dataesr/scientific-paragraphs-categorization huggingface dataset:
, Germany). Individual estimates were obtained for 5-6 plants per population providing genome size estimates for each population. Additional plants were analysed using pooled sample strategy (5 individuals plus the reference standard analysed simultaneously) providing estimates of DNA ploidy only. The value of genome size in mass units (2C in pg; sensu  Greilhuber et al. 2005 ) was obtained using the following equation: Limonium sp. 2C nuclear DNA content (pg) = (Limonium sp. G1 peak mean / reference standard G1 peak mean) * genome size of the reference standard. Because the genome size value was obtained for several individuals that were also karyologically characterized (see below), it was possible to infer DNA ploidy levels for all the individuals analysed. Descriptive statistics were calculated for genome size data (mean, standard deviation of the mean, coefficient of variation and minimum and maximum values).
Acknowledgements We acknowledge the  Haut Commissariat aux Eaux et Forêts et à la Lutte Contre la Desértification, Morocco , for providing scientific permission ( N°04/2015 ) for collecting samples. Thanks to  Sofia I. R. Conceição  ( LEAF/ISA ) for help in cytogenetic experiments and  Er-Riyahi Saber  ( Département de géographie, Université Moulay Ismail, Meknès ) for the preparation of the distribution map of L. mucronatum.
Cell cultures and treatments Cell lines were purchased from the American Type Culture Collection (ATCC). Cells were grown in a humidified environment at 37 o C with 5% CO 2. Cell lines were confirmed to be free of mycoplasma contamination (MycoAlert; Lonza). NSCLC cell lines (NCI-H460, NCI-H520, NCI-H1299, and A549) were grown in RPMI medium supplemented with fetal bovine serum at a concentration of 10%, 62. 5 mg/mL penicillin and 100 mg/mL streptomycin (Life Technologies). The human bronchial epithelial cell lines BEAS-2B and BZR were grown in LHC-9 medium (Thermo Fisher Scientific). Verification of all cell line identities was performed by short tandem repeat (STR) sequencing using reference databases from ATCC (Genetics Core, University of Arizona, AZ). Cells were plated 1-day before treatments and challenged with the desired concentrations of N6L or AS1411 as indicated in figure legends.
Nucleolin knockdown expression NCL small interfering RNA (siRNA) duplex sequences [5'-GGUCGUCAUACCUCAGAAGtt (NCL#1, ID#144014), 5'-CGGUGAAAUUGAUGG AAAUtt (NCL#2, ID#144016)] and scrambled siRNA sequences (Silencer Negative Control #1 siRNA (AM4635)) were chemically synthesized and annealed by Life Technologies. BLAST analysis showed no homology to any sequence in the Human Genome Database, other than the intended target. siRNAs were transfected using Lipofectamine RNAiMAX (Life Technologies), according to manufacturer's directions. Briefly, cells were plated and incubated overnight to allow adherence, then transfected with siRNAs for 8 h. Cell medium was replaced with fresh complete medium, and cells incubated for 72 h before analysis as described in figure legends.
Acknowledgments: The authors thank  Marco Antônio Silva Campos  and  Alexandre de Magalhães Vieira Machado  and their team who provided viral culture infrastructure at the  Imunologia de Doenças Virais Group (IDV-IRR/FIOCRUZ). We are grateful to all members of the  Mosquitos Vetores Group  ( MV -IRR/FIOCRUZ ) and the  World Mosquito Program. We are grateful for support from the  Coordination for the Improvement of Higher Education Personnel (CAPES).
For S 1 , a Riegl VQ-820-G sensor was used, whereas an Optech Titan sensor was used during S 2 (March 2016) and S 3 (March 2017) (Figure  3 ). Using these data, high-resolution digital elevation models (DEMs; cell size of 0. 25 m 2 ) were produced using Arcmap software v. 10. 3. DEMs of difference (DoDs) were established to calculate the channel elevation changes for each period (P i ). Field measurements of water depths and flow velocities and visual estimates of the bed grain size were conducted along cross-sections (n=18) at a spacing of 100 m with 12 to 19 sampling points for each cross-section, and the measurements were collected by a private company  [45] [46] [47] [48]. All sampling points were georeferenced by a global positioning system (Promark 700). The same sampling points were recorded during each survey to evaluate the changes in physical parameters as accurately as possible (Figure  2.
c). A rod and a Nautilus C2000 current meter were used to measure the water depth and the flow velocity, respectively, at 0. 4 times the water depth from the channel bottom using a boat. A bathyscope was used to qualitatively estimate the bed grain size according to the ordinal classification of  [49]. The discharges during data collection for S 1 , S 2 , S 3 and S 4 were approximately 67 m 3. s -  , 52 m 3. s -  , 70 m 3. s -  , and 70 m 3. s -  , respectively (Figure  2 ). estimated by  [50]. Stars correspond to topo-bathymetric surveys, squares correspond to the collection of physical parameters, and the grey arrow denotes the end of the restoration.
Hydraulic models A two-dimensional hydraulic model was performed for each state (S i ) by using HEC-RAS software (v. 5. 0. 3). The topo-bathymetric data for the models were provided by high-resolution DEMs. Each model run was calibrated with the field measurements of the water depths and flow velocities performed at each S i and correlated with the daily discharges provided by the Rheinweiler gauging station (Figure  1. b; Figure  2 ). Sensitivity analyses of the Manning coefficient were performed to determine the best calibration values for modelling purposes. The values of the Manning coefficient varied over the four S i from 0. 025 to 0. 030, similar to the values proposed by  [52]  and  [53]  for rivers similar to this by-passed reach. For each state, we modelled the water depths and flow velocities for the studied reach at 52 m 3.
Fish habitat models and metrics CSI i = ∏ (SI ij ) 1/n (6) where i, j and n correspond to the cell in which the computation is occurring, the physical parameter being multiplied and the number of physical parameters, respectively. From the CSI, we calculated the habitat suitability index (HSI) to estimate the habitat suitability at the section scale and calculated it as follows  [28, 58, 59] : HSI = 1 A * n ∑ i=1 a i * CSI i ( 7 ) where a i is the surface area of cell i and A is the cross-sectional area of the section being studied (upstream, impacted and downstream sections). All indices were calculated by using ArcGIS v10. 3 software based on Python scripts and calculated from the results of the two-dimensional hydraulic model runs. In addition, we calculated the following three metrics using FRAGSTATS software v.
Acknowledgments: This research was supported by the  company Electricité de France (EDF)  within the research collaboration "Redynamization of the Old Rhine -years  2014-2018" (EDF 5910132058 ). The authors thank their colleagues who provided fieldwork assistance:  J. Houssier ,  J. Serouilou ,  G. Skupinski ,  D. Eschbach  and  F. Bruckmann. The authors also thank ECOTEC, which provided the flow velocity, water depth and grain size field measurements, and  Rennes University  for the airborne LiDAR topo-bathymetric surveys. This work was performed within the framework of the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of the  University of Lyon  within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR).
Gene annotation Protein-coding genes were annotated from the genome sequences using MAKER v2. 31. 8  [70]. For this annotation, first, repetitive elements were masked using RepeatMasker v4. 0. 3  [71]. Second, ab initio gene prediction was performed with protein-coding sequences from two strains in S. frugiperda  [31]  and Helicoverpa armigera (Harm_1. 0, NCBI ID: GCF_002156995), as well as insect protein sequences from Drosophila melanogaster (BDGP6) and three Lepidoptera species, Bombyx mori (ASM15162v1), Melitaea cinxia (MelCinx1. 0), and Danaus plexippus (Dpv3) in ensemble metazoa. For transcriptome sequences, we used a locally assembled transcriptome from RNA-Seq data from 11 samples using Trinity v2. 0. 6  [72]. Third, two gene predictors, SNAP v2006-07-28  [73]  and Augustus v3. 3. 1  [74] , were trained to improve gene annotations.
Resequencing evolved clones revealed a large number of mutations in our experiment, although the strains were not constitutive hypermutators. Additional sequencing and experiments revealed that bacterial clones underwent transient hypermutagenesis (~5-10× increase in mutation rate) during each exposure to the plant culture medium, with a further increase (~4×) in the presence of Mimosa pudica plantlets. We identified error-prone DNA polymerases from the imuABC family that were located on pRalta and responsible for this transient hypermutagenesis  [144]. In our system, SIM accelerates adaptation since, when re-evolved for 5 additional cycles, clones carrying imuABC genes (like our original ancestor) adapted faster than isogenic imuABC mutants evolved in the same conditions  [144]. Interestingly, the interrogation of genomic databases revealed than more than half of symbiotic plasmids carried error-prone DNA polymerases.
Funding: This research was funded by the  French National Research Agency  ( ANR-16-CE20-0011-01 ) and the  Laboratoire d'Excellence (LABEX)   TULIP  ( ANR-10-LABX-41 ). G. G. D. d. M. was supported by a fellowship from the  French Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI). P. R. was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska Curie  grant agreement No  845838.
Observations The phenological data were collected from 15 experimental vineyards in three European countries (France, Luxembourg and Portugal, see Figure  1  and Table  1 ). The weather data (i. e. , daily maximum and minimum air temperature, • C) were freely available from weather services. More specifically, for France, the land-based daily weather data were collected by the National Centers for Environmental Information and then archived at NOAA (https://www. ncdc. noaa. gov/ [34] ) and from a weather station near the VitAdapt project  [35]. Hourly temperatures, rainfall, wind speed and reference evapotranspiration were recorded and stored in Climatik, a database managed by INRA, US 1116 Agroclim research station, 84914 Avignon, France (https://intranet. inra. fr/climatik_v2; limited access).
For Luxembourg, the weather data were collected by the Administration des Services Techniques de l'Agriculture at the Institut Viti-Vinicole (https://agriculture. public. lu/) while the weather data for Portugal derived from the Instituto Nacional de Investigação Agrária e Veterinária (INIAV http://www. iniav. pt/) and the Associação para o Desenvolvimento da Viticultura Duriense (ADVID https://www. advid. pt/). The weather data were obtained from the closest and more representative stations for the vineyard weather conditions (Figure  1  and   The objective of this study is to evaluate the performances of six phenological models (BRIN tested using hourly and daily temperature) at reproducing the budbreak date of eight grapevine varieties collected at different latitudes in Europe. In order to consider the influences of temperature during the dormancy period on model representations, two starting dates were set during chillingforcing and forcing models' simulation, respectively.
Observations The phenological data were collected from 15 experimental vineyards in three European countries (France, Luxembourg and Portugal, see Figure  1  and Table  1 ). The weather data (i. e. , daily maximum and minimum air temperature, °C) were freely available from weather services. More specifically, for France, the land-based daily weather data were collected by the National Centers for Environmental Information and then archived at NOAA (https://www. ncdc. noaa. gov/ [34] ) and from a weather station near the VitAdapt project  [35]. Hourly temperatures, rainfall, wind speed and reference evapotranspiration were recorded and stored in Climatik, a database managed by INRA, US 1116 Agroclim research station, 84914 Avignon, France (https://intranet. inra. fr/climatik_v2; limited access).
For Luxembourg, the weather data were collected by the Administration des Services Techniques de l'Agriculture at the Institut Viti-Vinicole (https://agriculture. public. lu/) while the weather data for Portugal derived from the Instituto Nacional de Investigação Agrária e Veterinária (INIAV http://www. iniav. pt/) and the Associação para o Desenvolvimento da Viticultura Duriense (ADVID https://www. advid. pt/). The weather data were obtained from the closest and more representative stations for the vineyard weather conditions (Figure  1  and Table  1 ). The phenological data were collected for eight grapevine varieties (Cabernet Sauvignon, Chardonnay, Gewürztraminer, Grenache, Pinot Gris, Riesling, Touriga Franca and Touriga Nacional). For France, these data were retrieved from PERPHECLIM platform (https://www6.
inrae. fr/projetaccaf-perpheclim_eng/The-PERPHECLIM-project [36] ) and VitAdapt project  [35]. For Luxembourg, the data were obtained from the Administration des Services Techniques de l'Agriculture at the Institut Viti-Vinicole. Finally, the data for the Lisbon wine region (Portugal) were obtained from the national ampelographic collection at the INIAV, located in Dois Portos. The data for the Douro wine region were collected by the associates from ADVID. Most of these grapevine varieties are for high-quality wine production derived from the world's benchmark regions and can be grouped on the basis of average growing season temperatures and ripening potential  [37]. The phenological observations were collected by different research institutions and farmers at budbreak date which typically corresponded to BBCH 09  [38] , except for Cabernet Sauvignon, Touriga Franca and Touriga Nacional collected in France (Bordeaux site; BBCH 07).
Outliers Removal The model fitting, validation and application were performed after removing outliers from the observed dataset. This procedure consists in the identification of possible anomalies by comparing the observed and simulated results obtained by a preliminary models' fitting against the original datasets for each variety. The outliers were identified by using the Tukey method  [43] , based on the non-parametric measure of the statistical dispersion of a given distribution quantified using the interquartile range (IQR). For each model and starting date, the outliers represent the values in which the difference between the observed and simulated budbreak Days of Year (DOYs) are outside the whiskers of the distribution (below Q1 -1. 5 IQR or above Q3 + 1. 5 IQR values). An outlier of a specific variety is removed from the dataset when it is observed in at least 4 combinations (models x starting date).
The percentage of outliers removed from the fitting, validation and application datasets is: 4. 17% (Cabernet Sauvignon), 5. 36% (Chardonnay), 1. 85% (Gewurztraminer and Riesling), 3. 70% (Grenache), 1. 52% (Pinot Gris), 10% (Touriga Franca and Nacional). These values were generally observed after extreme weather conditions (e. g. , frosts of 1956, 1963 in France) or due to other influences (e. g. , agronomic practices or errors in data collection) which cannot be reproduced during model simulation.
Each fitted model was validated on the remaining 40% of the data and then applied on the validation datasets of the other subsamples by using the R software environment (version 3. 5. 0) [52]. Thus, the parameter set of each model combination (model x variety x starting date), which provided the highest-performance (HP) during fitting and validation procedures, was selected. Moreover, the average parameter sets (AV) derived from all fitted subsamples was also tested on the entire distribution of validation datasets. Finally, the HP and AV parameter sets were applied to the independent datasets of other study sites to evaluate the behavior of different parameterizations under different environmental conditions. Finally, the minimum, 10% quantile, median, mean, 90% quantile and maximum values of the statistics were extracted for describing the simulation results on all validation datasets.
Model Applications on Other Independent Datasets The statistical performances of the phenological models, applied on the respective independent datasets, are reported in Figure  5  and Figures S10-S16 while the comparison between CF and F models during fitting and applications is shown in Figure  6  and Figure  S17. In general, higher performances were found for CF compared to F models during fitting and applications (Figure  5 ), except for those varieties characterized by few observed data in the model application (Gewürztraminer, Riesling; Figure  6  and Figure  S17 ). Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www. mdpi. com/journal/applsci.
Model Applications on Other Independent Datasets The statistical performances of the phenological models, applied on the respective independent datasets, are reported in Figures  5  and S10 -S16 while the comparison between CF and F models during fitting and applications is shown in Figures  6  and S17. In general, higher performances were found for CF compared to F models during fitting and applications (Figure  5 ), except for those varieties characterized by few observed data in the model application (Gewürztraminer, Riesling; Figures  6  and S17 ). In particular, the model application showed similar performances for both HP (R 2 = 0. 55, AIC = 57. 97, RMSE = 9. 41) and AV (R 2 = 0. 54, AIC = 58. 37, RMSE = 9. 26) parameter sets. However, this difference slightly increased when CF (R 2 = 0.
56, AIC = 59. 05, RMSE = 8. 75) and F models (R 2 = 0. 52, AIC = 57. 01, RMSE = 10. 01) were compared without distinguishing between HP and AV parameterizations. Therefore, CF models showed better results in terms of R 2 (+8%) and RMSE (-15%) Figure  5. Highest-performance (HP) and average (AV) parameters' set applied on the independent datasets by considering all phenological models and starting dates. The example of Chardonnay variety. models, the better results were provided by UNIFORC (R 2 = 0. 54, AIC = 55. 98). However, in terms of RMSE, highest performances were found for BRIN hourly (8. 37, CF) and GDD (9. 67, Concerning the starting dates, similar results were found when the 1 st September (R 2 = 0. 58, AIC = 58. 92, RMSE = 8. 55) and 1 st of August (R 2 = 0. 57, AIC = 59. 17, RMSE = 8.
The extensive observed dataset coupled to the methodology applied in this study allowed to evaluate the influence of study site-temperature variability on the grapevine development. Moreover, the cross-validation scheme adopted in this model intercomparison experiment provided an exhaustive analysis of the bias and variance of the CF and F models while the comparison between two different types of parameterization (HP or AV) pointed out the issue to select a specific parameter set for model applications (Results section, Figure  5 ). This in-depth investigation of model fitting, validation and application was particularly useful for evaluating the influence of temperature during the dormancy period. In this context, the different model responses during budbreak predictions are related to the physiological requirements needed for dormancy fulfilment of the grapevine varieties.
Indeed, the goodness of F models is shown to significantly increase when the starting date is optimized against observed experimental data  [30, 45] , demonstrating that the beginning of eco-dormancy period is dependent to the origin of the observed dataset and, thus, on the fulfilment of specific physiological requirements that change in relation to the climate conditions of the different environments. In fact, although no relevant differences were found for budbreak date estimation of grapevine varieties using CF models set on 1st of September/August starting dates, the discrepancy of the F model simulations was evident when 1st of January/March dates were used. Higher accuracy of F models to predict the budbreak date of grapevine varieties collected in Northern European countries was observed using the 1st of March starting date as also confirmed by  [27] , while the 1st January seems to be a more suitable starting date for representing budbreak date of the grapevine varieties from Southern Europe (Figures  S5-S7 ).
[63]  in a two-year ad hoc experiment with different grapevine varieties and temperature regimes. However, the starting date for thermal unit accumulation depends on the physiological features of the grapevine variety and their interaction with the environment. Indeed, we found that the effect of temperature during the last part of winter and beginning of spring was also evident when models were tested on external sites (independent datasets, Figure  5 ). Although most of the models showed satisfactory results during application (R 2 and AIC), the average error (RMSE) between observed and simulated data was relatively high (from 4 to 19 days). This situation was exacerbated in F models where the reliability in simulating budbreak is dependent to the use of a specific starting date (1st of January/1st of March) and thus the original observed dataset.
Indeed, the findings of our model simulations evidenced distinct thermal requirements for grapevine varieties cultivated at different latitudes (i. e. , Northern Europe vs. Southern Europe). The use of different starting dates highlighted that, unlike CF models, the accurate estimation of budbreak in F models is affected by the selection of a starting date for beginning of the eco-dormancy period. This implies that, if this phase is not accurately defined, F models may lead to over-or under-estimations of the budbreak date under different environmental conditions. In this context, comprehensive phenological datasets (e. g. , grapevine phenological data collected at different latitudes) are needed to thoroughly investigate the effect of temperature variability on dormancy period and budbreak estimation.
Acknowledgments: We wish to acknowledge the  Clim4Vitis  project " Climate change impact  mitigation for European viticulture: knowledge transfer for an integrated approach", funded by  European Union 's  Horizon 2020 Research and Innovation Program , under grant agreement no. 810176  that supported this study. H. F. thanks the  FCT  for contract  CEECIND/00447/2017  and project  COA/CAC/0030/2019.
Funding: This research was funded by the  European Union 's  Horizon 2020 Research and Innovation Programme , under the  Clim4Vitis  project: " Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach ", grant agreement no. 810176. It was also supported by  FCT-Portuguese Foundation for Science and Technology , under the project  UIDB/04033/2020  and the  French National Research Agency (ANR)  in the frame of the Investments for the Future Program, within the cluster of excellence  COTE  ( ANR-10-LABX-45 ).
Comparison with O. sativa Moreover, we looked for polymorphisms at RYMV1, CPR5-1 and NLR RYMV3 in O. sativa based on the SNP-Seek database  [25] , which pools genotyping data from the 3000 Rice Genomes Project  [26]. Seventeen nonsynonymous mutations were identified in RYMV1. They resulted in amino-acid substitutions or small deletions, but only three occurred in the middle domain of the eukaryotic initiation factor 4G (MIF4G), where all mutations conferring resistance to RYMV were located (Additional file 1: Table  S6 ). One of them (A303D) was present only in the few O. glaberrima accessions included in the 3000 Rice Genomes Project, as well as in all accessions from our O. glaberrima collection. This mutation was therefore considered to be specific to O. glaberrima and not associated with resistance to RYMV.