dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Funding. The study was supported by  DompeFarmaceutici SPA and Paolo Procacci Foundation , through unconditioned grants. The terms of the financial support from  Dompe Farmaceutici SPA and Paolo Procacci Foundation  included freedom for the authors to reach their own conclusions and an absolute right to publish the results of this work, irrespective of any conclusions reached. DompeFarmaceutici SPA did not have any role in design, planning, or execution of the review. The journal's rapid service and open access fee were funded by DompeFarmaceutici SPA. Editorial Assistance. The authors wish to thank  Jo Ann LeQuang of Leqmedical  for English revision and additional editing of the paper. Her support was generously offered by  NEMA Research Ltd. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take the responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
T he Microbacterium genus is composed of ubiquitous high-GC-content Gram- positive Actinobacteria. Despite its presence in many environments and its potential in bioremediation processes (1-7), the bacterial genus Microbacterium is underrepresented in the genome databases and is still poorly studied. At the time of writing, there were more than 100 Microbacterium species with a valid name (http://www. bacterio. net/microbacterium. html) and 323 genomes, including 28 complete genomes, available in the databases (https://www. ncbi. nlm. nih. gov/assembly/?termϭmicrobacterium). Here, we report the complete genome sequences of four members of this genus, which were isolated from metal-rich or radionuclide-rich soil samples. The Microbacterium oleivorans strain A9 was isolated from radionuclide-contaminated soil in Chernobyl  (8)  and exhibits a high uranium tolerance  (9).
In the first step, genome de novo assembly was performed on the PacBio reads using Canu software version 1. 0 with default parameters  (14). Trimming and circularization in a single genome assembly were done using this tool. Then, to improve the quality of the genome sequence, a correction was made using the Illumina reads with the MIRA assembler version 4. 0. 2 (15) on the complete genome sequence (assembled using Canu and oriented on the origin of replication [oriC] by an in-house pipeline) as reference. We used the default configuration for MIRA except for the option job, which was set to "genome," "mapping," and "accurate," and for the option parameters, which were set to SOLEXA_SETTINGS -CL:pecϭyes COMMON_SETTINGS -NW:cacϭno -SK:mmhrϭ1. The accession numbers, assembly metrics, and genome characteristics for each genome are listed in Table  1.
All chromosomal sequences were circularized and oriented with the predicted oriC region as the beginning of the sequences. Taxonomic assignment at the species level was provided by the NCBI using their quality control test for bacterial genomes. This test uses average nucleotide identity (ANI), which compares the submitted genome sequence against the genomes of the type strains and proxytype strains that are already in GenBank, as described in reference 16. The chromosomal sequences were annotated with the NCBI Prokaryotic Genome Annotation Pipeline version 1. 2 with default parameters  (17). Two-component system proteins (TCSs) and transcription factor proteins (TFs) were identified using the P2RP Web server version 2. 7 with default parameters  (18). Data availability. The raw data and whole-genome sequences of the four Microbacterium strains have been deposited in the GenBank database under the accession numbers listed in Table  1.
Indeed, as a preliminary step toward the joint use of both approaches via data assimilation, the purpose of this study is the comparison of the TSEB model  (Norman et al. , 1995)  and the SEtHyS SVAT model (described in  Coudert et al. , 2006) , in terms of overall performance, errors and domains of validity where model inputs and parameters are uncertain. This is carried out here using a large and unique in situ database covering several crops and seasons under relatively well-watered conditions and in a limited advection environment. This paper is organized as follows. After briefly introducing the datasets and both models (Sect. 2), the analysis of the models' performance is presented (Sect. 3. 1). Then, we focus on the sensitivity analysis results (Sect. 3. 2) and on discussions related to parameters and inputs (Sect.
Sites description and data The experimental dataset was gathered in the southwest of France  (Béziat et al. , 2009)  and the southeast of Morocco  (Chehbouni et al. , 2008; Jarlan et al. , 2015)  as shown in Fig. 1. As presented in Table  3 , all necessary data to run, calibrate and evaluate models were collected over three agricultural sites, spanning a total of seven culture cycles from seeding to harvest: four wheat (Triticum aestivum L. ) crops, one sunflower (Helianthus annuus L. ) crop and two corn (Zea mays L. ) crops. Experimental sites differ by management practice (culture rotation and irrigation), soil properties, topography and climate (six temperate crop cycles and one semiarid crop cycle). The Auradé (43. 55 • N, 1. 11 • E) and Lamasquère (43. 50 • N, 1. 24 • E) experimental sites are located near Toulouse in southwestern France and are part of the "Sud-Ouest" project  (Dedieu et al.
, 2001; Béziat et al. , 2009). Both experimental sites experience a temperate climate. A rotation of wheat and sunflower is cultivated in Auradé, whereas a rotation of wheat and irrigated corn is cultivated in Lamasquère. A complete description of the site features and datasets are presented in  Béziat et al. (2009). The Sidi Rahal (31. 67 • N, 7. 60 • W) experimental site is located on the Haouz Plain in central Morocco and is part of the "SUDMED" project  (Chehbouni et al. , 2008; Jarlan et al. , 2015). It is part of an irrigated agricultural area that experiences a semiarid climate, where wheat is the most widely grown crop. More information about the site and dataset is given in  Duchemin et al. (2006). Each experimental station collected standard meteorological data at 30 min time step intervals: global incoming shortwave and longwave radiation (R g and R a , respectively), wind speed (U a ), air temperature (T a ), atmospheric pressure (P a ), relative humidity (RH) and rainfall.
Vegetation height (h c ) and the green leaf area index (LAI) were collected periodically along crop cycles and interpolated using the piecewise cubic Hermite algorithm. Green LAI was determined from destructive measurements with a LiCor planimeter (LI-3100, LiCor, Lincoln, NE, USA). In order to obtain an estimation of the fraction of green (f g ), total LAI (LAI green + LAI yellow ) was extrapolated from green LAI data by applying a linear decrease starting at the LAI maximum and ending at harvest with a value of LAI total = 0. 8×LAI max. In order to assess the potential loss of accuracy of meteorological inputs at the landscape scale and the impact on model simulations, SAFRAN reanalysis data  (Quintana-Seguí et al. , 2008)  were used within this study. SAFRAN is based on an optimal interpolation between a background estimate obtained from Météo-France's numerical weather prediction model (ALADIN) and weather station observations, except for precipitation, which relied on the ground station network only, and the incoming radiation fluxes (downwelling surface shortwave and longwave), which were computed from the radiation scheme of  Ritter and Geleyn (1992)  from the outputs of a numerical weather forecast model and the solar constant at the top of the atmosphere (for shortwave incoming radiation).
1 < LAI < 0. 3), growth stage (rapid increase of LAI and LAI ≈ 1), maximum development stage (around LAI maximum value) and senescence stage (LAI decreases). Starting days of periods were adjusted to optimize the quality of available data, as the datasets are subject to sporadic measurement issues and energy closure inconsistencies (filtered to a minimum of 80 %). In order to better assess the differences of model skills during stress periods, water stress is quantified along the whole crop cycles using two indicators: the evaporation stress (SE,  Boulet et al. , 2007).
related to the ratio between real and potential evapotranspiration events: SE = 1 - LE LE pot , (15) where LE pot is computed using the Penman-Monteith equation (canopy resistance is estimated following Jarvis, 1976 formulation with a minimum value of 90 s m -1 ); the soil wetness index (SWI, Douville, 1998, among others) of the root zone ranging from zero at wilting point to one at field capacity: SWI = W 2 -W wilt W fc -W wilt , ( 16 ) where W 2 is the root zone water content, W fc is the water content at field capacity, and W wilt is the water content at wilting point. As cultures from our dataset are irrigated or in temperate areas, most stress periods are found during senescence phases, when water resources are low or irrigation is stopped. The model skills are assessed using classical statistical metrics including the root-mean-square error (RMSE), the mean absolute percentage deviation (MAPD), bias and the determination coefficient (r 2 ).
Implementation of the models Considering our objective was to compare a complex SVAT model with the TSEB tool (which was taken as an example of a simple and robust approach), a different strategy was applied for the implementation of the two models. The 22 parameters of the SEtHyS model were finely tuned for each crop and each phenological stage. The objective of this calibration was not to fit the data best, but rather to evaluate the sensitivity of model outputs to potentially poorly calibrated parameters when the model is to be applied to an heterogeneous agricultural landscape at the field or intra-field scale. To this end, four different cases corresponding to four different sets of parameters were considered to quantify the potential loss of performance due to incorrect parameter values.
What we consider the "best" case is very unlikely for a spatialized application of the tool, as even the largest available database will never cover all of the conditions encountered at the scale of an heterogeneous agricultural landscape where each plot has its specific soil, technical itinerary, hydric status, an so on. Thus, our objective is to get different parameter sets with values close to what is expected for each type of condition (crops, climate, site, phenological stage, etc. ) but without assigning too much importance to the values themselves. To help perform the calibration, a stochastic multi-objective calibration method (multi-objective calibration iterative procedure, or MCIP;  Demarty et al. , 2004 Demarty et al. , , 2005) )  is implemented in order to minimize the RMSE values between simulations and measurements at 30 min time intervals.
Five objective functions are identified: the RMSE of H and LE fluxes, surface brightness temperature (T b ), net global radiation (R n ) and root zone soil water content (SWC). An ensemble of simulations based on a Monte Carlo sampling of the parameter space is carried out, and the objective functions are optimized jointly following a Pareto ranking. The basic principle is that a simulation is classified as "better" than the others if all of the objective functions have lower values. For more details on the MCIP methodology, see  Demarty et al. (2004 Demarty et al. ( , 2005)). For TSEB, the most robust configuration of the model is sought. To this end, as a first step, the most sensitive parameters for convection fluxes prediction are calibrated on the whole database at once  (Diarra et al.
05 and 0. 7. Still with the goal of identifying the most robust configuration of the TSEB model, several refinements proposed by different authors to improve models' prediction for specific crop and climate conditions were also tested with our database. The Priestley-Taylor formulation, although relatively simple, provides accurate potential transpiration in a wide range of conditions but neglects the aerodynamic resistance. Colaizzi et al. (2014)  proposed replacing the Priestley-Taylor expression with the Penman-Monteith equation  (Monteith, 1965) , in particular for advective conditions; thus, the increase in the value of the α PT parameters (as proposed by  Kustas and Norman (2000)  for such conditions) could be avoided. Unfortunately, the Penman-Monteith version worsened the results by about 6.
3 % on average over the whole database and was not retained in this study. Several parameterizations aiming to represent the diurnal course of have also been proposed. Those of  Santanello et al. (2003)  and  Chávez et al. (2005)  have been tested. The overall performance of the model with respect to predicting convective fluxes was slightly worsened on average except under some very specific conditions. Interestingly enough, a multiplicative factor for κ lower than 1 was proposed by  Campbell and Norman (1998)  in the divergence equation (Eq. 9) to take the clumping of some crops into account; this clumping may intercept a lower portion of the incoming radiation compared with leaves that are randomly distributed  (Kustas and Norman, 1999). The calibrated values of κ for sunflower and maize are above the 0.
To a lesser extent, SEtHyS skill is also mitigated during the rising phase. Generally, when evaporation is predominant over transpiration, more weight is given to soil transfer processes that are harder to characterize due to the high heterogeneity of soil characteristics and the limited soil measurements available for calibration. The poor performance is more conspicuous with TSEB, leading to the estimation of H with a MAPD of 85 %. By contrast, both models tend to show better performance when vegetation is fully developed (MAPD less than 23 % for LE). The model performance by crop and growth stage is detailed in Fig. 3a  and b , respectively, as normalized Taylor diagrams  (Taylor, 2001). This diagram is a concise way to display the ratio between the variances of the model outputs and the observed data, the correlation coefficient (r), and the RMSE between model estimates and observations normalized by the variance of the observed dataset.
Overview Given the overall purpose of our research, which was dedicated to the spatialized estimation of evapotranspiration at various scales, quantifying the decrease of model performance due to the deterioration of input data quality combined with change of spatial scale from the field to a heterogeneous agricultural landscape is a prerequisite. Applying the models at the landscape scale is not performed the same way for both approaches: TSEB is designed to be driven by remote sensing data with ET computed directly at the resolution of the TIR pixel, whereas SEtHyS is spatially distributed by separately computing fluxes at the crop scale for each homogeneous entity. As a consequence, both models' performance is expected to exhibit sensitivity to the quality of auxiliary spatialized meteorological and vegetation forcing variables, TSEB's performance is expected to depend on the quality of the TIR data, and SEtHyS's performance is expected to depend on the quality of the description of the state of each homogeneous entity (i.
e. , soil water content initialization, and sets of parameters describing soil properties and vegetation behavior). The specific purpose of this section is twofold: (1) to identify the most sensitive inputs and (2) to quantify the expected model performances when realistic input errors are introduced. Expected uncertainties on input variables have been evaluated by comparing available in situ data to the spatialized datasets (SAFRAN meteorological reanalysis and ASTER, LANDSAT and FORMOSAT-2 satellite imagery and products). Results are presented in Table  5  and details are given in the following sections.
Sensitivity analysis to vegetation forcing inputs The focus here is on evaluating the bias effect on SEtHyS and TSEB flux predictions. Indeed, on the one hand, errors on vegetation characteristics are much more difficult to evaluate as in situ measurements are time-consuming and therefore not always available at a small time interval. On the other hand, biases on satellite estimates are more likely to occur than white noise errors due to the detection limit of visible sensors in the case of sparse vegetation and a possible saturation effect when the leaf area index is above 3 m 2 m -2. On average, Claverie (2011); Claverie et al. (  2012 ) highlight a potential bias of 20 % for the LAI estimated from  FORMOSAT data. Canopy height (h c ) is not directly available from remote sensing data but can be estimated from the LAI.
However,  Colaizzi et al. (2014)  noted that larger α PT values did not mitigate the discrepancies on the evaporation (E) and transpiration (T ) components of the total latent heat flux (ET). These authors proposed a revised version of TSEB, replacing the Priestley-Taylor formulation with the Penman-Monteith equation in order to better account for large variations of water vapor pressure deficits and correct the evaporation, transpiration and total LE simulations. Thus,  Boulet et al. (2015)  built the SPARSE model based on Penman-Monteith; this model showed satisfying performance with respect to the Morocco wheat site dataset, which was better than that of TSEB with default parameter values.
To this end, this study takes advantage of a large and unique in situ database spanning two climates and seven different crop cycles. The main results drawn from this study can be summarized as follows: -On average, over the entire database, both models provide close statistical metrics with respect to daily average values of LE (RMSEs of 36 W m -2 for SEtHyS vs. 39 W m -2 for TSEB), whereas TSEB is slightly bet-ter regarding H predictions (21 vs. 29 W m -2 ). This emphasizes the remarkable performance of the TSEB model compared with the relative simplicity of the approach, all the more given that SEtHyS parameters are calibrated for each crop, each phenological stage and each site. -SEtHyS skill appears more stable regardless of the growth stage or crop, whereas limitations of the TSEB model are clearly emphasized during rising and senescence stages.
In addition to the characterization of the model and background errors, this study provided some insights to guide the implementation of a data assimilation algorithm at the scale of an agricultural landscape for the joint use of both approaches by highlighting deficiencies in specific conditions. Nevertheless, our current database suffers from a lack of hydric stress conditions and does not allow for the precise characterization of this crucial aspect. A new experiment in  Morocco (seasons 2017 Morocco (seasons -2018 Morocco (seasons and 2018 Morocco (seasons -2019) )  focusing on water stress on a wheat field is currently being carried out. Our perspectives will focus on the exploitation of TIR data by using TSEB as a proxy to be assimilated in SEtHyS following  Crow et al. (2008) , but also by taking advantage of the MCIP methodology to tune parameters to better fit surface temperature measurements following  Coudert et al.
Acknowledgements. The authors wish to acknowledge  USGS ,  NASA  and  JPL  for providing LANDSAT and ASTER thermal infrared data. This work was also partly supported by the  French PNTS and TOSCA programs  (incorporating the " Assimilation multicritère dans la modélisation TSVA: complémentarité des grands domains spectraux "  PNTS  project by  Jérome Demarty ; the " Validité des estimations d'évapotranspiration basées sur l'utilisation de données infrarouges thermiques "  PNTS  project by  Dominique Courault ; and the EVA2-IRT  TOSCA  project by  Gilles Boulet  and  Albert Olioso ); the  Seventh Framework Programme  ( FP7 ) incorporating the  SIRIUS  project; the  MIS-TRALS ENVIMED  and SICMED programs; and the  Joint International Laboratory TREMA  (http://www. lmi-trema. ma/,last access: 13 December 2019), which allowed for collaboration with  UCAM University  for the application of the models on the Morocco site.
The development of our methodologies was performed within the framework of the future TRISHNA mission of the  French space agency (CNES)  and the  Indian Space Research Organization (ISRO). Finally, the authors would like to thank  Martha Anderson  and  William Kustas  from  USDA-ARS  for the dissemination of the TSEB model through the community which allows for such research. Financial support. This research has been supported by the  Institut National des Sciences de l'Univers, Centre National de la Recherche Scientifique  (grant no. AO2011-642675 , LEFE 2011, MISTRALS/SICMED, and  MISTRALS/ENVIMED programs) , the  Centre National d'Etudes Spatiales  (grant no. EVA2IRT ), and the  Seventh Framework Programme  (grant no. 262902 -SIRIUS ). The PhD scholarship of  Guillaume Bigeard  has been funded by the  French Ministry of Research.
CSF-1R interacts with EGR1 to downregulate gene expression. Since CSF-1R is recruited on EGR1 motifs and EGR1 is a transcription factor involved in monocytopoiesis  23  , we performed co-immunoprecipitation experiments in whole monocyte extracts, which demonstrated an interaction between the two proteins (Fig. 4a ). We reasoned that CSF-1R and EGR1 peaks might colocalize in genome-wide analysis. ChIP-seq experiments were performed on two healthy donor monocyte samples with an anti-EGR1 antibody. Broad peak calling using MACS2 algorithm identified 3542 common peaks (Supplementary Data 3). A large fraction of EGR1 peaks (56. 9%) co-localized with CSF-1R peaks and were mainly localized to intergenic regions (Fig. 4b , Supplementary Fig. 3a  and Supplementary Data 4). Ranking heatmap centered on CSF-1R common peaks further showed their colocalisation with EGR1 and H3K4me1 while being distant from H3K4me3 peaks (Fig.
05% saponin during 1 h at room temperature (RT), followed by wash. The secondary antibody was added (10 µg/mL) with 0. 05% saponin during 30 min followed by wash. Slides were mounted using Vectashield mounting medium with Dapi (Vectorlabs, Clinisciences). Specimens were examined on Leica confocal microscopes (TCS SPE and SP8, Leica Microsystems, Nanterre, France). The nuclear CSF-1R fluorescence was quantified using ImageJ software. The nucleus areas were selected in each cell using Dapi staining before measuring the mean intensity of CSF-1R fluorescence inside each nucleus with ROI manager. Live cell imaging movie and orthogonal view were created with ImageJ software. CSF-1 chemical modification. Human recombinant CSF-1 (Peprotech) was coupled with Alexa Fluor 488 using the Alexa fluor 488 Microscale Protein Labeling Kit (ThermoFisher Scientific) according to the supplier instructions.
CRISPR Knockout. Two EGR1 guide RNA sequences and Cas9 were encoded in V2 CRISPR-GFP or Cherry plasmid (one for each guide). Guide sequences were designed in the first exon of EGR1 with CRISPOR software (http://crispor. tefor. net/) and cloned in one of the two lentiviral vectors: EGR1 guide 1 F: 5′-AAACGG CCGGGTTACATGCGGGGC-3′; R: 5′-CACCGCCCCGCATGTAACCCGGC C-3′, EGR1 guide 2 F: 5′-AAACTCGGCGTAGGCCACTGCTTAC-3′; R: 5′-CACC GTAAGCAGTGGCCTACGCCGA-3′. For lentiviral production, 293 T were cotransfected with plasmid of interest along with pCMV and pMD2. G plasmids using jetPRime reagent (Polyplus transfection, Ozyme) according to the manufacturer instructions. Retroviral particles were collected 48 h after transfection and concentrated by ultracentrifugation. THP-1 cells were co-transduced with a MOI of 2.
Acknowledgements This work was supported by  INSERM , the  Fondation ARC  and the Ligue contre le cancer (labeled team). We are grateful to  F. Ling  and  M. Gaudry  who provided us sequencing primers for Luciferase cloning assessment, to  M. Gastou  for flow imaging and cellular compartment separation protocol, to  F. Porteu ,  J. L Villeval , and  F. Louache  for scientific discussions. We thank  Gustave Roussy institute facilities for imaging (Sophie Salome-Desnoulez ) and genomics, and the  Imagine institute  facilities for flow imaging.