dataesr/scientific-paragraphs-categorization huggingface dataset:
This new generation of Convection-Permitting RCMs could also bring a better representation of the 2-meters near-surface air temperature  (Lucas-Picher et al. , 2021). 5 Tables  554   Table 1  Observational datasets and reanalysis available at the airports selected as case studies for this study   Table  4  The RCM × GCM matrix indicating which combinations from the Euro-CORDEX Historical experiment were available for the study in the period 1961-2005 i RCM Reference(s) 1C L M c o m -E T H -C O S M O -c r C L I M -v 1 -1Böhm et al. ( 2006 Evolution of high-temperature extremes over the main Euro-Mediterranean airports Table  5  The RCM × GCM matrix indicating which combinations from the Euro-CORDEX RCPs scenarios experiment were available for the study between 2021 and 2100 RCP2. 6 CNRM-CM5 EC-EARTH HadGEM2-ES IPSL-CM5A-LR MPI-ESM-LR NorESM1-M CLMcom-CCLM4-8-17 3(r12i1p1) CNRM-ALADIN63 3(r1i1p1) DMI-HIRHAM5 3(r3i1p1) 3(r1i1p1) GERICS-REMO2015 3(r1i1p1) 3(r1i1p1) ICTP-RegCM4-6 3(r1i1p1) 3(r1i1p1) 3(r1i1p1) SMHI-RCA4 3(r12i1p1) KNMI-RACMO22E 3(r1i1p1) RCP4.
Acknowledgments. We gratefully thank the  CERFACS  for supporting this work. We also acknowledge the  World Climate Research Programme's Working Group on Regional Climate , and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We thank the climate modelling groups (listed in Table 4 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison , the  European Network  for Earth System Modelling and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ). We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (http://www.
uerra. eu)and the  Copernicus Climate Change Service , and the data providers in the  ECA&D  project (https://www. ecad. eu). We thank  AEMET  and  UC  for the data provided for this work ( Spain02 v5  dataset, available at http://www. meteo. unican. es/datasets/spain02). We also thank  Meteo-France  for the SAFRAN-France reanalysis data provided (under request). The analyses were performed using R Statistical Software (v3. 6. 2; R Core Team 2021 ) and the  NCAR Command Language  ( Version 6. 6. 2 ) [Software]. ( 2019). Boulder, Colorado:  UCAR/NCAR/CISL/TDD. http: //dx. doi. org/10. 5065/D6WD3XH5. Figures were produced using R, in particular, maps were generated with the 'ggmap' package (Kahle and Wickham, 2013). Special thanks to  S. Somot  for constructive discussions on this study, and to  J.
CNRM-CM5 EC-EARTH GFDL-ESM2G HadGEM2-ES CLMcom-CCLM4-8-17 3(r1i1p1) 3(r12i1p1) 3(r1i1p1) CLMcom-ETH-COSMO-crCLIM 3(r1i1p1) 3(r12i1p1) 3(r1i1p1) 3(r1i1p1) 3(r3i1p1) Data Availability CMIP5 and CORDEX multimodel datasets are publicly available via the website of Earth System Grid Federation (http://pcmdi9. llnl. gov/). E-OBS dataset was accessed from https://surfobs. climate. copernicus. eu/ dataaccess/access eobs. php. NOAA Global Historical Climatology Network Daily (GHCN-Daily) data can be downloaded from https://registry. opendata. aws/noaa-ghcn. The Spain02 v5 dataset is available at http://www. meteo. unican. es/datasets/spain02). The SAFRAN-France reanalysis data were provided by Météo-France under request.
Anyway, we will, more or less exploratively, analyse young people aged 18-25 and 26-35 respectively. Like other developed countries, Denmark has had a net migration from rural to urban areas. However, in recent years more young women than men have migrated to cities for education, leaving the remaining young men potentially with problems of finding a partner. For this reason, we analyse an interaction effect between sex and urban-rural typology among young people. Despite the net migration from rural to urban areas, a sizeable number of people still migrate the other way, from urban to rural areas. Not least, we see this type of migration among elderly people who want to spend their retirement in the countryside. We have register data on all respondents included in the survey, and these register data occur both before and after the survey interviews.
Data and the prime dependent and independent variables The survey data covers thirty-eight (of ninety-eight) Danish municipalities with subjective well-being as the central theme. In each municipality, we have survey data from approximately 1,000 respondents. The rich, individual-level register data, measuring sex, age, place of residence, education, income, members of household etc. , covers all people with a permanent address in Denmark, and we can match this data with the survey data. Added to this data are also several municipality-level key measures. The design of the survey data set, with representative data from many municipalities all around the country, makes it possible for us to investigate the difference between urban and rural municipalities with multilevel analysis, and thereby better differentiate individual-level explanations from placelevel explanations.
Uncertainties The measurement uncertainties were evaluated based on calibration tests, carried out before or after the tests, and on the data provided by the sensor manufacturers. The calibration tests consisted in obtaining the differences between standard values (from specific calibrated sensors or samples) and the measured values, read on the acquisition system. Two levels of uncertainty were defined following the procedure given by Hamins  [23]. The standard uncertainties were obtained at room temperature without fire. A second level, the expanded uncertainty, took into account the fire test conditions and test repeatability, and was introduced through a coverage (or correction) factor, applied to the standard uncertainty. The uncertainties of elaborated variables (the flow rate in the ventilation, the flame spread velocity, the MLR and the HRR), determined from measured quantities, are assessed from qualification tests.
Given the good safety profile of sotrovimab and of anti-S antibodies in general (up to 4000/4000 mg for imdevimab/casirivimab for instance), increasing sotrovimab dose may increase its clinical value, a hypothesis that is being tested at 1000 mg in the RECOVERY clinical trial (NCT04381936). Combination therapy with sotrovimab and antivirals to improve virological and clinical responses in immunocompromised COVID-19 patients also deserves investigation  31. In conclusion, our data supports the use of sotrovimab to treat the most recent omicron lineages and suggest that increasing the dose may provide a clinical benefit.
Patient Samples Patient samples were obtained from the ongoing multicentre prospective observational cohort study ANRS 0003S CoCoPrev (Prevention of COVID-19 complications in high-risk SARS-CoV-2-infected subjects eligible for treatments under an emergency use authorization or early access, NCT04885452,  33  ) This cohort recruited consecutively patients at high-risk for progression to severe COVID-19, having PCR-proven mild-to-moderate COVID-19 in the first 5 days of symptoms and who were treated under an emergency use authorization or early access in one of the 32 participating centers. Here, we included vaccinated immunocompromised patients having IgG anti-spike sera titers less than 280 BAU/mL at inclusion and having a serum sample collected at day 0, day 7, month 1 and month 3 after treatment initiation.
Viral isolates The reference D614G and BQ. 1. 1 isolates were previously described 34. The XBB. 1. 5 strain was isolated on IGROV-1 cells from a nasopharyngeal swab of an anonymous individual attending the emergency room of Hôpital Européen Georges Pompidou (HEGP; Assistance Publique, Hôpitaux de Paris). Sequencing of the nasopharyngeal swab, and the isolated virus confirmed XBB. 1. 5 lineage. For sequencing, we used an untargeted metagenomic sequencing approach with ribosomal RNA depletion as previously described  34. The sequences were deposited on GISAID (D614G: EPI_ISL_414631; BQ. 1. 1: ID: EPI_ISL_15731523; XBB. 1. 5: EPI_ISL_16353849). Viral stocks were prepared from isolated strains using either Vero E6 cells or IGROV-1 cells and titrated using Vero E6 and S-Fuse cells.
Acknowledgement We thank members of the  Virus and Immunity Unit  for discussions and help in the preparation of this manuscript. We thank  Valentin Leducq  and  Emna Ghidaoui  for their technical assistance. We thank  Laurent  hocqueloux and  Thierry Prazuck  for providing the clinical antibodies for in vitro studies and  Faustine Amara  for her help with the plasmids. We thank  Pr Yazdan Yazdanpanah  and all the  ANRS-MIE team  for their invaluable support and help. This study would have not been possible without the teams involved in the CoCoPrev Study and designated as the CoCoPrev Study Group:  Magali Garcia ,  Valentin Giraud ,  Agathe Metais ,  France Cazenave-Roblot ,  Jean-Philippe Martellosio  ( CHU de Poitiers ).
Fundings Work in TB and OS lab is funded by  Institut Pasteur ,  Urgence COVID-19 Fundraising Campaign of Institut Pasteur ,  Fondation pour la Recherche Médicale (FRM) ,  ANRS-MIE , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  ANR / FRM Flash Covid PROTEO-SARS-CoV -2 ,  ANR   Programme Hubert Curien Maimonide ,  Coronamito ,  HERA  european funding,  Sanofi  and IDISCOVR. The E. S. -L. laboratory receives funding from  Institut Pasteur , the  INCEPTION program  ( Investissements d'Avenir  grant  ANR-16-CONV-0005 ) and  NIH   PICREID program  (Award Number  U01AI151758 ). The  ANRS 0003S CoCoPrev cohort  is conducted with the support of  ANRS│MIE  and funded by  French ministries : Ministère des Solidarités et de la Santé and Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation».
Data and processing (1) Flux tower data To evaluate the models' performance, we acquired the daily eddy covariance (EC)-derived GPP 145 and NEP from three flux tower sites over the study area (Fig. 1 ), which was available from the ChinaFLUX network  (Yu et al. , 2006). The ChinaFLUX has undergone strict data quality control, including coordinate rotation, WPL correction, nighttime flux correction, gap filling, and flux partitioning. For instance, the nighttime CO2 flux data under low atmospheric turbulence conditions were screened using site-specific thresholds of friction velocity (u*), which was identified following 150  Reichstein et al. (2005) , and the NEE was also partitioned into GEP and ER with the method of  Reichstein et al. (2005). (2) Remote sensing data LAI. The Global Land Surface Satellite (GLASS) LAI product during 2001-2018 was obtained from the University of Maryland.
This data was generated using the general regression neural networks 155 (GRNNs) with a spatiotemporal resolution of 0. 05° and 8-day  (Xiao et al. , 2016). The daily LAI at 0. 05° resolution was obtained by linear interpolation of the 8-day GLASS LAI, which was used to drive the BEPS model  (Wang et al. , 2022). The GLASS LAI was used in this study because of its higher accuracy in China's forests compared to other satellite LAI products, such as the GEOVI LAI, etc. (Liu et al. , 2018; Xie et al. , 2019). 160 Satellite-derived Vcmax25 products. We obtained the spatial variation of satellite-derived Vcmax25 products from the National Ecosystem Science Data Center, National Science & Technology Infrastructure of China, available from 2000 to 2019, with a spatiotemporal resolution of 500m and 8day.
05°×0. 05° for comparison. The five published GPP products include (a) the MODIS GPP (MOD17A2H Version 6)  (Running et al. , 2015) , (b) the EC-LUE GPP generated by a revised light use efficiency model  (Zheng et al. , 2020) , (c) the NIRv GPP produced by near-infrared reflectance (NIRV) and machine learning method  (Wang et al. , 2021b) , (d) the VPM GPP produced by 175 the Vegetation Photosynthesis Model (VPM)  (Zhang et al. , 2017) , and (e) another published BEPS GPP product, which was also generated by the BEPS model but with independent driving data and globally calibrated parameters  (Chen et al. , 2019b; He et al. , 2021b). See Table  S3  for more details on the five GPP products. (3) Climate data 180 We obtained the daily meteorological data including the temperature, precipitation, relative humidity, and downward solar radiation from the Climate Meteorological Forcing Dataset (CMFD)  (He et al.
(4) Land cover data The annual land cover data sets from the European Space Agency (ESA) were used for 190 simulations  (ESA, 2017). The ESA CCI land cover data has a resolution of 300 meters, spanning the 1992-present period. The overall global accuracy of CCI land cover data is nearly 75. 4%, with higher accuracy for forests  (ESA, 2017). In this study, the original CCI land cover data were first aggregated into 0. 05°×0. 05° by using the CCI LC user tool. Considering the CCI land cover data composed of 37 original classes, we referred to  (Tagesson et al. , 2020)  to reclassify the CCI land cover data into 9 classes, 195 including the evergreen broadleaved forest (EBF), evergreen needleaved forest (ENF), deciduous broadleaved forest (DBF), and mixed forest (MF), cropland (CRO), grassland (GRA), shrubland (SHR), urban (URB), and barren land (BAR).
5) Soil and atmospheric CO2 data The available water capacity (AWC) data with a spatial resolution of 0. 05° was extracted from 200 the re-gridded Harmonized World Soil Database (RHWSD) v1. 2  (FAO, 2012; Wieder et al. , 2014)  and used to drive the model in this study. We obtained the annual mean atmospheric CO2 concentration data.
Model and Uncertainties 505 In this study, we used the process-based BEPS model to simulate forest GPP of the subtropical region. We first used the Vcmax25 product retrieved from remote sensing data (i. e. , leaf chlorophyll content) to replace the constant value of the Vcmax25 in the model. Wang et al. (2019) ,  Luo et al. (2018), and Croft et al. (2017)  indicated that the use of the remotely sensed leaf chlorophyll content to invert Vcmax25 can improve the accuracy of GPP simulation in evergreen conifer forests and a temperate deciduous forest. 510 Our results suggested that the BEPS model with spatial varying Vcmax25 values can also reach reasonable simulation of subtropical forest GPP over spatiotemporal scales (Fig. 2 -3, Fig. S1-S6 ). Incorporating the spatial variation of the Vcmax25 inverted by remotely sensed data into the process-based model does not require its pre-calibration  (Chen et al.
g. , FSGOM LAI and MODIS LAI). The consistent conclusions also have been reported in other studies  (Chen et al. , 2021a; Jiang et al. , 2017; Xie et al. , 525 2019). Therefore, it was reasonable to use GLASS LAI as input to model forest GPP in this study. There are large differences between the available land cover data, such as ESA CCI land cover data  (ESA, 2017)  and MODIS land cover data  (Sulla-Menashe et al. , 2019) , which were mainly caused by the discrepancies in the definition of forest and divergent data sources  (Li et al. , 2016; Magdon et al. , 2014). Eventually, the use of different land cover data may also lead to uncertainty in the estimate of the 530 regional total GPP. The satellite-derived ESA CCI land cover used in this study may suffer from cloud contamination, satellite signal aliasing, and uncertainty from algorithmic flaws that affect the accuracy of forest cover mapping  (Dong et al.
, 2012). Yang et al. (2017b)  systematically evaluated the accuracy of different land cover data in China, showing that ESA CCI data has higher accuracy, especially compared to the commonly used MODIS land cover data. Currently, remote sensing is still considered 535 to be the only effective tool for land cover mapping at large scales, and more precise remote sensing is still needed in the future. Besides, assessing the uncertainties and discrepancies in carbon flux simulations.
A. thaliana is a small annual plant inhabiting contrasting environments and climates all over the world  (1001 Genomes Consortium, 2016; Hoffmann, 2002; Lee et al. , 2017). An international effort of sampling made available fully-sequenced genomes for hundreds of natural genotypes (hereafter 'accessions')  (1001 Genomes Consortium, 2016). This open-access database is a great opportunity to study the genetic basis of phenotypic variation. For instance, genomic variation between accessions of A. thaliana showed evidence of local adaptation to climate  (Clauw et al. , 2022; Dittberner et al. , 2018; Exposito-Alonso, 2020; Lasky et al. , 2014; Vasseur et al. , 2018). Moreover, natural accessions of A. thaliana expressed contrasting values of constitutive defences against herbivores, notably in glucosinolate concentration in leaf tissues  (Brachi et al.
A value of zero was given if the snail did not consume any part of the plant and a maximum value of five if all the leaves of the plant were eaten by the snail (Figure  S1 ). We confronted the leaf consumption index estimated by two independent observers on a training data set of 227 pictures to validate the method (ρ = 0. 86, p < 0. 0001). The initial rosette area of each plant was estimated from the picture taken before the passage of the snail (ImageJ; version 1. 53c,  Schneider et al. , 2012). We then calculated the 'rosette area difference', as the difference between initial rosette areas of the R plant and the N1 plant in each arena.
Range of variation Related questions First choice Binomial [0; 1] Which plants were chosen first by the snail? The effect of accession identity was tested with linear model for the first duration and with quasibinomial models for the three other response variables. Rosette area difference was systematically added as a covariate in these models. The effect of accession identity was reported as its global effect on the variance (ANOVA of type II, package 'car'). From these models, we calculated marginal means of the response variables of all accessions using the package 'emmeans'  (Lenth et al. , 2018). Correlations between mean responses of accessions were assessed with the Kendal's statistic.
| Quantitative genetic analysis We tested for allelic effects on the four response variables through genome-wide association studies using the online platform EasyGWAS (https://easyg was. ethz. ch/, Grimm et al. , 2017). Marginal means of accessions, as calculated above, were implemented. We used the EMMAX algorithm, filtering for minimum alleles frequency at 5% and including the two first axes of a PCA performed on SNPs to correct for population structure. Significance thresholds were assessed by the Bonferroni method with an alpha error of both 0. 05 and 0. 1. Known functions of genes were extracted from TAIR10 database (https://www. arabi dopsis. org, Lamesch et al. , 2012).
2-500 s) or long (more than 2000 s), with fewer intermediate durations (Figure  S2 ). The identity of the accession explained a significant part of the variance of the first duration spent on a plant (20% of the total variance, p = 0. 02). However, no SNP position along the genome was significantly associated with the first duration spent on a plant (Figure  3 , Table  S2 ). We found a large variation in the proportion of time spent on R plants across accessions (16%-100%). Four SNPs along the genomes were significantly associated with the proportion of time spent on R plants (Figure  3 ). Among them, the gene AT1G24560. 1 encodes for plant-unique RAB5 effector 2 (PUF2), a key protein in cell-to-cell communication in plants (Table  S3 ). The leaf consumption index on R plants relative to N1 plants was significantly explained by accession identity (χ 2 = 164, df = 112, p < 0.
001). In other words, depending on accession identity, a prior exposure to a damaged plant can increase or decrease the attractiveness of undamaged plants. Along the genome, four SNPs were significantly associated to the proportion of leaf consumption index on R plants (Figure  3 ). Among them, AT3G10050. 1 encodes for the first enzyme in the biosynthetic pathway of isoleucine. Another gene was found on chromosome 3 (AT3G03380. 1) that encodes for a periplasmic protease (Table  S4 ), a protein involved in stress tolerance  (Luciński et al. , 2011). The effects and the frequencies of minor alleles for every significant SNP are detailed in Table  S5.
Data For our research purposes, we collected data on the UK innovators focusing on BMR activities for two main reasons. First, UK has implemented a rich policy mix (R&D tax credits, support for start-ups, and many others) similarly to what has happened in other developed countries  (Miroshnychenko et al. , 2020). Second, we focus on innovative firms because it is widely acknowledged that they are the backbone of the economic growth and development  (Massa & Testa, 2008). Following prior research on BMR  (Saebi & Foss, 2015; Foss & Saebi, 2018; Pieroni et al. , 2019; Rachinger et al. , 2019; Zott and Amit, 2017) , we matched three different datasets: 1) the UK Innovation Survey (UKIS), 2) the Business Expenditure in Research and Development (BERD), and 3) the Business Registry (BSD). Firstly, we gathered and matched six sequential UKIS waves conducted every second year by the UK Office of National Statistics (ONS): from wave 4 (related to  [2002] [2003] [2004]  to UKIS 9 (related to 2012-2014).
Secondly, we deployed BERD and BSD data for every second year from 2002 to 2012. The data were matched to a correspondent UKIS wave for the initial year of the UKIS period. From the BERD dataset, we gathered data about the number of researchers with university degrees and above employed by the firm and in-house and bought-out R&D. From the BSD dataset, we collected information about industry, employment, firm size, ownership, and firm age. Direct measures of BMR, such as an increase in a range of products/ services, and entering new markets are included in the UKIS. Additional measures that were included in the study pertain to partner types, human capital, collaboration networks, and barriers to innovation. We work with three samples of 1) 24,211 observations available for the first proxy of BMR (Exploration goods-Model 1); 2) 7020 observations available for the second proxy of BMR (Exploration markets-Model 2); 3) 21,140 observations available for the third proxy of BMR (Industry switch-Model 3).
Data analyses We used probit estimation on an unbalanced panel with three distinctive samples given the data availability for three dependent variables of BMR to test our research hypotheses. We start by first analyzing the unbalanced panel sample of Model 1 (exploration goods) which has 24,211 observations with a panel element, firms that observed at least twice 3087. This estimation will analyze the factors that affect the propensity of a firm to explore new goods. Second, we estimate Model 2 (exploration markets) with includes 7020 observations and 1488 firms that observed at least twice. This Model 2 analyses the factors that affect the propensity of a firm to explore new markets. Finally, we estimate Model 3 (industry switch) using an unbalanced panel of 21,140 observations and a panel element of 2890 firms observed at least twice.
Limitations and further research This study is not without limitations. Firstly, due to the anonymous nature of the UKIS, no additional sources for information on partners could be added to the database, which could have been used to supplement the data. In particular, we are unable to track the number of contacts and the length of engagements between partners. Secondly, a cross-country comparative approach in understanding the relationship between knowledge collaboration and BMR could have provided more robust and generalizable results. While we were able to use data between 2002 and 2014, further research might extend the sampling window and gather more recent data. The use of unbalanced panel dynamic data is rather common in many studies deploying innovation survey data (see  Audretsch & Belitski, 2020b; Ebersberger, Galia, Laursen, & Salter, 2021; Giovannetti & Piga, 2017).