dataesr/scientific-paragraphs-categorization huggingface dataset:
2018. 2323 " Gaseous or rocky? Unveiling the nature of small worlds ". This project was supported by the  CNES. M. F. and C. M. P. gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). M. G. is an F. R. S. -FNRS Senior Research  Associate. M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. N. A. W. acknowledges  UKSA  grant  ST/R004838/1. This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional Competitividade e Internacionalizacão  by these grants:  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017  &  POCI-01-0145- FEDER-032113, PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953, A44, page 11 of 14 A&A 674, A44 (2023) PTDC/FIS-AST/28987/2017 & POCI-01-0145-FEDER-028987, O.
D. S. D. is supported in the form of work contract (DL 57/2016/CP1364/CT0004) funded by national funds through FCT. P. M. acknowledges support from STFC research grant number ST/M001040/1. We acknowledge support from the Spanish Ministry of Science and Innovation and the European Regional Development Fund through grants ESP2016-80435-C2-1-R, ESP2016-80435-C2-2-R, PGC2018-098153-B-C33, PGC2018-098153-B-C31, ESP2017-87676-C5-1-R, MDM-2017-0737 Unidad de Excelencia Maria de Maeztu-Centro de Astrobiología (INTA-CSIC), as well as the support of the Generalitat de Catalunya/CERCA programme. The MOC activities have been supported by the ESA contract No. 4000124370. S. H. gratefully acknowledges CNES funding through the grant 837319. S. C. C. B. acknowledges support from FCT through FCT contracts nr.
IF/01312/2014/CP1215/CT0004. S. G. S. acknowledges support from FCT through FCT contract no. CEECIND/00826/2018 and POPH/FSE (EC). A. C. C. and T. W. acknowledge support from STFC consolidated grant numbers ST/R000824/1 and ST/V000861/1, and UKSA grant number ST/R003203/1. V. V. G. is an F. R. S-FNRS Research Associate. X. B. , S. C. , D. G. , M. F. and J. L. acknowledge their role as ESA-appointed CHEOPS science team members. Y. A. and M. J. H. acknowledge the support of the Swiss National Fund under grant 200020_172746. L. Bo. , V. Na. , I. Pa. , G. Pi. , R. Ra. and G. Sc. acknowledge support from CHEOPS ASI-INAF agreement n. 2019-29-HH. 0. NCS acknowledges support from the European Research Council through the grant agreement 101052347 (FIERCE). This work was supported by FCT -Fundação para a Ciência e a Tecnologia through national funds and by FEDER through COMPETE2020 -Programa Operacional Competitividade e Internacionalização by these grants: UIDB/04434/2020; UIDP/04434/2020.
602e -05 , F-value=19. 17 and, RpoB: p=1. 144e -65 , F-value=472. 7). DNA concentrations were also logtransformed and analyzed using glm with a gamma distribution when ratios were not relevant for the analyzes. To analyze reproduction parameters, we used generalized linear models with the function lm, glm or generalized mixed model with glmer (R package Lme4  (Bates et al. 2015) ) according to the distribution of the data and the absence (glm) or presence (glmer) of a random effect. The random effect was added for analyses targeting the offspring of tick females to account for the lack of independence of data points (since measured individuals may be offspring of the same mother). For egg incubation time and time for molting into first nymphal stage, the level of variability between mother was not sufficient to warrant incorporating it as a random effect.
Acknowledgements The authors would like to thank  Olivier Duron ,  Rémi Pereira , and the members of the  UMR ASTRE  for the discussions on molecular biology protocols, as well as  Facundo Muñoz  for the discussions about statistics. All experiments on arthropods have been performed on the Baillarguet insectarium platform (https://doi. org/10. 18167/infrastructure/00001),member of the National Infrastructure EMERG'IN and of the Vectopole Sud network (http://www. vectopole-sud. fr/). The Baillarguet insectarium platform is led by the joint units  Intertryp (IRD, Cirad)  and  ASTRE (Cirad, INRAE).
Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new coming family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Meteo-France. The performance of the models is evaluated on a test dataset independent from the training set both in space and time. We found that the data-driven model's performance is very dependent on the training set. On the one hand, even a simple MLP can significantly outperform a state-of-the-art physical retrieval method, provided the training set is sufficiently large and similar enough to the test set.
Our objective is not to propose a new retrieval method; we, therefore, implement a simple, purely machine-learning-based model. We, nonetheless, conduct a thorough and critical analysis of its performance and compare it with a state-of-the-art retrieval model, Heliosat 4  (Qu et al. , 2017) , operational as part of the Copernicus Atmosphere Monitoring Service (CAMS) radiation service. The paper is organized as follows. In Section 2, we present the data used in this study. In Section 3, we describe our proposed machine-learning-based model. In Section 4, we set the stage for our analysis and present the experimental setups. The results are discussed in Section 5. Discussion and conclusion are given in Section 6.
The Météo-France station of Carpentras, included in the dataset described in Section 2. 2. 1, is also part of the BSRN  (Ohmura et al. , 1998)  and Aeronet networks  (Holben et al. , 1998). As a BSRN station, it provides measurements of 1-min SSI. As an Aeronet station, it provides measurements of spectral aerosol optical depth (AOD). The AOD is only measured under clear-sky conditions; measurements are therefore not regular.
This channel is not used in this study. 2 The details of the instrument at each station can be found at: https://donneespubliques. meteofrance. fr/?fond=contenu&idcontenu = 37 data we use as a reference (Section 2. 2. 1). ozone and water vapor data from CAMS atmosphere services  (Lefèvre et al. , 2013; Gschwind et al. , 2019). In this work, we use its global component, abbreviated CSI for clear-sky irradiance. As it will be used to detect clear-sky instants in Carpentras station, we use 1-minute values.
6 µm, 0. 8 µm, 10. 8 µm} The solar azimuth angle ψ s and the solar elevation γ s , computed using the sg2 python library  (Blanc and Wald, 2012) , are also provided as predictors. They define the topocentric angular position of the Sun. The day of the year and the hour of the day are given as predictors too. Finally, the latitude and longitude, as well as the corresponding altitude are used as predictors. In total, the model has 358 predictors, summarized in Table  2. Each predictor is normalized and centered. These 358 predictors are concatenated in a single 1D vector which is used as input to the neural network.
Performance metrics The SSI estimations produced by the ML model, xML , as well as the SSI estimation from CAMS, xCAMS , are compared with the ground measurements x of SSI from Météo-France stations. Both datasets have a resolution of 1-hour. Three different error metrics are used, namely, the root mean square error RMSE, the mean bias error MBE and the standard deviation of the error SDE: RMSE = 1 n n k=1 (x k -x k ) 2 (1) MBE = 1 n n k=1 (x k -x k ) (2) SDE = 1 n n k=1 (x k -x k -MBE) 2 (3) where n is the number of points and xk ∈ {x M L k , xCAMS k }. MBE measures the accuracy -or bias -of the estimations, SDE their precision, and RMSE is a combination of both. The three metrics are related as follows: RMSE 2 = MBE 2 + SDE 2 The correlation between x and x is also a popular metric. correlation is, however, not appropriate to unveil non-linear relationships between two time-series.
Clear sky detection In Section 5. 2, we focus our analysis on days with a majority of clear-sky instants. It is difficult to accurately detect clearsky instants with mean hourly SSI; we, therefore, restrict the analysis to the station of Carpentras, for which we have minute data (Section 2. 2). We first detect clear-sky minutes with a 1-minute resolution, using the Reno and Hansen algorithm (with a window length of 10 minutes)  (Reno and Hansen, 2016)  implemented in the pvlib python library (F. Holmgren et al. , 2018). We then select days for which 75% of the daytime is detected as clear sky.
Perspectives Third-generation geostationary satellites are already operational above the Untied State (GOES-R) and Japan (Himawari-8), while Meteosat Thid-Generation will soon cover Europe and Africa. These new meteorological satellites have better temporal, spatial and spectral resolutions than their second-generation counterparts. They thus produce a significantly larger amount of data. To treat these data operationally and fully benefit the additional information, deep learning certainly has a critical role to play. However, the solar research community needs to address the limitations of purely statistical models, as revealed in this Variations of hybrid models include the use of machine learning models trained on datasets derived from physical simulations. These models can serve as proxies for parts of existing physical models and can be further fine-tuned on real datasets via transfer learning.
Data availability. The following data sources are accessible online for free: -CAMS estimates of solar surface irradiances and clear sky irradiances can be downloaded from the soda website: https://www. soda-pro. com/help/cams-services/cams-radiation-service/download-europe-volume or with the following pvlib function: https://pvlib-python. readthedocs. io/en/stable/reference/generated/pvlib. iotools. get_cams. html -MSG data is available on the Eumetsat website: https://www. eumetsat. int/access-our-data -Ground irradiance data for Carpentras station can be downloaded from the BSRN website: https://bsrn. awi. de Meteo-France data was generously provided by Meteo-France for research purpose. More information can be found on the Meteo-France public data website: https://donneespubliques. meteofrance.
Acknowledgements The authors would like to thank  Marina Nanni  and IIT clean-room staff for their technical support in primary cultures preparations and microfabrication, respectively, and the  IIT-Neurofacility technical  staff for their excellent support. We also thank  Alberto Perna  for his helping with the Supporting Video, and the  Animal Facility staff of IIT central research labs in Genoa  for their assistance in animal experiments. This work was supported by intramural funds of  Fondazione Istituto Italiano di Tecnologia (IIT)  to D. D. P. T. and L. B. ; O. B. M. was partly supported by the  European Research Executive Agency (REA)  through the  FP7-PEOPLE-2014-IEF ' ASTROCLOCK' (629867) ;  Fondazione CARIPLO  research grant ( 2015-0590 ) and " Ramon y Cajal " contract ( RYC2018-026293-I ).
PEEP was then stepwise reduced in decrements of 3 cmH 2 O. Each PEEP level was maintained for at least 3 min for stabilization of cardiopulmonary variables. The compliance of the respiratory system was measured continuously at each PEEP with a custom-made data acquisition system, which fits the unicompartmental equation of motion to each respiratory cycle  [31]. After this, a second recruitment maneuver was performed, and the PEEP was then set according to the maximal compliance. Between each strategy, a period of 15-30 min was allowed for physiological stabilization. A set of cardiopulmonary measurements were collected following stabilization at each PEEP-setting strategy.
Statistical analysis Number of experiments was calculated considering paired differences and using previous estimates of mean and standard deviations of gas fractions and tidal strains yielding an expected effect size of 1. 7-2. 0  [35]. All data are presented as mean ± SD. Normality was assessed using Shapiro-Wilk test and Q-Q plots. Global respiratory mechanics, hemodynamic, and lung computed tomography derived parameters (aeration, strain, and tidal recruitment summarized as mean, 95th percentile and coefficient of variation) were compared using a paired, two-tailed Student's t-test (Injury versus Baseline; and maximal compliance PEEP versus ARDSNet low-stretch strategy). Vertical distributions of lung computed tomography derived parameters were analyzed using two-way (region and ventilatory strategy) repeatedmeasures ANOVA.
VAR2CSA Model Construction and equilibrium MD simulations The VAR2CSA NF54 strain was considered in this work. A refined and complete model of the core domain (residue IDs 1-1955) was obtained using existing cryo-EM structures of the full VAR2CSA ectodomain (PDB id. 7JGH, 7JGD, and 7JGE  [31] , and 7B52  [21] ), as well as X-ray structures of individual domains (DBL3X: PDB id. 3CML and 3CPZ  [24] , and 3BQI, 3BQK, and 3BQL  [23] ) and of the DBL3X-DBL4ϵ complex (PDB id. 4P1T  [26] ). The structures were combined into a modelling protocol with Modeller 9. 18  [43]  to complete missing residues and loops that were absent in the full ectodomain structures. The modelled segments comprising residues 407-485 and 529-556 were rotated manually after the modelling procedure to prevent their wrapping around the protein.
The CSA dodecamer found in the major CSA binding site of the NF54 structure was extended to 21 sub-units using PyMOL. Subsequently, the system was energy minimized with the steepest descent algorithm applying harmonic restraints on the atoms which were observed in the NF54 structure (harmonic elastic constant was July 31, 2023 3/20 5000 kJ/mol/nm 2 and reference positions for the harmonic potentials were taken from the NF54  [31]  structure). The resulting model displayed a root mean square deviation (RMSD) of 0. 23 Å from NF54 structure (PDB id. 7JGH  [31] ) and of 1. 43 Å from the FCR3 strain structure (PDB id. 7B52  [21] ). The final model was then equilibrated through the use of equilibrium MD simulations, following a protocol of progressive restraint relaxation as explained as follows.
CSA dodecamer Docking at cryptic binding sites and MD relaxation To understand whether force-exposed cryptic binding sites have the ability to accommodate a functional CSA molecule, we docked a CSA dodecamer to the open VAR2CSA molecule using the HADDOCK webserver  [45]. We selected this size of saccharide since a dodecamer is the minimum length needed for CSA binding in the major CSA binding site  [32, 33]. The dodecamer was treated as "ligand" while the VAR2CSA together with the 21mer sugar chain bound to the major binding site were considered as the "receptor" during the docking calculation. The structure of the dodecamer (ligand) was taken from the cryo-EM structure (PDB id. 7JGH  [31] ). For the protein with the bound sugar 21mer (receptor), we selected an open conformation with the N-terminal and C-terminal subregions separated by a distance of 9 nm (see Figure  3D ) from each replica of the pulling simulations, resulting in 20 initial input receptor configurations.
The dodecamer was docked at two different locations of the protein: one corresponding to the minor binding site, which locates at the interface between the DBL2X and the ID2a domain, and the other corresponding to the DBL4ϵ domain portion of the major binding site that got exposed upon application of force. Accordingly, based on the cryo-EM holo structure of VAR2CSA (PDB id. 7JGH  [31] ), the active residues directly involved in the interaction were assumed to be T910, Y911, July 31, 2023 5/20 T912, T913, H949, K952, D968, and K970 for the minor binding site and H1782, I1783, G1784, I1785, I1878, M1879, E1880, K1889, R1890, N1896, N1898, and Y1899 for the DBL4ϵ site. In addition, the first five sugars of the dodecamer were treated as the active region involved in the interaction. No passive residues were considered in the docking calculation.
7). Cells were washed with MACS buffer. RBC were then incubated for 20 min at room temperature and in the dark with Streptavidin-PE (eBioscience). Cells were washed in MACS buffer. RBC were then resuspended in MACS buffer and analyzed for PE-staining by flow cytometry with MACSQuant10 (Miltenyi Biotec) or LSR Fortessa (BD Biosciences). GMFI were obtained by analysis with the FlowJo software V10.
Acknowledgements: We are indebted to the core facilities at  INFINITY INSERM U1291  and  INSERM US006. We are grateful to  Alain Townsend  for providing the IH4-RBD reagent. This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-20-COV8-0004-01  and  ANR-20-CE15-0014-01 ).
As for the delay between last immunization and blood sampling, it was about 6 months (180 days) for all groups except for the severe COVID-19 group, for which the delay was of 9 months (289 days) (Table  1 ). No major difference was observed regarding the demographic data between unvaccinated convalescent patients with mild COVID-19, convalescent vaccinated, and naïve vaccinated individuals. Last, for a more accurate estimation of the rate of infection events for convalescent vaccinated and naïve vaccinated individuals, an intermediate blood sample was collected between vaccination and the 6 months' time point. We monitored the occurrence of breakthrough infections in our cohorts by assessing RBD IgG titers using the bioMerieux Vidas SARS-CoV-2 IgG assay. In our hands, this assay was more reliable than monitoring seroconversion against the N antigen.
Study design For the Covid-Ser cohort, clinical data were recorded by a trained clinical research associate using Clinsight software (version _ Csonline 7. 5. 720. 1). Blood samples were processed and stored at the Centre de Ressource Biologique Neurobiotec, 69500 Bron. Seven hundred and fourteen naïve health care workers (HCWs) infected with SARS-CoV-2, vaccinated against SARS-CoV-2, or both were included in a prospective longitudinal cohort study conducted in Hospices Civils de Lyon (HCL; Lyon, France). Blood sampling was performed 6 months after the end of vaccination schedule or infection. Concerning naïve vaccinated HCWs, the absence of previous SARS-CoV-2 infection was confirmed using the Wantai Ab total assay in the prevaccine sample. The Wantai SARS-CoV-2 total Ab assay, detecting anti-RBD Abs, was selected to detect a previous infection before vaccination on the basis of its better sensitivity in infected individuals as compared with other commercial qualitative assays we had evaluated in a previous study  (70).
SARS-CoV-2 infection before vaccination occurred during the first wave of the pandemic, before the emergence of VOCs. Infected patients were considered to have a mild form of COVID-19 if they were symptomatic but did not require hospitalization. Written informed consent was obtained from all participants; ethics approval was obtained from the national review board for biomedical research in April 2020 (Comité de Protection des Personnes Sud Méditerranée I, Marseille, France; ID RCB 2020-A00932-37), and the study was registered on ClinicalTrials. gov (NCT04341142). The inclusion criteria for this study were as follows: (i) seropositive HCWs screened for anti-SARS-CoV-2 Abs; (ii) be at least 18 years of age; (iii) HCWs having given their written consent and accepting a follow-up every 6 months; and (iv) HCWs affiliated to a social security scheme or beneficiary of such a scheme.
The exclusion criterion of this study was pregnant or lactating woman. The Immunonosocor cohort is an extension of the NOSOCOR project registered as ClinicalTrials: NCT04637867. Hospitalized patients were considered to have a severe form of COVID-19 if their hospital stay was longer than 24 hours. In our cohort, the median (IQR) duration of the hospital stay was 10 (5. 5 to 20) days, and 36 of 183 (19. 7%) patients were admitted into the intensive care unit. The clinical research and ethics committee of Ile de France V approved the study on 14 October 2020 (no. 20. 02. 27. 69817 Cat 3). Inclusion criteria for this study were (i) SARS-CoV-2 confirmed by reverse transcription polymerase chain reaction (RT-PCR), (ii) adults at least 18 years of age, (iii) signed consent form, and (iv) adults affiliated with the French health system.
Statistical analysis Raw, individual-level data for experiments where n < 20 are presented in data file S1. The outcomes of interest were immune parameters measured between 6 and 8 months on average after the last immune stimulation-either infection or vaccine. Statistical analyses were carried out identically for each immune variable studied. All groups were first described with box-and-whiskers plots. To take into account confounding factors, we fitted a multivariate linear regression model, adjusting the group effect for the age, sex, and delay covariates. The P values shown on box-and-whiskers plots were derived from the adjusted model. Log 2 transformation of the immune variable was applied to approach the assumptions of the linear model. Quadratic effect of age was tested and retained in the model when it was significant.
Model diagnoses were performed to check underlying assumptions, and special attention was paid to the standardized residuals. The analyses were performed with R software (https://cran. r-project. org/). Our predictors were age, as a continuous variable (considered as a possible confounder), sex, and the vaccination regimen, whereby convalescent = 0 and naïve = 1. For determination of the statistical significance of intergroup differences regarding mBC subset frequencies (Figs. 4, C to H, and 5A), we used beta regression modeling because our outcome variable is doubly bounded on the (0,1) interval. This approach uses an error structure appropriate for proportion data assuming that the dependent variable follows the beta distribution. In addition, using the logit link, parameter estimates resulting from our model allow for a better interpretation of the differences between groups because both magnitude and direction of group effect can be accurately quantified.
Morphometric evaluation To evaluate the effect of transplantation and vitrification on oocyte and follicle growth, the diameters of both were calculated from the mean of two perpendiculars, using the image analysis program Software ImageJ (NIH, USA). The follicle boundary was defined with the basement membrane clearly visible, as a demarcation between the granulosa cells and a special theca, and the oocyte boundary was zona pellucida. Thirty follicles from class (primary, secondary and antral) were randomly selected for the treatments. Only follicles completely free of any signs of atresia were analyzed.
Oocyte lipid quantification Paraffin-embedded ovaries were also serially sectioned (5 μm) and stained with Sudan red dye, as described by  Sudano et al. (2012). The Sudan red dye solution was prepared with 3% Sudan IV in 70% ethanol. To estimate the relative amount of lipid droplets per oocyte, images of antral follicles (12 oocytes per animal; 6 animals per group) were captured in 40X objectives. All photos were converted to black and white (32 bt), and the oocytes included in antral follicles were delimited and analyzed using the area of integrated density compared. This evaluation was performed in the Image J ® software according to  Leite et al. (2017).