la recherche médicale , and the  Milieu Intérieur Program  ( ANR-10-LABX-69-01 ). This work was supported by the  European Research Council Seventh Framework Program  ( ERC-2013-StG 337146 ), and by  Gilead Sciences HIV Cure Grants  (# 00397 ). Author contributions:  H. Mouquet  conceived and supervised the study. M. Braibant,  M. S. Seaman ,  J. P. Di Santo ,  F. A. Rey , and  H. Mouquet  supervised the experiments. V.
2020 ; Holt 2020). First, while jSDMs have been applied to a large number of species presence/absence datasets  (Norberg et al. 2019 ; Wilkinson et al. 2019 ; Wilkinson et al. 2020) , simulation studies showed that co-occurence networks inferred from such data does not necessarily provide evidence for species interactions  (Sander et al. 2017 ; Dormann et al. 2018 ; Blanchet et al. 2020)  and only inform about spatial and temporal associations between species  (Keil et al. 2021). Some authors speculated that jSDMs applied to abundance data -instead of presence/absence data -are likely to provide a better proxy for biotic interactions  (Blanchet et al. 2020 ; Momal et al. 2020). Accordingly, jSDM have progressively been extended and applied to abundance data  (Hui 2016 ; Ovaskainen et al. 2017b ; Chiquet et al.
Materials & Methods We used the HMSC (Hierarchical Modeling of Species Communities) framework applied to the long-term REBENT coastal monitoring dataset (rebent. ifremer. fr). In the following subsections, we sequentially describe Fig. 1 :  (1) the HMSC framework, (2) the data used in this study, (3) data splitting between training and testing sets to assess the explanatory and predictive powers of models, respectively, (4) the rationales for the suite of alternative models considered and, (5) the performance metrics used to compare models.
Functional traits and phylogeny data We collated species-specific information related to functional traits and phylogeny for inclusion in different models. These data were particularly well resolved for the polychaete community which therefore constitutes the main object of inference. Polychaeta is a taxonomic group composed of numerous species exhibiting diverse lifestyles  (Jumars et al. 2015)  that can be used to monitor the health of benthic habitats  (Giangrande et al. 2005). The polychaete traits data, which was available for the 99 polychaete species present in the training set, includes 11 fuzzy-coded traits for a total of 41 modalities  (Boyé et al. 2019). Prior to jSDM fitting, the dimensionality of the trait matrix was reduced using a fuzzy-PCA with the fpca function from the ade4 R package  (Thioulouse et al.
2018). The first three axes, which account for 59% of the total variance of the trait matrix, were included in the model as synthetic traits data (Fig. 1 ). The first axis distinguishes mobile predatory species from sessile microphages; the second axis differentiates semelparous species from iteroparous species; and, the third axis separates burrowers from tube-dwellers (Fig. S5 ). In complement to the traits information available for the 99 polychaete species of interest, we retrieved their taxonomic classification through the WoRMS database ((www. marinespecies. org; assessed in january 2020) and used this information as a proxy for phylogenetic relationships (Fig. 1  ;  Ricotta et al. (2012)  ;  Ovaskainen & Abrego (2020) ). Phylogenetic distances between polychaete species were then estimated using the ape R package  (Paradis & Schliep 2019).
Assessing model performance and interpretability In order to independently assess models' predictive performance, we splitted the dataset into a train and a test set. The training dataset includes 180 sampling units defined as unique combinations of years (varies between six and nine depending on sites), sites (21) and two habitats (Fig. S1 ). From this dataset, that originally contained 519 species, we removed the species that occurred less than four times across the 180 observational units to avoid convergence issues and poor model inference, leading to the removal of 241 species. The remaining 278 species encompassed the 99 polychaete species that made up the target community and the 142 non-target species that were included in the WhC model. The test dataset was composed of 35 sampling units related to all surveys over a 9-year period at two specific sites , where both habitats (i.
e. seagrass and bare sand) occur. Beyond the presence of both habitats, these two sites were also chosen because they occur in environmental conditions that can be considered average at the scale of the region (thereby limiting extrapolation of the model;  Boyé et al. (2017)  ;  Boyé et al. (2022)  ; Toumi Chirine (n. d. )) while still harbouring different communities, representative of the known diversity gradient across the region  (Boyé et al. 2017 ; Toumi Chirine n. d. ). To investigate jSDM's performance, models were evaluated using a set of complementary metrics to evaluate both their explanatory (predictions compared to observations of the train dataset) and predictive (predictions compared to observations of the test dataset) powers  (Wilkinson et al. 2020). To assess models' performance, both overall (i.
e. , across all species) and at individual species level, we used AUC and root mean squared errors (RMSE) for presence/absence and abundance models, respectively. For the "whole community" model that most improved predictive power (see results), we further explored species-specific gain in explanatory power by examining potential correlations between (i) RMSE and the proportion of presences and (ii) RMSE and average abundance using the Kendall rank correlation coefficient. While the AUC and the RMSE provide estimates of model performance, either overall or for individual species, these measures only partially capture model accuracy (or performance) at the community scale. Hence, we also explored differences between observed and predicted community composition (both for the train and test datasets) by decomposing the total beta diversity (using the Sørensen index) into species turnover and nestedness using the betapart.
temp function from the betapart R package  (Baselga 2010 ; Baselga et al. 2022). For abundance models, predictions were transformed to presence/absence before computing beta diversity (i. e. all non-zero abundance predictions were considered as presences). Thus, this framework assigns a total beta diversity of zero to a model predicting the exact observed community, whereas a model predicting a completely different community compared to observations is associated with a total beta diversity of one. Moreover, using  Baselga et al. (2022) 's framework, we decomposed beta diversity (i. e. predicted error in community composition) according to two components: (1) turnover, if model correctly estimates observed species richness but mispredicts species identity and (2) nestedness, if model correctly predicts the identity of observed species but omits some.
Community level On the training set, the median Sørensen dissimilarity index, which ranged from 0. 36 to 0. 38 across all models (both presence/absence and abundance), suggests that predicted communities are relatively similar to observed communities (Fig. S8  and Fig. S9 ). Errors were equally distributed between turnover and nestedness (Fig. S8  and Fig. S9 ). However, relative to observed communities in the test data set, abundance models predictions presented a median Sørensen dissimilarity of 0. 65 while dissimilarity reached 0. 72 for presence/absence models (Fig. S8  and Fig. S9 ). Greater dissimilarity between predicted and observed communities in the test dataset relative to the training dataset is a direct consequence of models' limited predictive power at the species level (see above and Fig.
Acknowledgment The authors are very grateful to  Marion Maguer  and  Vincet Le Garrec  who conducted the fieldwork and laboratory analysis that made this study possible, as well as to all students, technicians, engineers and Ph. D. that have been involved in this long-term effort. This study was supported by the  REBENT programme  coordinated by  Sandrine Derrien   (MNHN)  and its funding partners ( Agence de l'eau Loire-Bretagne, Région Bretagne ,  DREAL Bretagne). The authors would also like to acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage and computational resources. https://pcdm. ifremer. fr. MPM is the recipient of an  ANR  early career grant  ANR-21-CE02-0006.
Violet et al. 2022 | Essential ingredients in Joint Species Distribution Models bioR𝜒iv | 18 of 27could indeed vary depending on robustness of non-target species monitoring data (e. g. detection issues), their role within the ecosystem (e. g. engineer species are likely more influential on local communities than rare transient species) or the process shaping the target assemblage (e. g. when the target assemblage is mostly under the influence of abiotic factors, then adding other species will have limited influence on model performance). Furthermore, how many non-target species should be included warrants further investigations. In the future, simulated datasets, overcoming limitations related to real world datasets (DiRenzo et al. 2022) , could be used to more systematically assess how and to which extent jSDM performance is affected when increasing the number of non-target species and whether the functional or the trophic position of these species matters in this context.
In the urban environment, gas such as nitrogen dioxide NO2, and particles impose adverse impacts on pedestrians' health. The conventional computational fluid dynamics (CFD) methods that regard pollutant as passive scalar cannot reproduce the formation of secondary pollutants, such as NO2 and secondary inorganic and organic aerosols, leading to uncertain prediction. In this study, SSH-Aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using unsteady Reynolds-averaged Navier-Stokes equations (RANS) model. The simulated concentrations of NO2, PM10 and black carbon are compared with field measurements on a street of Greater Paris.
concentrations  (Kim, 2019). To simulate pollutant concentrations considering both transport and transformation, many studies have coupled air-quality models with gas-phase chemistry and aerosol modules and achieved chemical transport from a regional scale (~100 km)  (Sartelet et al. , 2007)  to a street scale  (Lugon et al. , 2021b). However, few models can simultaneously represent detailed particle dispersion in a complicated urban flow field considering secondary aerosol formation. For the recent development and application of CFD-chemistry coupling model,  Kurppa et al. (2019)  implemented a sectional aerosol module into large eddy simulation (LES), and conducted a particle dispersion simulation on a neighborhood scale. Gao et al. (2022)  employed the same model to examine the dispersion of cooking-generated aerosols in an urban street canyon.
The time and space discretizations of velocity, pressure and other scalars in all transport equations are realized through a centred scheme and a fractional step scheme  (Archambeau et al. , 2004). For both CFD software, the dry deposition schemes for gas and particle are added to the transport equations using volume sink terms based on  Zhang et al. (2003)  and  Zhang et al. (2001) , respectively. The details of the implementation are provided in Appendix A. SSH-Aerosol  (Sartelet et al. , 2020)  is a modular box model that simulates the evolution of not only gas concentrations but also the mass and number concentrations of primary and secondary particles. In SSH-Aerosol 112 gas species and 40 particle species are considered. The particle compounds are dust, black carbon, inorganics (sodium, sulphate, ammonium, nitrate and chloride), primary organic aerosol (POA) and secondary organic aerosol (SOA).
Three main processes involved in aerosol dynamics (coagulation, condensation /evaporation and nucleation) are included. The particle size distribution is modelled using a sectional size distribution. In this study, nucleation was not considered and six particle size sections were employed with bound diameters of 0. 01, 0. 04, 0. 16, The coupling between CFD and SSH-aerosol was achieved by using the application program interface (API) of SSH-aerosol. The gas and particle concentrations were initialized in CFD and are transported in the domain for each time step. For each grid volume cell, these transported concentrations, as well as meteorological parameters, such as temperature and humidity, are then sent to SSH-aerosol to advance one time step of gaseous chemistry and aerosol dynamics. Once the SSH-aerosol calculation was completed, the concentrations were sent back to the CFD for the next time step.
Conclusions Particles in urban environment impose adverse impacts on pedestrians' health. Conventional CFD methods regarding particles as passive scalars cannot reproduce the formation of secondary aerosols and may lead to uncertain simulations. Therefore, to increase the simulation accuracy of particle dispersion, we coupled the CFD software simulate the evolution of primary and secondary aerosols. The main processes involved in the aerosol dynamics (coagulation, condensation /evaporation and dry deposition) were considered. We simulated a 12-hour transient dispersion of pollutants from traffic emissions in a street canyon using the unsteady RANS model. The simulation domain and background concentration were based on field measurements. The flow field is based on the WRF model. The particle diameter range (0.
https://doi. org/10. 5194/acp-2022-365 Preprint. Discussion started: 22 June 2022 c Author(s) 2022. CC BY 4. 0 License. (NMSE). These metrics are defined as follows: and CFD simulated concentrations for the compound/species , respectively. The overbar represents the mean value of the entire dataset. The ideal values are 1 for MG, and 0 for FB and NMSE. Previous research has.