dataesr/scientific-paragraphs-categorization huggingface dataset:
S5A  in the supplemental material) and produced three stable clones of the Jijoye EBV strain. Validation of these clones by restriction digestion and pulsed-field gel electrophoresis (see Fig. S5B ) showed a reduced number of W repeat units in clone C16 and also a deletion in clone L3, which Nanopore sequencing identified as encompassing the promoter driving GFP but leaving an intact EBV genome in clone L3. Nanopore sequencing of clone C4 showed 99. 8% identity to the published Jijoye genome sequence (GenBank accession number LN827800) in the unique regions that were sequenced by both methods; therefore, this BAC clone was used to make virus-producing 293 cell clones as described previously  (40). Virus production from the HEK293 cell lines containing BAC EBV genomes was induced by transfection of a 1:1:1 ratio of plasmids expressing gB (BALF4), BZLF1, and BRLF1 as described  (26).
The cone cells were layered over Ficoll-Paque Plus (GE Healthcare), and the mononuclear cells were isolated by density gradient centrifugation. Blood mononuclear cells were infected with recombinant EBV at a MOI of 0. 1 green Raji unit per cell in the presence of 1 mg/ml cyclosporin A. On day 8 after infection, the viability of infected cells was determined using the LIVE/DEAD Fixable Aqua dead cell stain kit (number L34957; Thermo Fisher Scientific). The cells were then washed and surface stained with antibodies specific for CD3 (phycoerythrin [PE]-Cy7 conjugated, number 557851; BD Pharmingen), CD20 (R-phycoerythrin [RPE] conjugated, number R7013; Dako), and CD21 (allophycocyanin [APC] conjugated, number 561357; BD Pharmingen). The stained cells were analyzed on a Becton, Dickinson LSRFortessa cell analyzer, and data were analyzed with FlowJo software (version 10.
ACKNOWLEDGMENTS We are indebted to the patients and their families for their contributions and blood samples gifted with consent to be used for our research. We thank the  University College London Pathogen Genomics Unit  for hybrid enrichment DNA sequencing and  Sanjay Vashee  and  Lauren Oldfield  for TAR cloning vectors. In the United Kingdom, this work was supported by  MRC  grant  MR/S0022597/1  (P. J. F. and R. E. W. ) and the  Imperial NIHR Biomedical Research Centre  (P. J. F. ); R. G. M. established human cord blood collections using a  KKLF Junior Fellowship (grant   KKL1051 ) and  Royal Society Research Grant  (grant  RG160682 ). In France , research support was from  Ligue Contre le Cancer-Equipe Labélisée  (S. L. ),  Institut National du Cancer  grants  PLBIO-2020  (S. L. ) and  PEDIAC consortium INCa_15670  (S.
Numerical experiments Numerical experiments are performed on an illustrative example, 10 instances based on realistic products and 480 randomly generated instances to evaluate the performance of the proposed integrated approach. The program is coded using the C++ programming language in Microsoft Visual Studio 2019. All numerical experiments are performed on a personal computer with Core I5 and 3. 20 GHz CPU with 12 GB RAM. All models in Sections 6. 1, 6. 3, 6. 4 and model P3 in Section 6. 5 are solved using the CPLEX solver version 12. 9. The CPLEX code and instance data are available on https://www. researchgate. net/ publication/359266654_Instance_data_and_cplex_code.
Acknowledgments This work was partially supported by the  China Scholarship Council  ( 202006650025 ), the  China Franco-Chinese Cai Yuanpei project  (Grant No. 43956QF ), the  National Natural Science Foundation of China  (Grant No. 71871159 ,  72071144 ), the  Humanities and Social Science Foundation of the Chinese Ministry of Education  ( 21YJA630096 ), and the  2nd Fujian Young Eagle Program Youth Top Talent Program ,  People's Republic of China.
Incucyte Live Cell Analysis To analyze the neurite complexity, the Incucyte ®® S3 live-cell imaging system (Sartorius, Minisart, Göttingen, Germany) was used. A total of 4000 PC12 cells stably expressing mCherry were seeded on collagen coated Costar ®® 24-well Clear TC-treated Multiple Well Plates (Corning, Corning, NY, USA). Differentiation was induced with RPMI 1640 medium with 1% fetal horse serum and 0. 5% fetal bovine serum containing 50 µg/mL NGF. This medium was refreshed every 48 h until full differentiation at day 10. Plates were scanned using the Incucyte system using a 20× objective and 36 images were taken per well. Assessment of neurite length and branch points was performed using the Incucyte ® Neurotrack analysis software module (Sartorius, Minisart, Göttingen, Germany). Average neurite length and branch points per cell were calculated by dividing total neurite length or branch points per well by the cell confluence expressed in mm 2.
Statistical Analysis All data are presented as means ± SEM. N is the number of independent experimental replicates. Statistical significance was determined using a Mann-Whitney analysis for a comparison of two experimental conditions, or a Kruskal-Wallis test followed by a post hoc Dunn test for multiple comparisons or a 1-way ANOVA followed by a Dunnett's test for multiple comparisons when more than 2 experimental conditions were compared. All statistical analyses were performed using GraphPad Prism version 9 (GraphPad Software, San Diego, CA, USA).
g. activ e re gions at the stellar surface, nanoflares, interactions with external bodies) of similar intensity. and He I time-series are shown in Fig. 2. Unsurprisingly, the periodograms of the CCF-based activity indicators exhibit prominent peaks at P rot and its first harmonic. By modelling, each of these time-series independently using a Gaussian Process (GP;  Rajpaul et al. 2015 )  with the quasi-periodic covariance kernel introduced in  Haywood et al. ( 2014 )  as implemented in the PYANETI python package  (Barrag án et al. 2022 ) , we find consistent rotation periods of 4. 863 ± 0. 004 d, 4. 86 ± 0. 01 d, and 4. 867 ± 0. 004 d for the RV, FWHM, and BIS time-series, respectively. 2 As illustrated in Fig. 2  , we note that including the flares in the GP fit, no more than marginally affects the best-fitting P rot.
Finally, we interpolate the resulting smoothed spectrum using a cubic spline kernel, and divide the full spectrum within the window by the interpolated function. As a final step, we divide each continuum-normalized spectrum by the median out-of-flare spectrum and compute a GLS periodogram for each velocity bin of the resulting time-series of line profiles using the ASTROPY package  (Astropy Collaboration 2013 , 2018 ). The resulting bidimensional periodograms are shown in Fig. 3  , for He I , and in Fig. B3  (respectively Fig. B4  ) for the other lines with (respectively without) including the observations affected by stellar flares in the input data. We find that the bidimensional periodogram of the He I line exhibits a clear significant peak at a period of 8. 33 ± 0. 04 d, well-centred on the line profile, present whether we include the flares in the input data set or not.
Chromospheric line Ca  is strongest, and compute the GLS periodogram of the resulting timeseries. Using a bootstrap-based method to estimate the FAP levels, we find that the peak at 8. 33 d reaches FAP levels of 0. 2 per cent and 1 per cent when the flares are respectively included and excluded from the data set. As a sanity check, we built 100 data sets by removing a third of the data points randomly chosen within the observational dates and found that the peak was systematically detected at a confidence larger than 2 σ , reinforcing the non-spurious nature of this detection. As a final test, we created two subsets, C 1 and C 2 , of data from the time-series of continuum-normalized He I profiles, containing the data collected at planet orbital phases 3  respectively between 0. 75 and 0. 25 (when the planet is closer from the observer than the star) and between 0.
Application to AU Mic Intrinsic stellar variability is not yet included in the current version of our DI code. We thus divide our time-series of HARPS CCFs into two subsets of data, I 2020 and I 2021 , containing respectively the 21 observations taken in No v ember/December 2020 and the remaining 61 observations collected in 2021, after removing Moonand flare-contaminated line profiles. We then perform the DI analysis independently on each data set. These two subsets of data respectively span 24 and 121 d (i. e. respetively 5 and 25 stellar rotation cycles), lower or of similar length to the ∼100-d time-scale on which A U Mic' s activity signal evolves  (Plavchan et al. 2020 ). In each DI reconstruction, we adopt a linear limb-darkening law of coefficient 0. 66  (Claret & Bloemen 2011 ) , an axial inclination of i DI = 80 • , 4  and a vsin i rot of 7.
8 km s -1 (see  Weise et al. 2010 ; Klein et al. 2021  ). We apply the process described in Section 4. 1 to independently invert I 2020 and I 2021 into surface brightness topologies and planet velocimetric semi-amplitudes. We find that the 2020 data set is consistent with a solid-body rotation at a rotation period of 4. 85 ± 0. 01 d, in line with the photometric rotation period 4. 86 d (see Table  1  ). In a similar way, the 2021 profiles are found to be sheared by a weak DR with eq = 1. 294 ± 0. 001 rad d -1 and d = 0. 013 ± 0. 005 rad d -1 , implying rotation periods of 4. 856 ± 0. 004 and 4. 90 ± 0. 03 d at the equator and pole, respectively. The reduced χ 2 maps are shown in the ( K b , K c ) space at the bestfitting DR parameters for both data sets in Fig. 4. Unsurprisingly, the relatively low number of data points on the 2020 period (21 profiles of 26 data points each) makes it impossible to reco v er an y of the two planets from the I 2020 data set alone.
In contrast, we obtain a 3. 3-σ detection of a planetary signal of semi-amplitude of K c = 13. 3 ± 4. 1 m s -1 at the orbital period of AU Mic c, whereas the RV signature of AU Mic b remains no more than marginally reco v ered, with a semi-amplitude of K b = 7. 1 ± 3. 8 m s -1. To ensure that the reco v ered signals are indeed of planetary origin, we reapplied the DI process to the 2021 data set but, this time, freezing the DR parameters to their best estimates and leaving the two orbital periods and velocimetric semi-amplitudes of the planets as free parameters of the model. We reco v ered orbital periods of 8. 47 ± 0. 01 and 18. 87 ± 0. 02 d for AU Mic b and AU Mic c, both fully consistent with the literature values (see Table  1  ). Moreo v er, the planet semiamplitude estimates remain similar, but slightly less precise than their counterparts from the previous paragraph.
4 ). Still, we emphasize that the planet mass estimates reported in this study are obtained using a fully independent method to that used in paper I. Therefore, having consistent planet mass estimates between the two studies reinforces the robustness of the planet detection from the HARPS data set. Similarly to paper I , the masses of AU Mic b and AU Mic c are respectively found slightly lower and significantly larger than their counterparts in  Cale et al. ( 2021 ) , who conducted a velocimetric analysis of a large number of data points collected with multiple optical and nIR spectrographs. The origin of this discrepancy, discussed in more details in the section 4. 1 of paper I , remains unclear. Hence the need to (i) continue the dense RV follow-up of the AU Mic system using different high-precision velocimeters and (ii) carry out independent analyses led by different teams.
7. For each period (i. e. 2020 and 2021), we generate 20 mock CCF time-series assuming different level of DR, from d = 0 to 0. 17 rad d -1 (corresponding to the level DR detected from the large-scale magnetic field in Kl21), and inject random noise at the same level as in our observations. In order to assess whether a hypothetical DR would have been detected in our data, we apply our DI code on all these data sets using the method described in Section 4. 1 , and estimate the significance of a model at the bestfitting DR parameters compared to a model assuming a solid-body rotation. This process is then repeated for 10 different realizations of white noise. For both 2020 and 2021 periods, we find that a DR would have been accurately detected at a significance larger than 5 σ. We therefore discard strong biases towards low DR values from our actual DI reconstructions.
AC K N OW L E D G E M E N T S We gratefully acknowledge X. Dumusque and F. Bouchy for their coordination of the HARPS time-share, which allowed us to spread the observations o v er the two semesters with the appropriate time-sampling, and all the observers involved in the time-share (F. Bouchy, B. Canto, I. de Castro, G. Dransfield, M. Esposito, V. Van Eylen, F. Hawthorn, M. Hobson, V. Hodzic, D. Martin, J. McCormac, H. Os-borne, J. Otegi, A. Suarez, and P. Torres) for carrying out the observations on our behalf. We also thank J. Patterson for managing the Oxford Astrophysics compute cluster, glamdring, which was used to carry out the data analysis. The full journal of observations, including the observation dates, noise values in the CCFs, RV/FWHM/BIS time-series, and the chromospheric activity indices computed in Section 3.
This study is based on observations collected at the European Southern Observatory under ESO programmes 0105. C-0288 7 & 0106. C-0852 (PIs Aigrain and Zicher). This research has made use of NASA's Astrophysics Data System. KB, BO, and AS acknowledge funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 865624, GPRV). NZ acknowledges support from the UK Science and Technology Facilities Council (STFC) under Grant Code ST/N504233/1, studentship no. 1947725. AAV and RDK acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 817540, ASTROFLOW). LDN thanks the Swiss National Science Foundation for support under Early Postdoc.
Mobility grant P2GEP2 200044. AS acknowledges PLATO CNES funding at CEA/IFU/DAp and funding from the Programme National de Plan étologie (PNP). BN's work is supported by STFC Consolidated Grant ST/S000488/1 (PI Balbus). JFD and JB acknowledge funding from the ERC under the H2020 research and innovation programme (grant agreements no. 740651 NewWorlds, and no. 742095 SPIDI: Star-Planets-Inner Disk-Interactions). Finally, we thank the re vie wer for valuable comments and suggestions which helped us improving an earlier version of the manuscript.
Mobility grant P2GEP2 200044. AS acknowledges PLATO CNES funding at CEA/IFU/DAp and funding from the Programme National de Plan étologie (PNP). BN's work is supported by STFC Consolidated Grant ST/S000488/1 (PI Balbus). JFD and JB acknowledge funding from the ERC under the H2020 research and innovation programme (grant agreements no. 740651 NewWorlds, and no. 742095 SPIDI: Star-Planets-Inner Disk-Interactions). Finally, we thank the re vie wer for valuable comments and suggestions which helped us improving an earlier version of the manuscript. MNRAS 512, 5067-5084 (2022).
INTRODUCTION Since the beginning of 2020, COVID-19 infected more than 254 million people and caused more than 5. 1 million deaths worldwide (accessed on November, 23, 2021) 1. Therefore, the development of vaccines raised hope to control the pandemic. However, due to the intensity of the pandemic and the overburden of healthcare systems in many countries, the use of new vaccines was authorized despite limited trials and safety data under the conditional marketing authorization rule  (European Commission, 2006). Early clinical trials or mRNA vaccine for infectious diseases (rabies) were performed in 2013. Then, the first clinical trial of mRNA vaccine in lipid nanoparticles was performed in 2015 (influenza). However, before COVID-19, large scale use of mRNA vaccine in lipid nanoparticles and their safety in large-scale utilization in the real world were lacking  (Dolgin, 2021).
Population Under Study SARS-CoV-2 infection was diagnosed using real-time reverse transcription (RT)-PCR (qPCR) on a nasopharyngeal swab. For each patient, we collected anonymously from the hospital database their date of birth, sex, symptoms when available, evolution, hospitalization in conventional medicine department or intensive care unit (ICU), death or recovery. "Vaccinated" patients referred to all patients who had received at least one vaccine dose; "fully vaccinated" patients referred to those who had a complete vaccine scheme; and "partially vaccinated" patients referred to those who had received an incomplete vaccine scheme.
This approach allowed us to check whether the observed effect of vaccination was consistent in the whole population and across different clinically relevant subgroups. Finally, we compared the efficiency of a complete to an incomplete vaccination scheme (1 or 2 doses, depending on the vaccine) using logistic regression models on the population infected with an Alpha/20I or Delta/21A variant (n = 11,624) with adjustment for age, sex and variant. Goodness of fit of the multivariate logistic regressions was assessed using the Hosmer-Lemeshow (HL) test. A two-sided α value of less than 0. 05 was considered to be statistically significant. Analyses were carried out using SAS 9. 4 statistical software (SAS Institute, Cary, NC, United States).
Denmark The MIKE SHE model is the base model of the Danish national water resources model  (Henriksen et al. , 2003; Højberg et al. , 2013; Stisen et al. , 2019a; 2019b). MIKE SHE is a physically-based, integrated and fully-distributed groundwater-surface water model  (Abbott et al. , 1986; Graham and Butts, 2005). Here, the model is set up at a 250 m x 250 m resolution. Input climate data come from datasets developed by the Danish Meteorological Institute gridded at a 20 km x 20 km resolution for temperature and potential evapotranspiration and 10 km x 10 km for precipitation  (Scharling, 2012). Daily streamflow data come from National Environmental Monitoring Programme (NOVANA) from the Danish Ministry of Environment and Food (DCE), described by  Stisen et al. (2019a); (2019b)  and made available by  Koch and Schneider (2022).
France Three lumped daily rainfall-runoff models (GR4J, HBV0 and TOPMO) associated with a snow module (CemaNeige) are used to simulate the hydrology of the French catchment. These models have been widely used in the country. Data come from the Hydro-SAFRAN daily data set  (Delaigue et al. , 2020). Daily streamflow measurements at the outlet of the catchments were retrieved from Banque HYDRO  (Leleu et al. , 2014). Daily meteorological data were supplied by the SAFRAN dataset, an 8 km x 8 km atmospheric reanalysis  (Vidal et al. , 2010)  aggregated at catchment scale. Potential evaporation (PE) was computed using the temperature-and radiation-based formula proposed by  Oudin et al. (2005). A short description of the models is provided here: a) GR4J  (Perrin et al. , 2003)  is a parsimonious four-parameter lumped conceptual model.
The daily amount of melted water is used as input to the rainfall-runoff models in addition to liquid precipitation. The parameters of CemaNeige were calibrated simultaneously with the parameters of the hydrological models. GR4J and CemaNeige are used within the airGR R package  (Coron et al. , 2017 (Coron et al. , , 2018)). HBV0 and TOPMO are used within the airGRplus R package (in development,  Coron and Perrin, 2018). The models are automatically calibrated by optimizing the KGE computed on the square-root of streamflow. The optimization algorithm is based on a prior global screening of the parameter space followed by a local search from the identified best parameter set (see  Edijatno et al. , 1999; Mathevet, 2005).
c) WiMMed (Watershed Integrated Model for Mediterranean Environments;  Polo et al. , 2010) , is a hourly-timestep distributed physically-based model. The snow module (SnowMed) uses a punctual mass and energy balance extended to a distributed scale using depletion curves  (Herrero et al. , 2009; Pimentel et al. , 2017a). AE is calculated using the Penman-Monteith PE formulation  (Penman, 1948; Monteith et al. , 1964)  whereas infiltration is estimated using the  Green and Ampt (1911)  approach in a two-layer soil discretization. Calibration is done through a process-oriented stepwise approach based on expert knowledge, minimizing mean absolute error in daily water volumes.
Do the hydrological models used in each case have the same transferability skills? The DSST methodology highlighted different capabilities among hydrological models to describe changing climatic conditions, especially in the mountainous snow-dominated catchments (France and Spain) where one of the hydrological models clearly had better transferability skills despite comparable calibration results. In the Spanish case, the different snow modules used by the hydrological models might explain the different transferability capabilities. The physically-based conceptualizations of WiMMed were able to capture the high variability in snow dynamics found in these catchments (especially the role of evaposublimation  (Herrero and Polo, 2016) ), that the degree-day approaches were not able to reproduce.
Additionally, the higher spatiotemporal resolution used by WiMMed and the other models (30 x 30 m and hourly compared to 100 km 2 and daily, respectively) can also explain the differences in skill. In the French case, the better performance of the HBV0 model is most certainly explained by a better structural description of the catchment processes, although snow processes dominate the hydrological regime in winter and spring and all three models have the same snow module. The interactions between the snow module and the parameters of the rainfall-runoff models could yield equifinality and affect model calibration. For instance, the parameter governing snowpack inertia in TOPMO was significantly different from the other models and might have caused an inappropriate scaling of the snow processes in warm conditions.
Acknowledgements This work was funded by the project  AQUACLEW , which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  DLR (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO  ( ES ),  ANR (FR)  with co-funding by the  European Commission  [Grant 690462]. R. Pimentel acknowledges fundings by the modality 5. 2 of the Programa Propio-2018 of the  University of Cordoba  and the  Juan de la Cierva Incorporación Programme  of the  Spanish Ministry of Science and Innovation  ( IJC2018-038093-I ). J. Aparicio acknowledges fundings by the  Programme Ayudas para contratos predoctorales para la formación de doctores  of the  Spanish Ministry of Science and Innovation  ( PRE2019-090493 ). R. Pimentel  and  J. Aparicio  are members of  DAUCO, Unit of Excellence ref. CEX2019-000968 -M, with financial support from the  Spanish Ministry of Science and Innovation , the  Spanish State Research Agency , through the  Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D.
Single-Crystal X-ray Diffraction Single-crystal diffraction data were collected on a Xcalibur Gemini diffractometer with graphite monochromated Mo Kα radiation (λ = 0. 71073 Å), using the related analysis software  [70]. Absorption correction has not been performed because it caused a significant decrease in data quality: Increase of Rint value and decrease of the rate of completeness. The structures were solved using the SHELXT program  [71]  and refined by full-matrix least-square methods on F 2 with the 2018 version of SHELXL program  [72]  on OLEX2 software  [61, 73, 74]. All non-hydrogen atoms were refined with anisotropic displacement parameters. Hydrogen atoms belonging to carbon atoms were placed geometrically in their idealized positions and refined using a riding model. Crystallographic data are presented in Table  1.
Magnetic Measurements Magnetic susceptibility data (2-300 K) were collected on powder samples using a SQUID magnetometer (Quantum Design model MPMS-XL) in a 1T applied magnetic field. A magnetization isotherm (2 K) was measured between 0-5 T. All data were corrected for the contribution of the sample holder and diamagnetism of the samples estimated from Pascal's constants  [61, 75, 76].