dataesr/scientific-paragraphs-categorization huggingface dataset:
ADDITIONAL RESOURCES ClinicalTrials. gov Identifier: MEMO-CoV2, NCT04402892. (legend continued on next page) (D-H) Flow cytometric gating strategies for the analysis and sorting of major B cell populations from PBMCs of convalescent COVID-19 patients. (D) Gating strategy to analyze SARS-CoV-2 S-specific B cell population. Lymphocytes were first gated based on morphology, before exclusion of doublets, dead cells and CD3/CD14 cells. CD19 + cells were next gated before exclusion of CD38 hi plasma cells. CD38 int/-cells were then divided in four quadrants using CD27 and IgD. Upper left quadrant defines memory B cells (MBCs), lower left quadrant double-negative (DN), upper right quadrant CD27 + IgD + cells (MZB) and lower right quadrant naive B cells (excluding CD38 hi transitional). SARS-CoV-2 S-specific B cells were then analyzed within the B cell population of interest using a doublestaining strategy with anti-His antibodies of a His-tagged SARS-CoV-2 S protein.
Figure3. Phenotypic evolution of the SARS-CoV-2 S-specific B cell response up to 6 months post-infection in M-and S-CoV patients (A) UMAP projections of concatenated multi-parametric FACS analysis of CD19 + IgD À cells from all S-CoV (n = 15) and M-CoV (n = 16) patients analyzed over time in our cohort (TableS1). His-tagged labeled SARS-CoV-2 S-specific cells are overlaid as red dots. (B) Unsupervised clustering (FlowSOM) was performed on the concatenated FACS dataset based on IgD, CD71, CD27, CD38, CD11c, CD19, and CD21 fluorescence intensity. 95% density contours for each identified cluster are overlaid in the UMAP projection. (C) Cluster distribution for all analyzed SARS-CoV-2 S-specific cells in identified clusters at indicated time point. Bars indicate mean with SEM. (D) Proportion of SARS-CoV-2 S-specific CD19 + IgD À CD27 + CD38 À MBCs at M0, M3, and M6 in S-CoV and M-CoV patients, as compared to six pre-pandemic healthy donor (HD) controls.
Each dot represents one patient, and bars indicate mean with SEM. Dashed line indicates the mean + 2 SD of the detected frequency of SARS-CoV-2 S-specific MBCs in HD. (E) Proportion of S-specific MBCs over time post symptom onset in S-CoV (black dots, dark blue line) and M-CoV (white dots, light blue line) patients. Lines indicates linear regression (R 2 = 0. 051, p value = 0. 02 for S-CoV, and R 2 = 0. 018, ns, for M-CoV, Pearson correlation). Colored area between dashed lines indicates error bands. Red dots and lines represent data obtained for the four S-CoV patients analyzed as part of our scRNA-seq dataset. (F) Representative dot plot showing CD71 and CD19 expression in CD19 + IgD À CD38 À B cell at indicated time points from two representative S-CoV and M-CoV patients. SARS-CoV-2 S-specific cells are overlaid as red dots.
Red dots and lines represent data obtained for the four S-CoV patients analyzed as part of our scRNA-seq dataset. (F) Representative dot plot showing CD71 and CD19 expression in CD19 + IgD À CD38 À B cell at indicated time points from two representative S-CoV and M-CoV patients. SARS-CoV-2 S-specific cells are overlaid as red dots. Gating strategy for ABCs and classical MBCs according to CD71 expression is further displayed. (G) Proportion of SARS-CoV-2 S-specific cells displaying an ABC (CD19 + CD27 + IgD À CD71 + ) (left) or MBC (CD19 + CD27 + IgD À CD71 low/int ) (right) phenotype at indicated time points. Bars indicate mean with SEM. (H) Proportion of activated MBCs over time post symptom onset in S-CoV (black dots, dark blue line) and M-CoV (white dots, light blue line) patients. Lines represents the linear regression.
(D and E) Histograms showing the relative distribution of mutation numbers in the Ig V H segment from all SARS-CoV-2 S-(D) or RBD-(E) specific clone members (10X Genomics scRNA-seq dataset and single-cell heavy-chain sequencing data) at M0, M3, and M6 as well as from sequences from the literature (Brouwer et al. , 2020; Kreer et al. , 2020; Liu et al. , 2020; Robbiani et al. , 2020; Shi et al. , 2020; Wec et al. , 2020;  Zost et al. , 2020), mainly determined between M0 and M3.
Total number of sequenced cells is indicated in the middle of the pie. (E) Circus plot showing SARS-CoV-2 spike-(gray) or RBD-(light blue) specific clones shared between patients in our dataset or with sequences from the literature. (F) Histogram showing log-normalized counts for both barcoded his-tagged SARS-CoV-2 S proteins in all cells from the M6 time point of our 10X Genomics scRNA-seq dataset (white) and cells in clonal relationship with sequence from single cell culture (dark gray) (G and H) Histograms showing the distribution of mutations in Ig V H for SARS-CoV-2 spike-(G) and RBD-(H) specific clones at M0, M3 and M6 according to the assay of origin. (I) Plot showing the raw percentage of neutralization for single cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6.
ACKNOWLEDGMENTS We thank  Garnett Kelsoe  for providing us with the human cell culture system, together with invaluable advice. We thank the  Prince of Monaco  and the  Government Council  for free donation. We also thank  Sebastien Storck ,  Sandra Weller ,  Cecile Masson , and  Florence Canoui  for their advice and support; we thank the physicians  Constance Guillaud, Frederic Schlemmer ,  Giovanna Melica ,  Elena Fois ,  Henri Guillet ,  Nicolas De Prost ,  Pascal Lim ,  Sebastien Gallien , and  Bertrand Godeau , whose patients were included in this study. This work was supported by a grant from the  Agence Nationale de la Recherche  and the  Fondation pour la Recherche Medicale (ANR ,  MEMO-COV-2 ), by the  Fondation Princesse Grace , and, in part by an  ERC   Advanced Investigator Grant  (B-response).
Leptospirosis, an environmental infectious disease of bacterial origin, is the infectious disease with the highest associated mortality in Seychelles. In small island territories, the occurrence of the disease is spatially heterogeneous and a better understanding of the environmental factors that contribute to the presence of the bacteria would help implement targeted control. The present study aimed at identifying the main environmental parameters correlated with animal reservoirs distribution and Leptospira infection in order to delineate habitats with highest prevalence. We used a previously published dataset produced from a large collection of rodents trapped during the dry and wet seasons in most habitats of Mahé, the main island of Seychelles. A land use/land cover analysis was realized in order to describe the various environments using SPOT-5 images by remote sensing (object-based image analysis).
Landscape Analysis In view of the relatively small size of Mahé Island and the diversity of rodent habitats, this study required a precise and recent land use/land cover dataset, which was not available. The SEAS-OI Station (http://www. seas-oi. org/) provided us with two scenes of high-resolution and near cloud-free SPOT-5 satellite images (© CNES-2013, Distribution Astrium services/Spot images S. A. , France, all rights reserved). Each scene is composed of a panchromatic image at 2. 5 m spatial resolution and a multispectral image at 10 m. They were acquired on 6 December 2012 and 6 January 2013. We preprocessed the raw images (level 1A) with the open source software OrfeoToolBox 5. 8. 0 (OTB, https://www. orfeotoolbox. org/) in order to make the images comparable. In a first step, we orthorectified the images with the orthorectification functionality using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) at 90 m resolution (http://dwtkns.
Meteorological Data The Seychelles Meteorological Authority provided monthly rainfall data measured at 14 ground stations throughout Mahé Island. We spatially interpolated these data using an inverse distance weighting method with ArcGIS software. We calculated for each trapping site the average rainfall over 20 years  and the rainfall of the month of capture. In order to assess the impact of cumulative rainfall on leptospirosis, we also calculated the rainfall for the month preceding the capture, as well as the cumulative rainfall for the two and three previous months.
Cartography We used QGIS software for mapping the results. The Infectious Disease Surveillance Unit of the Ministry of Health provided human leptospirosis data for the 22 districts of Mahé for the year 2015 only. These data do not allow a study of human epidemiology and this was not the purpose of this study, but we used them to contextualize rodent data on a map. We obtained publicly available district population data from the National Bureau of Statistics of Seychelles that were used to calculate and map the incidence of leptospirosis by district. We used a district contour map provided by the Ministry of Habitat, Infrastructure and Land Transport of the Republic of Seychelles.
For the multivariate analysis, we decomposed the dataset into two subdatasets: learning/training data and test data. We used the caret package to perform a logistic regression with k-fold repeated cross-validation (k = 5, repeated 5 times) on the learning set by testing the discrimination of positive/negative rats according to all environmental variables kept for the analysis. The stepwise method, based on the AIC criterion, allowed us to define the optimal model. Finally, we assessed the accuracy of the model from the receiver operating characteristic (ROC) curve or the precision-recall (PR) curve when the dataset is imbalanced, by using the PRROC package  [40].
Ecological Pattern of Rat Distribution The final land use/land cover map includes nine classes (1-Dense Urban, 2-Periurban, 3-Wooded areas, 4-Shrub, 5-Herbaceous vegetation, 6-Mangroves, 7-Bare lands, 8-Wet areas, lakes, 9-Agricultural areas). We recorded 390 observations in the field to validate the classification (Table  2 ). The confusion matrix allowed us to calculate a Kappa index of 0. 88, which is very satisfactory for future uses of this classification. The least accurate class was bare soils due to confusion with the two urban classes that have similar spectral signatures. From this classification, we calculated 43 landscape indices to be used for statistical analyses. We also calculated five meteorological variables to complete the dataset. Both rat species could be trapped over the entire range of altitudes with close averages (149.
Environmental Prediction of Leptospires Carriage First, we kept for the multivariate modeling the eight variables previously identified as relevant: edge density within a 100 m buffer, distances to dense urban areas, distances to peri-urban areas, distances to shrubs, distances to bare lands, average rainfall over the last 20 years, cumulated rainfall during three months before capture and elevation. We applied a logistic regression with stepwise function on the learning dataset (Table  3 ). The edge density and average rainfall variables are the only ones that discriminate leptospirosiscarrying rats from healthy rats (p < 0. 001 and p = 0. 023, respectively). Areas with high edge density and high average rainfall will have more carrier rats. However, the area under the curve (AUC) from PR (since the carrier/healthy and observed/predicted groups are imbalanced) is very low (16.
Environmental Prediction of Leptospires Carriage First, we kept for the multivariate modeling the eight variables previously identified as relevant: edge density within a 100 m buffer, distances to dense urban areas, distances to peri-urban areas, distances to shrubs, distances to bare lands, average rainfall over the last 20 years, cumulated rainfall during three months before capture and elevation. We applied a logistic regression with stepwise function on the learning dataset (Table  3 ). The edge density and average rainfall variables are the only ones that discriminate leptospirosis-carrying rats from healthy rats (p < 0. 001 and p = 0. 023, respectively). Areas with high edge density and high average rainfall will have more carrier rats. However, the area under the curve (AUC) from PR (since the carrier/healthy and observed/predicted groups are imbalanced) is very low (16.
08%) (Figure  6 ). Then this first model could not predict the carriage of leptospires, neither in the test nor in the learning dataset. After removing the average rainfall variable, the cumulated rainfall and edge density remain included in the second model with little change in their estimate (Table  3 ). The variable "distances to dense urban areas" is strongly significant in this second model, whereas it was removed in the first one, contrary to the altitude. The AIC is similar between the two models and the areas under the curve are also comparable for ROC and PR (Figure  6 ). We can assume that average rainfall can be replaced by the distances to dense urban areas. Furthermore, this second model is unable to predict leptospires carriage. By keeping the remaining variables in the second model and adding the "species" variable, the third model is more accurate with a smaller AIC and much greater AUCs, especially for PR AUC (47.
Then, the significant effect of these two variables in the second model is mainly explained by the species effect. Indeed, the vast majority of healthy rats are RRs that were captured in less fragmented areas and at a greater distance from urban areas. Meanwhile, the inclusion of the "species" variable had very few effects on the cumulated rainfall (similar estimates in models 2 and 3). This third model allows the prediction of leptospirosis carriage, with a sensitivity of 28. 57% for a specificity of 99. 41% on the test dataset (or precision = 50% and recall = 28. 57% for F1 = 42. 11%, harmonic/weighted average of precision and recall, in the case of imbalanced data). By keeping the remaining variables in the first model and adding the "species" variable, the fourth model is also more accurate with a smaller AIC and much greater AUCs, especially the PR AUC (43.
07% compared to 16. 08% in the first model) (Table  3  and Figure  6 ). The "species" variable is highly significant (p < 0. 001) and the "edge density" remains significant, but less than for the first model (p = 0. 033 versus p < 0. 001). As for the third model, this fourth one allows the prediction of leptospirosis carriage, even if it remains very low: sensitivity of 21. 43% for a specificity of 98. 23% on the test dataset (or precision = 50% and recall = 21. 43% for F1 = 30%).
Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area.
Introduction Due to its remoteness and extreme weather and sea conditions, the Southern Ocean (SO) is a challenging region in which to carry out biological studies  (Kaiser et al. 2013 , Gutt et al. 2017). It is also one of Earth's regions where we observe the most rapid and dramatic environmental changes in marine ecosystems, motivating the study of these marine communities  (Turner et al. 2014 , Ashton et al. 2017 , Clark et al. 2019). Ecological modelling approaches are now well established and can be used to predict spatial patterns of organisms', populations' and species' distributions and assess their environmental drivers  (Peterson et al. 2011). Based on field observations and experimental datasets, ecological modelling encompasses valuable approaches to helping to analyse biological data and interpolating our knowledge of species distributions in relation to environmental descriptors  (Kennicutt et al.
g. marine invertebrates, fish, seabirds and marine mammals) and to determine species environmental preferences  (Loots et al. 2007 , Pierrat et al. 2012 , Xavier et al. 2016 , Nachtsheim et al. 2017) , to compare ecological niche predictions in response to changing environments  (Basher & Costello 2016 , Gallego et al. 2017 , Guillaumot et al. 2018b , Jerosch et al. 2019)  or to identify diversity hotspots for conservation purposes  (Pinkerton et al. 2010 , Hibberd 2016 , Thiers et al. 2017). However, the quality of ocean-wide models is often limited by the heterogeneity, amount and spatial distribution of data, along with limited temporal and spatial resolutions. For all of these reasons, both modelling methods and model construction should be tested for accuracy and robustness prior to interpretation, and these indicators should be transparently communicated to ensure that model outputs are relevant given the specificities of the datasets used for modelling.
In the present paper, we review the most common methodological issues encountered in species distribution modelling applied to the SO, following the flowchart in Fig. 1. Challenges regarding occurrence and environmental dataset peculiarities are described. The choice of SDM algorithm and procedures to implement and evaluate models are addressed. Based on benthic invertebrate case studies, we stress important precautions to take and pitfalls to avoid during common steps of SDM implementation. Finally, we aim to provide some guidelines with a set of potential methods and original solutions that can be used for improving model performance.
Quality of datasets Environmental datasets: field data Preparing environmental datasets is the first encountered challenge when generating models  (Gutt et al. 2012 , De Broyer et al. 2014). The SO, here defined as waters south of 45°S latitude, covers an extensive area of > 20 million km 2 (Breitzke 2014). Having access to environmental data with good temporal and spatial resolutions at such a broad scale is challenging, an issue common to all broad-scale oceanographic studies  (Robinson et al. 2017). 'Broad scale' is defined here as the entire SO, 'regional scale' as smaller areas of a few hundred square kilometres and 'local scale' as a few square kilometres to square metres  (Gage 2004). Oceanographic data acquisition in the field is strongly conditioned by weather and sea conditions along with the seasonality of polar regions (polar night and dense sea-ice coverage in winter) that prompt recurring gaps in the acquisition of environmental data in the SO.
Environmental datasets: satellite-derived data Satellite-derived data form a significant source of information for SO oceanographic studies. Providing valuable environmental indicators at broad spatial scale, they can give details about continuous and long-term measurements of water masses including sea-ice coverage, extent and duration, sea-surface temperatures and salinities, biogeochemical parameters, sea level, primary production and typical meteorological parameters  (El Mahrad et al. 2020). The accuracy of satellite data, however, should be considered with care, given detection limits, interpolations that reduce the influence of atmospheric particulate scatter and the use of interpolation and gap-filling methods that smooth raw data at broad spatial and temporal scales  (Pope et al. 2017 , Stock et al.
Environmental datasets: access to datasets Environmental data generated at the scale of the entire SO can be accessed for free through different web portals such as the NASA's OceanColor Web (https://oceancolor. gsfc. nasa. gov), where satellite-derived data, averaging different temporal measurements down to 4 km resolution, are available at the scale of the entire SO dating from 2000. These images are post-processed to characterize sea-surface temperature or ocean colour as proxies of surface productivity. The National Oceanic and Atmospheric Administration's (NOAA) data centre (WOCE2013; https://www. nodc. noaa. gov/OC5/woa13/woa13data. html) also makes available post-processed data of ocean temperature, salinity, oxygen concentration and nutrients at different grid formats, down to 0. 25°resolution, averaging over six decades  (from 1955 to 2012).
Bio-ORACLE (https://www. bio-oracle. org) compiles a large panel of marine data layers at 1°spatial resolution for different depth layers and time periods, for the present  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  and the future  (2040 -2050; 2090 -2100 ) (Assis et al. 2018)). Finally, GEBCO (https:// www. gebco. net) is the reference platform for very-high-resolution bathymetry data (∼500 m resolution) of the world's oceans. Several works also make available compilations of these SO datasets dedicated to ecological modelling in the SO; they represent a valuable source of information for starting data preparation and modelling (https://data. aad. gov. au/ metadata/records/Polar_Environmental_Data, https://data. aad. gov. au/metadata/records/fulldisplay/environmental_layers, https://data.
Environmental datasets: spatial and temporal resolutions Most environmental data are accessible through broad-scale maps from the aforementioned data portals and are available with a finest spatial resolution of ∼4 km, if not coarser (https://data. aad. gov. au/metadata/ records/Polar_Environmental_Data, De Broyer et al. 2014, https://data. aad. gov. au/metadata/records/fulldisplay/ environmental_layers, https://data. aad. gov. au/metadata/ records/Environmental_data_Southern_Ocean). This low resolution strongly hampers the precise assessment of relationships between species occurrences and environmental descriptors  (Pittman 2017 , Staveley et al. 2017)  and consequently the accuracy of model predictions  (Connor et al. 2018) , because the relevance of environmental descriptors represents a trade-off between their resolution and their spatial and temporal coverage  (Guisan et al.
2007 , Seo et al. 2009 , Lauzeral et al. 2013 , Vale et al. 2014). It is recommended that the resolution of environmental descriptors used in SDM should be in line with the scale of ecological processes at play and for which species ecophysiological responses show the highest variations, if models are expected to capture most species-environment relationships  (Austin & Van Niel 2011). The published environmental datasets are often averaged over relatively long periods of time (from years to decades for WOCE2013 or Bio-ORACLE). The analysis of inter-annual variations can complement the interpretation of model predictions: the absence of such information does not preclude running models, but this should be kept in mind when it comes to interpreting model outputs  (Guillaumot et al. 2018a). Important environmental variations within a reference time period may not satisfy the equilibrium criterion between species distribution and environmental conditions, which is a strong prerequisite of SDM  (Elith et al.
Environmental datasets: cartographic projections Considering the poles in numerical analyses has long been a source of difficulty in spatial modelling as the convergence of meridians distorts shapes, surfaces, angles or distances towards high latitudes when using standard cylindrical representations such as the Mercator projection  (Deleersnijder et al. 1993 , Eby & Holloway 1994 , Murray 1996 ). Working with conical or azimuthal projections (e. g. polar stereographic system) helps maintain the consistency of angles and shapes and therefore better meets the requirements of SO studies, although areas and distances are progressively distorted when moving away from the pole  (Mulcahy & Clarke 2001). Mapping environmental descriptors and projecting model predictions can be carried out with either square or hexagonal pixels.