dataesr/scientific-paragraphs-categorization huggingface dataset:
Water was changed once every 2 weeks, using an oxygenated mix of water from the Suzon River and dechlorinated, UV-treated tap water. Monitoring. All gammarids were inspected on a daily basis to record the death of any individual. Individuals that were exposed to parasites were dissected immediately after their death to determine their infection status (number of parasites and their development stage). In addition, 150 control and 150 gammarids exposed to parasite eggs were randomly selected at each temperature at the beginning of the experiment. Individuals found dead among them were then measured from body height at the level of the fourth coxal plate 80 using a microscope and Lucia G 4. 81 software (Prague, Czech Republic). Following detection of the first parasites at the advanced acanthella stage (> 1,000 µm) upon dissection, all individuals were checked daily under a binocular microscope to monitor the exact date of the switch between the acanthella stage (ovoid shape, translucent orange color) and the cystacanth stage (spherical and more pronounced opaque color  81  ).
Behavioral tests were then conducted (see below). At the end of the experiments, we measured and dissected all gammarids that were used in behavioral tests, including control individuals. Although the presence of P. laevis has not been recorded in the Suzon River, gammarids from this population can be naturally infected with other acanthocephalan parasite species (Echinorhynchus truttae and Polymorphus minutus), as well as other macro-parasites such as Cyathocephalus truncatus (Cestoda). Such infected individuals were removed from the data set. Measurement of refuge use. The use of refuges by gammarids was recorded on three consecutive occasions (hereafter referred as "rounds") for all infected individuals: 1 day, 8 days, and 16 days after the cystacanth stage was detected. We tested control individuals in a similar fashion in parallel.
We used nominal logistic regressions to investigate the success of infection (i. e. the proportion of gammarids harboring at least one parasite among those exposed to the infection), and Mann-Whitney U tests to compare the parasite load between the groups as well as the time needed for parasites to reach the cystacanth stage. Scores for refuge use were analyzed as repeated measures using the 'nparLD' R software package. This function is suitable for nonparametric analyses of right-censured longitudinal data, allowing the decrease in sample size along time, due to individuals' death  88. First, the effects of temperature (14 °C vs 17 °C), infection status (control vs infected) and their interaction were investigated along time (rounds of measurements: 1 day, 8 days and 16 days after parasites reached the cystacanth stage).
Acknowledgements We thank  Aude Balourdet ,  Alexandre Bauer  and  Sébastien Motreuil  for technical assistance. This work was funded by the  Agence Nationale de la recherche (ANR , Grant  ANR-13-BSV7-0004-01  to TR), the PhD Grant to SL from the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche  and was supported by the  French "Investissements d' Avenir" program , project  ISITE-BFC INVACLIM.
Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process.
Soil physical properties are used for determining soil available water capacity for root activity and crop growth. In dry environments especially, uncertainties in soil inputs have a predominant influence on yield prediction  [1]. Therefore, this requirement becomes critical if the desired prediction is at a regional scale  [2]. Into a data assimilation approach, remote sensing observations can be used to improve the accuracy of yield predictions and reduce the uncertainty in soil properties inputs. Recent studies have been carried out for various crops with some focus on wheat (  [3] [4] [5] [6] [7] [8] [9] ), maize  ([5,10-13] ) and sugar beet (  [2, 4] ). Most of them rely on leaf area index (LAI) assessments derived from satellite images to correct crop model simulations. Nevertheless, in  [5] , the researchers used soil moisture measurements, arguing that crop-specific variables, such as LAI, were not available for their study case.
Field Network This study targeted plots cultivated with sunflower, during three consecutive years  (2014, 2015 and 2016) , in the Haute-Garonne and Gers departments (southwest of France). A total of 281 sunflower fields (6. 05 ha in average) were monitored throughout the growth season: 76 fields in 2014, 145 in 2015 and 60 in 2016. Commercial grain yields (q•ha -1 , 9% moisture + 2% impurities) were provided exclusively by the farmers. Yield values were sampled either from individual fields (measured using a yield sensor or commercial data: 59%), islets of fields (21%) or even whole farms (20%). Crop management data were collected during farmers' interviews. The following information was gathered: sown variety, sowing date, amount and timing of nitrogen fertilization and irrigation. Rough information on soil depth was provided by the farmers.
The following variables were collected on each field which was observed two to three times: development stages, plant population, field heterogeneity (spatial data), disease injuries (qualitative assessments for mildew, phomopsis, verticillium, premature ripening), weed infestation (% covering), mineral deficiency (e. g. , boron). These data were used for running the crop model, diagnosing yield-limiting factors and discussing the gaps between observation and simulation. Four weather stations were used to provide daily data: maximum and minimum temperature, precipitation, global radiation and evapotranspiration.
Remote Sensing Data Processing An extensive dataset of high resolution optical satellite acquisitions in the visible and near infrared (NIR) was assembled for this study to maximize the number of assimilated data. While the Sentinel-2 satellites provide a very good revisit period, the mission provides data for the 2016 crop season; thus, data from Spot-5 Take-5, Deimos-1, Formosat-2, Sentinel-2 and Landat-8 satellites were considered. The Spot-5 Take-5 data set corresponds to the Spot-5 data during the Take-5 campaign, when Spot-5 had been placed on a five days cycle orbit that mimics the Sentinel-2 acquisition frequency before decommissioning of the satellite (end of 2015), as it was already done in 2013 with Spot-4  [20]. Spot-5 is a third generation Satellite Pour l'Observation de la Terre (SPOT) satellite from the Centre National d'Etudes Spatiales (CNES), the french space agency, providing very high resolution (10 m) images.
The Deimos-1 micro-satellite is a commercial Spanish Earth observation satellite as part of the Disaster Monitoring Constellation (DMC). Deimos-1 provides high resolution (22 m) images with variable incidence angles. Formosat-2 is a commercial Earth observation satellite formerly operated by the National Space Organization (NSPO) of Taiwan. Formosat-2 ceased operations in August 2016. Formosat-2 data consist of high-resolution VNIR with programmable acquisitions at possible daily revisits. Sentinel-2A is the first of the Sentinel-2 satellites and is part of the European Union Copernicus program for Earth observation. The Sentinel-2 satellites provide global high resolution (10 m) multi-spectral observations including the VNIR and the red-edge domains at a five days revisit frequency. Landsat-8, as its name indicates, is the eighth satellite of the Landsat program.
Landsat-8 is a joint mission from National Aeronautics and Space Administration (NASA) of the United States and the United States Geological Survey (USGS). Landsat-8 provides VNIR images at 30 m spatial resolution and 12 days revisit. Landsat-8 also provides thermal imagery, but this information was not exploited in this paper. Images from Landsat-8 covering the period from 2014 to 2016 were extracted. As they provide the longest time series and also cover the acquisition dates of all the other sensors, data from Landsat-8 were used as a baseline for comparing Normalized Difference Vegetation Index (NDVI) in this study. The main characteristics of the platforms mentioned above are summarized in Table  1 , which shows the differences between the sensors acquisition characteristics (spectral bands, spatial resolution and acquisition modes).
All sensor data were available with the MAJA cloud mask processing except for Deimos-1. Images are corrected for geometric, radiometric and atmospheric impacts using either KALIDEOS processing chain (http://kalideos. cnes. fr) or MACCS method  [21, 22]. The MACCS chain enables for cloud and cloud-shadow filtering  [23]  with an absolute geolocation error lower then 0. 4 pixels. The NDVI were averaged over field limits considering a five meters buffer region. The LAI was retrieved using the BVnet [24] tool for 2014 and 2015, and for the following sensors: Landsat-8, Deimos-1, Spot-5 Take-5 and Formosat-2. BVnet is a neural network implementation of the PROSAIL radiative transfer model  [25]  that enables the estimation of biophysical variables such as LAI. It relies on the inversion of the radiative transfer model PROSAIL using artificial neural network.
The BVnet tool uses the green, red and near infrared spectral bands, and the short wave infrared band when available. It computes LAI taking into account for the spectral and directional characteristics (illumination and viewing angles) of the remote sensing data. LAI data were extracted and averaged over the plots excluding a one pixel buffer area. Cloud mask conditions were then applied. To homogenize the Sentinel-2A data with the other time series, the Landsat-8 acquisitions which cover the entire time span of the study were considered as reference. Figure  1  shows the total Landsat-8 and Sentinel-2A acquisitions for 2016. It also shows the dates of simultaneous acquisitions of the two sensors within a four days gap and low-cloud cover conditions. Based on the dates of simultaneous acquisitions, the NDVI from Landsat-8 and Sentinel-2A are compared.
This information is generally easier to get from surveys and databases although some residual error exists (e. g. , distance from the weather station, inaccuracies in input timings and amounts). To identify the most important input factors, a screening approach has been conducted using the Morris method  [35]  on both the maximal value of LAI (at flowering) and the grain yield. The Morris method, by using intensive simulations, computes two indices for all screened input factors: µ * and σ. For a given input factor, these indices are expressed in the unit of the output variable (either grain yield or LAI in our case) and aggregate elementary effects. An elementary effect is the absolute difference of output value due to the difference in the value of one solely input. The µ * index represents the overall linear influence of the input on the output and σ is the non-linear influence of the input plus the influence of its interaction with other inputs.
Data Assimilation Three alternative options for assimilating remote sensing data into the SUNFLO crop model are considered in this work. They are all detailed in Appendix A. The simplest method consists of direct insertion (DI) of the value of LAI into the crop model each day an observation is available. The assumption is that the total dry biomass (TDM) that is closely related to grain yield will be updated by propagation of the observation through the simulation equations. An example of the resulting LAI dynamics is illustrated on a plot of our dataset in Figure  4. A more sophisticated update scheme is the ensemble Kalman filter (EnKF). Rather than updating only the LAI variable into the system at days of observations, nine state variables (defined in Section 2. 3) are updated according to a covariance matrix of these variables.
Experiments Settings and Software The crop model SUNFLO and the EnKF method were implemented in C++ (gcc 5. 3. 0) with the VLE software version 2. 0  [39]  using the modeling platform RECORD  [40]  and the eigen library version 3  [41]. The rest of the experiments were performed with R version 3. 6. 1  [42]. Crop model simulations and optimization (∼2M simulations) were performed on the muse cluster (Montpellier University). For the sensitivity analysis, the Morris method with parameter values r = 100 and levels = 5 from the R packages sensitivity  [43]  was used. That consists in 2400 simulations for each sensitivity analysis. To smooth LAI observations, the implementation of the Whittaker smoother in the R package pracma  [44]  with the parameters lambda = 500 and d = 2 was mobilized, and LAI were extracted from the smoothed sequence every four days.
No setting was required for the open-loop simulation and the DI methods. For the EnKF method, 100 particles were simulated (M = 100 in Appendix A) and an error variance of the LAI observation was set to 0. 2 (R = 0. 2 in Appendix A). For the LSE approach, the genetic optimization algorithm genoud  [45]  from the package rgenoud (version 5. 8) was used with 50 generations and a population of size 16 for each optimization.
Data Assimilation Results Table  4  gives a global overview of the evaluation criteria for the performance of the assessed yield forecasting approaches, over the 281 sampled plots. Bias, root mean squared error (RMSE), normalized RMSE (RRMSE) which is the RMSE divided by the mean of the observations, mean absolute error (MAE) and the coefficient of determination (R2) are listed for all the combinations of the different methods. As a reminder, this evaluation encompasses the effects of the assimilation method (open-loop simulation, direct insertion, the ensemble Kalman filter and least square estimator), the weather series used to complete the simulation ('oracle' or 'past weather') and the LAI variables used during the assimilation process ('raw LAI' or 'smoothed LAI'). Relying on LAI measurements to forecast the grain yield led to better results than the simulation alone, regardless of the assimilation approach and the criterion.
Impact of the Use of Past Weather Series To achieve a complete simulation, a daily weather series is necessary from the day of forecast to the harvesting time. Available past weather series at the location of the plot were used to complete the simulations. The impact of this choice was assessed by comparing the 'oracle' alternative and the 'past weather' dataset. In the 'oracle' dataset, the actual unknown weather series was used to complete the simulations. The mean absolute difference of yield forecasts based on the 'oracle' alternative on one hand, and the 'past weather' alternative on the other hand, was only 0. 02 q•ha -1 and was not statistically significant (with a p-value p = 0. 15 in the non-parametric paired Wilcoxon test). The maximal difference, over the 281 plots, between the two alternatives was also very low (1.
Impact of Yield Limiting Factors To further analyze results with an agronomic point of view, the crop monitoring data that were collected during the growing season on each field was mobilized: the fraction of field area infested by weeds or concerned by severe irregular plant population, and the fraction of plants severely attacked by fungal diseases (e. g. , phomopsis, phoma, verticillium). Situations were considered out of the validity domain of the simulation model (i. e. , yield was limited by factors not included in the model) when weeds were covering more than 25% of the area, when yield loss expected from disease survey was more than 3 q•ha -1 , or if the field had important areas without plants. Based on this categorization of the 281 plots, the methods were compared with respect to the different yield limiting factors (Table  6 ).
Discussion In this study, it has been shown that the availability of accurate soil conditions for the SUNFLO crop model is of crucial importance for yield simulation. The performed sensitivity analysis confirmed the high impact of soil conditions on simulated yield and leaf area index, which is in line with previous works on other crops and other crop models  [1, 2]  and validated these inputs as good candidates for calibration in a data assimilation process. This result was expected as sunflower crop is grown as a rainfed crop in southwestern France, in a context of high summer evaporative demand and moderately deep soils. As stated, water is the main limiting factor for production of sunflower in the region  [46]. Statistically significant improvement was achieved in yield forecasting by assimilating LAI from remote sensing into the sunflower crop model SUNFLO, compared to simulation alone.
It is also foreseen that the increased availability of LAI from optical sensors like Sentinel-2 and proxy-LAI from Synthetic Aperture Radar data like Sentinel-1 from the European Union Copernicus program will enable high temporal frequency of LAI even in cloudy weather. Considering that smoothing LAI reduces the uncertainty of the dataset and corrects for satellite acquisition configurations and sensor differences of the LAI dynamics, except for very extreme events (fire, hail,etc. ), it should remain of interest to use this type of filter.
Conclusions The goal of this study was to investigate the relevance of data assimilation of remote sensed LAI into the SUNFLO crop model in order to forecast sunflower grain yield about one month before harvest. Three approaches were considered: the direct insertion of remote sensed LAI into the crop model, an ensemble Kalman filter approach and a least square estimator approach. A preliminary sensitivity analysis of the crop model was performed to identify the crop model inputs that influence the most the simulations of LAI and grain yield. The impact of smoothing the remote sensed LAI before the assimilation, using a Whittaker smoother, was also assessed. Experiments were conducted near Toulouse, southwest France, on a total of 281 fields monitored over three consecutive years (2014-2016).
Acknowledgments: The authors thank Arterris and  Val de Gascogne  for their help in structuring the field network. They also thank  A. Micheneau ,  H. Gibrin ,  B. Garric ,  Y. Fernandez  and  F. Attia  for their work on this project. R. T. additionally thanks  A. C. Ruane  ( NASA Goddard Institute for Space Studies ) for his comments on this work. This work has been realized with the support of the  High Performance Computing Platform MESO@LR , financed by the  Occitanie Region ,  Montpellier Mediterranean Metropole  and the  University of Montpellier.
Analysis of ankF in 52 C. burnetii Strains Genome assemblies of strains, which had been uploaded at the complete genome, chromosome, scaffold and contig levels and for which information on their genome group classification was known  (Hemsley et al. , 2019) , were identified using the search term "Coxiella burnetii" in NCBI Genome (https://www. ncbi. nlm. nih. gov/genome/), downloaded and used to create a Coxiella-WGS database in Geneious Prime 2019. 2. 3 (Biomatters, New Zealand). Only a single sequence was taken from strains with multiple entries or passage variants. The respective ankF sequences were identified by BLAST analysis of Coxiella-WGS using the ankF coding sequence from the RSA493 Nine Mile strain as reference and the Geneious default parameters. Sequences from two strains were discarded due to sequence ambiguity (Cb171_QLYMPHOMA; CDBG01000000) and a partial sequence at the end of a contig (Q321; AAYJ01000000), so that 52 sequences remained for the mutational analysis.
STED Microscopy For high-resolution STED microscopy, fixed and stained cells on cover slips (12 mm radial cover slips, 0. 17± 0. 005 mm) were visualized with use of an Abberior 3D STED 2-Channel Super Resolution-and resolft microscope (Abberior Instruments GmbH, Göttingen, Germany). Images were acquired with the Imspector software (Abberior GmbH, Göttingen, Germany).
burnetii were in a different CCV, but still in the same cell. Thus, we reasoned that AnkF might be altering the CCV in a way, which allows bacterial replication. Next, we generated a clean AnkF deletion mutant (DankF) and the respective complemented strain to prove the importance of AnkF for intracellular replication. Thus, we infected HeLa cells with wild-type, DankF and DankF::AnkF and analyzed the size of the CCVs and the infection rates at 14 and 60 h post-infection by immunofluorescence microscopy. As shown in Figure  6A  at 60 h post-infection, wild-type C. burnetii establishes large CCVs, while the size of the CCVs harboring C. burnetii lacking AnkF were significantly smaller and the CCVs containing the complemented strain showed an intermediate vacuole size. In addition, while the infection rate at 14 h post-infection was 80% to 90% and did not differ between the three different strains (data not shown), at 60 h post-infection the infection rate of cells infected with the AnkF deletion mutant were reduced by ~40% in comparison to cells infected with wild-type C.
ACKNOWLEDGMENTS This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  through the  SPP1580  to AL, by the  Bundesministerium für Bildung und Forschung (BMBF)  under the project number  01KI1726A/01KI1726C  of "Q-GAPS" as part of the research network zoonotic infectious diseases to AL and CB, and by the  French National Research Agency  ( ANR ;  ANR-14-CE14-0012-01 ) to MB. We thank  Rita A. Eckart ,  Nadine Übelmesser ,  Christian Söldner  for experimental help,  Uwe Appelt  ( Core Facility  for Cell Sorting and Immunomonitoring of the  Universitätsklinikum Erlangen ) for cell sorting and  Philipp Tripal  ( Optical Imaging Center Erlangen (OICE) ) for support with confocal and STED microscopy and with live cell imaging. We acknowledge the Montpellier imaging facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Plasmids Construction The open reading frame (ORF) of AtAMT1;3 (protein accession number: At3g24300) was amplified from a cDNA pool of Arabidopsis thaliana (Col-0) by high-fidelity PCR using the following primers (5 -3 ): GTCGAATTC ATGTCAGGAGCAATAACATGC and GTCTCTAGATTAA ACGCGAGGAGGAGTAGC. After verification by sequencing, the ORF of AtAMT1;3 was cloned into pCI vector for oocyte expression as previously described  (Hao et al. , 2016). Point mutations (see the relevant figures for the positions) were introduced by overlap extension PCR and the mutated sequences were transferred into pCI.
Sequence Alignments, Homologous Modeling and Molecular Docking The full-length amino acid sequences of AtAMT1;3 (protein accession number: At3g24300) and EcAmtB (protein accession number: NP 286193) were aligned using DNAMAN 6. 0. Transmembrane domains (TMs) and conserved amino acid residues potentially involved in substrate binding and poreconfining were highlighted according to the crystallized EcAmtB protein  (Khademi et al. , 2004). Sequence comparison among plant AMTs was also focused within the short regions where point mutations were analyzed. Homology modeling was carried out in collaboration with Beijing Computing Center (Beijing, China) using the crystallized EcAmtB (1U7G) protein  (Khademi et al. , 2004)  as the template by Modeler (Version 4. 0) software. Molecular-docking simulation was performed for the prediction of substrate binding using the AutoDock Vina software (version 4.