dataesr/scientific-paragraphs-categorization huggingface dataset:
The soil texture and soil depth also used in the crop growth model are parameters derived from the French soil database (Référentiel Régional Pédologique,  Party et al. , 2016)  by considering the dominant soil type in each SU. The soils of the agricultural SU show low variability (all clay-loam soils) and all belong to a single soil class. In addition, the three shape parameters of the SU infiltration capacity curve (Equation (  1 )) not defined in the AqYield database, namely, I max , K s and λ, were adjusted as detailed below. Two of the four parameters of the GU storage-discharge function were defined based on an analysis of Gélon outflow discharges during recession periods, while the other two, namely, parameters a and b of Equation (  2.
Most of the necessary data (e. g. , topography, hydrographic network, soil characteristics, nature of crops, and meteorological variables) may be extracted or derived from generic databases, often available throughout Europe. Moreover, as this model is built on already proven models and on widely used equations, some parameters can be fixed from the literature. For example, this is the case for the plant growing coefficients or for the k factor for converting reference evapotranspiration to reservoir evaporation. This makes it easy to envision the use of the model in catchment areas other than the one we studied. However, there is no generic database for all model inputs or for all variables used for its evaluation. In such cases and when available, those data may be derived from local databases, specific surveys or local expertise.
This is particularly the case for data on reservoirs: there is currently no database at the European or even the French level that allows a complete and high-quality description of small water reservoirs. The availability and estimation of the small reservoirs properties and the water use from the reservoirs have remained a real challenge regardless of the approaches used in catchment hydrological modelling with reservoirs  (Hughes and Mantel, 2010; Lowe et al. , 2005). This has motivated the development of remote sensing methods to estimate position and capacity of small reservoirs over large areas  (Ogilvie et al. , 2016). In France, the collective water management structures recently set up in deficit areas ("Organismes Uniques de Gestion Collective de l'Eau") are beginning to create a type of database gathering characteristics and water uses of small reservoirs.
Databases describing agricultural practices are also incomplete, either in terms of geographical location or in terms of content, as explained in  Leenhardt et al. (2010 Leenhardt et al. ( , 2020)). Therefore, this requires the implementation of specific acquisition methods, for example, as presented for cropping systems by  Murgue et al. (2016)  and  Rizzo et al. (2019). It is clear that the lack of generic databases for some of the necessary variables to use or evaluate the model makes using the model more difficult. However, this constraint is not specific to MHYDAS-Small-Reservoirs and has been encountered by other modelling approaches dealing with the cumulative effect of small reservoirs. The quality of the data used also guarantees the predictive quality of the model and the reliability of the model assessment.
The use of indirect acquisition methods necessarily introduces inaccuracies, either because of the quality of the expertise  (Rizzo et al. , 2019)  or because of the method itself. For example, in our case study, we did not manage to meet all the owners of the reservoirs (absences or refusals) so that the data for some reservoirs correspond to hypotheses based on our observations or on the expertise of neighbours. However, the existence of generic databases does not exclude the need to examine the quality of the data included in them. For example, in the present study, although we had databases providing stream flow, meteorological data and crop yield values, we were only able to access stream flow data at a nearby station located within the same basin but outside the Gélon catchment, while meteorological data and crop yield values, respectively, were at a resolution that was too low to obtain internal spatial variability on an 8 km 2 grid and averaged over the entire Gers department,.
Acknowledgements This work was financed by a PhD scholarship from the  French Occitanie-Pyrénées-Méditerranée Region  and the  French National Research Institute for Agriculture, Food and Environment (INRAE). A part of this work was financed by the  French Biodiversity Agency (Office Français pour la Biodiversité). The authors thank the  Compagnie d'Aménagement des Coteaux de Gascogne (CACG)  and MeteoFrance for access to their databases. We would like to thank  Jean-Christophe Fabre , OpenFLUID team leader, for his help and contribution to the development of the MHYDAS-Small-Reservoirs. Also, special thanks to  Romain Lardy ,  Marie Estienne ,  Manuel Chataigner  and  Ekaterina Ferry-Zadonina  for their help in the modelling work, to  Koladé Akapko ,  Victor Giffone  and LISAH technicians  Sébastien Troiano ,  David Fages  and  Olivier Huttel  for facilitating the in situ data collection and to  Marie Lefrancq  for providing hydrological data.
Software and data availability MHYDAS-Small-Reservoirs runs with the OpenFLUID platform. OpenFLUID is a software environment for modelling and simulation of complex landscape systems. The documentation and versions of Open-FLUID platform can be found on the OpenFLUID site at www. op enfluid-project. org. A dedicated GitHub workspace is also available at https://github. com/OpenFLUID/openfluid. The GitHub workspace dedicated to MHYDAS-Small-Reservoir is available at https://github. com/UMR-LISAH/MHYDAS-Small-Reservoirs.
Note: The software packages provided with the live-cell microscope may differ but operating the acquisition and the experimental design will include the following steps. 12. From the main menu, chose File and Acquire (Resolve3D) to initialize the system. 13. Rotate objective turret until 203 objective is in the active position. Update selection in the Lens drop down menu (Figure  3D ). Note: The DeltaVision system used here has a 1. 593 auxiliary magnification (final magnification 31. 83). 14. Use the Polychroic drop down menu and select Quad (Figure  3B ). The filters will be set in the Experiment setup window, in step 18d. 15. Set the Image size to 512 3 512 (Figure  3C ). 16. Set the Binning drop down menu to 2 3 2 (Figure  3E ). Note: Using the Edge/sCMOS_5. 5, the pixel size ends up at 0.
The coordinates from both microscopes are recorded on a point list as described. The Zeiss microscope will flip the coordinates for display, according to a horizontal axe (see Figure  6 , step 2). The point lists will be used to determine the coefficients of a linear regression for both x and y coordinates of each point, which can then be applied to convert a point list from the DV to the PALM Zeiss setup. With the manual rotation of the FrameSlide and the Zeiss display flip, y Zeiss only depends on x DV , and x Zeiss only depends on y DV  (Figure 6, step3). A second round of this procedure can be repeated using cells instead of marker points in order to increase the accuracy of the conversion. The python code ''DVtoZeiss. py'' to compute the conversion between DV and Zeiss (Data S1) is accessible on https://github.
com/jrxlab/live_cell_phenotyping_capture_proc (https://doi. org/10. 5281/zenodo. 6671339), with more information about the 3 steps in the Readme section. To execute ''DVtoZeiss. py'' to obtain the coefficients of linear regression and perform a conversion of a point list (with a file with DV coordinates for the 20 fields (the function takes into account the path toward the file (path_to_dv_pts_file) and the name of the. pts file (name_dv_pts_file)) + a numpy array with the DV coordinates of the marker points (DV_init) + the numpy array of the corresponding coordinates in the Zeiss (Zeiss_init): To execute ''DvtoZeiss. py'' to simply perform the conversion of a point list, the function will use our marker point coordinates to compute the conversion DeltaVision to Zeiss: To execute ''DvtoZeiss.
Note: The following steps describe the analysis of single cell trajectories using ImageJ and publicly available plugins (https://imagej. nih. gov/ij/plugins/) for most users. However, inhouse plugins and other analyses routines can be used here to track single-cell ratio intensities overtime. The following steps are illustrated in short tutorial (Methods videos S1, S2, and S3). 22. Open the raw image stack sequences in ImageJ software. 23. In the main menu, select Image -Stacks -Tools -Make Substack (Figure  7A , Methods video S1). 24. In the Substack Maker tool, specify the range and the increment corresponding to each channel to have three different substacks, one for CFP, YFP and polarized channels respectively (Figure  7B ). 25. Repeat step 23 for each channel. 26. Select the CFP substack, draw a single ROI in a background region.
Note: The position of the ROI within the cell, does not change significantly the measured trajectory. Note: The next steps can be performed using a Python procedure (''Plot_AllTraj_FrameSlide_ Cell_Field_Label. py'' (Data S1), available on https://github. com/jrxlab/live_cell_phenotyping_ capture_proc (https://doi. org/10. 5281/zenodo. 6671339). This code creates a single label for each cell in the experiment, it ranks the cells depending on the mean time derivative values. This ranking allows to select cells of interest: the 7 Top High Responders and the 7 Bottom Low Responders that would be microdissected for downstream analysis and that can be identified on the images by their unique labels: field#. cell#. It takes as input a directory containing the. csv files where each file corresponds to a field in the frame slide.
Bioinformatics analysis 61. cDNA alignment: after Fastq file production using Illumina bcl2fastq software, the first step is to align the read1 against human build reference using a splice-aware aligner. In the following example, cDNA reads mapping was performed using STAR  (Dobin et al. , 2013)  against human hg19 following Encode RNA-seq recommendations. We obtain a BAM file composed of SAM records describing how/where cDNA reads aligned to the reference genome. Note: Encode RNA-seq recommandations (https://www. encodeproject. org/documents/ cede0cbe-d324-4ce7-ace4-f0c3eddf5972/@@download/attachment/ENCODE%20Best% 20Practices%20for%20RNA_v2. pdf. ). Cell barcodes and UMI processing: TagBamWithReadSequenceExtended pipeline from Drop-seq toolkit  (Macosko et al. , 2015)  was then used to tag the SAM records with the cell barcode of the cell from which the cDNA originated (named ''XC'') and the UMI (named ''XM'') extracted from in-dex1 reads (cell barcode: 1-8 nt, UMI: 9-20 nt).
Note: The BAM file of our example fate-seq are accessible at GEO accession number GSE138832 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE138832). 62. Digital Gene Expression count matrix production: TagReadWithGeneExon pipeline from Dropseq. jar toolkit is then used to tag SAM records of cDNAs falling inside gene locus with a gene name tag (GE in our example). Then DigitalExpression pipeline using edit distance of 1 is used for production of sc-RNAseq count table (counts of UMI).
Single-cell data analysis For the single-cell data analysis, the cell response phenotypes and the scRNAseq count table will be required. 63. The cell response phenotypes are the ones that were obtained previously from the live-cell experiments (high and low responders from step 35). 64. The count table is generated from the bioinformatics analysis above. 65. Standard single cell data analysis workflows can be used, such as workflows based on R packages Seurat, scran or edgeR. As an example, the single-cell data analyses performed for the fate-seq of the HeLa cells described in this protocol treated with TRAIL, are available here: https://github. com/jrxlab/fate-seq_proc, and the expected results are shown in Figure  13.
ACKNOWLEDGMENTS We thank the live-cell microscopy setup at the  IRCAN's Molecular and Cellular Core Imaging (PICMI) Facility  and the CytoMed, the  IRCAN's Flow Cytometry Facility  (supported by funding from  FEDER, Ministere de l'Enseignement Superieur Conseil regional Provence Alpes-Cote d'Azur, Conseil departemental  06 ,  ITMO Cancer Aviesan, Canceropole PACA, INCa, Gis Ibisa et Inserm ), as well as  Maeva Gesson  and  Damien Alcor  for the laser microdissection setup at  Centre Mediterraneen de Medecine Moleculaire  ( C3M/INSERM 1065 ). Single-cell RNA sequencing was performed by  UCAGenomiX , a partner platform of the  National Infrastructure France Genomique  ( ANR-10-INBS-09-03  and  ANR-10-INBS-09-02 ), with support from  Conseil Departemental des Alpes Maritimes  ( 2016-358 294DGADSH-CV ), and  Canceropole PACA.
Authors were supported by an  Agence Nationale de la Recherche  '' Investissements d'Avenir'' program :  LABEX SIGNALIFE ANR-11-LABX-0028-01  (to all) and  IDEX  UCAJedi  ANR-15-IDEX-01  (to J. R. ). This work was funded by a  Marie Curie International Incoming Fellowship  within the 7 th European Community  Framework Programme (proposal SysBioDRez , N 626190, call reference:  FP7-PEOPLE-2013-IIF ), an  INCa Plan Cancer Biologie des Systemes, ITMO Cancer (proposal ImoDRez , N  18CB001-00 ), a  young investigator award  ( Emergence Jeunes Chercheurs ), a Single-cell Starting package as well as a Prematuration fund from  Canceropole Provence-Alpes-Cote d'Azur , the  French National Cancer Institute (INCa)  and the  Provence-Alpes-Cote d'Azur Region , and a  Programme Prematuration  from  CNRS Innovation  to J.
These regions were scattered across the C3 scaffolds (Supplemental Fig. S4 ). Using BLASTX against the NCBI Protein database  (Altschul et al. 1990) , we found that the majority (>90%) of these sequences had strong hits to Acanthamoeba proteins. The average gene length in the two strains (∼2. 3 kb), combined with the number of additional genes in C3 relative to  Neff-v2 (1307) , supports the idea that most of the extra sequence in C3 relative to Neff is owing to a larger number of genes. Although the retrieved C3-specific sequence does exceed the difference in genome size between C3 and Neff-v2, the Neff-v2 genome has strain-specific genes of its own that likely account for this discrepancy. Despite the differences in gene content and genome size, the Neff and C3 genomes are highly syntenic (Fig.
1D ), and the additional DNA in C3 does not appear to result in large structural rearrangements. We further tested this by aligning the Hi-C reads from C3 to the Neff genome and generating a contact map binned at 20 kb. Only three translocations were identified, in agreement with the Circos plot shown in Figure  1D   (Krzywinski et al. 2009). We used both Broccoli  (Derelle et al. 2020)  and OrthoFinder (Emms and Kelly 2019) for inference of orthologous groups (see Methods). A summary of the inferred orthogroups shared by, and specific to, the Neff and C3 strains of A. castellanii is presented in Figure  2 , with orthogroup numbers from both orthologous clustering tools included. This figure only compares Neff against C3, irrespective of orthogroup presence or absence in outgroup taxa. In this analysis, each strain-specific gene that was not assigned to an orthogroup by either program was still considered to be a single strain-specific orthogroup in order to account for the presence of genes without any orthologs across the five species.
All enriched Gene Ontology (GO) terms and the corresponding P-values can be found in Supplemental Table  S2. Note that a multiple testing correction has not been applied to these P-values, consistent with the method of topGO (because of the hierarchical relationship of GO terms, the tests are not strictly independent, and the multiple testing theory does not directly apply). For some of the enriched functional categories, the strain-specific genes contributing to the enrichment showed a relatively cohesive signal in terms of best hits when searched against the nr database with BLASTP  (Altschul et al. 1990 ). The Neff genes annotated as "virion parts" were the same ones responsible for the enrichment in "structural molecule activity. " These genes had best BLAST hits to the major capsid protein from various nucleocytoplasmic large DNA viruses (NCLDVs).
3A ). ECC finder  (Zhang et al. 2021a ), a computational tool that detects extrachromosomal circular DNA elements, was run on the Oxford Nanopore long reads to verify that these rDNA sequences do not correspond to extrachromosomal circular rDNA contacting the genome at various positions. No evidence of circular DNA elements was found, suggesting that rDNA sequences in A. castellanii are interspersed within chromosomes. However, given the repetitive nature of these sequences, it is formally possible that some of the rDNA insertion sites are incorrect. In addition, the possible amplification of extrachromosomal palindromic linear rDNA, similar to that found in Dictyostelium  (Sucgang et al. 2003) , may also confound their proper analysis. In addition to large, interchromosomal subtelomeric contacts, we also explored the existence of intrachromosomal chromatin 3D structures in the contact maps using Chromosight, a program that detects patterns reflecting chromatin structures on Hi-C contact maps (Matthey-Doret et al.
castellanii is not a very good host for infection by Legionella partly owing to an accumulation of amino acid substitutions in its MBP, substitutions that may prevent Legionella from binding to this protein during cell entry. It is worth noting that the Neff strain has been in axenic culture since 1957, so it may be that the relaxed selective pressure on this protein, combined with repeated population bottlenecking during culture maintenance, has allowed for mutations in the Neff strain MBP gene to accumulate. At the present time, without available genome data for strains more closely related to the Neff strain, it cannot be determined whether these mutations arose in nature or in culture. However, given that the divergence of the A. polyphaga ortholog to the MEEI 0184 strain is much less than that of the Neff strain, despite all four strains having similar lifestyles in nature, evolution of the Neff strain since being deposited in the culture collection seems likely.
pneumophila strain Paris constitutively expressing GFP. A. castellanii C3 was infected at an MOI = 0. 1, and strain Neff was infected at MOI = 0. 1, MOI = 1, or MOI = 10 in T25 flasks. At t = 0 h post infection (hpi) and at t = 1 hpi, 300 µL was removed from the infection; amoeba cells were lysed by vortexing the sample for 60 sec and centrifuging the sample at 14,000 rpm for 3 min; and 100 µL was spread on BCYE plates. Colony forming units (CFU) were counted to determine bacterial numbers used for infection (input, t0) and numbers of internalized bacteria (internalization, t1/t0). In parallel, at t = 0, 1, 24, 48, and 72 hpi, 300 µL of infected amoebae was removed from the infection culture and lysed as detailed before. One hundred microliters was plated in 96-well plates in duplicates, and bacterial numbers were analyzed by flow cytometry in a MACSQuant VYP flow cytometer with MACSQuantify software (Miltenyi Biotec) using a 488-nm laser and a 500-550 bandpass filter (B1 channel).
Short-read sequencing Illumina libraries associated with the data submitted to the NCBI Sequence Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra) under accession numbers SRX12218478 and SRX12218479 were prepared from A. castellanii strains C3 and Neff genomic DNA, respectively, and sequenced by Novogene at 2 × 150 bp on an Illumina NovaSeq 6000 machine. To generate the data associated with SRA accession number SRX4625411, a PCR-free library was prepared and sequenced by Génome Québec from purified A. castellanii strain Neff genomic DNA. The library was barcoded and run with other samples on an Illumina HiSeq X Ten instrument, producing 150-bp pairedend reads.
Oxford Nanopore sequencing To generate the data associated with SRA accession number SRX12218489 and SRX12218490, DNA was extracted from A. castellanii strains Neff and C3 using the Qiagen blood and cell culture DNA kit following the specific recommendations detailed by Oxford Nanopore Technologies in the info sheet entitled "Highmolecular-weight gDNA extraction from cell lines (2018)" in order Oxford Nanopore libraries were prepared with the ligation sequencing kit LSKQ109, flow cell model MIN106D R9. Base-calling was performed using Guppy v2. 3. 1-1 (https://nanoporetech. com/ community). For other libraries, genomic DNA samples were obtained from A. castellanii strain Neff using an SDS-based lysis method, followed by digestion with RNase A, then Proteinase K, and then a phenolchloroform-based extraction.
Genome assembly Oxford Nanopore reads were filtered using filtlong v0. 2. 0 (rrwick/ Filtlong; https://github. com/rrwick/Filtlong [accessed July 12, 2022]) with default parameters to keep the best 80% reads according to length and quality. Illumina shotgun libraries were used as reference for the filtering. A de novo assembly was generated from the raw (filtered) Oxford Nanopore long reads using flye v2. 3. 6 (  (Kolmogorov et al. 2019 )) with three iterations of polishing. The resulting assembly was polished using both Oxford Nanopore and Illumina reads with HyPo v1. 0. 1  (Kundu et al. 2019). Contigs from the polished assembly bearing >60% of their sequence or 51% identity to the mitochondrial sequence from the Neff-v1 assembly were separated from the rest of the assembly to prevent inclusion of mitochondrial contigs into the nuclear genome during scaffolding.
Genome annotation The structural genome annotation pipeline used here was implemented similarly as described previously  (Gray et al. 2020). Briefly, RNA-seq reads were mapped to the genome assembly using STAR v2. 7. 3a  (Dobin et al. 2013) , followed by both de novo and genome-guided transcriptome assembly by Trinity v2. 12. 0  (Grabherr et al. 2011). Both runs of Trinity were performed with Jaccard clipping to mitigate artificial transcript fusions. The resulting transcriptome assemblies were combined and aligned to the genome assembly using PASA v2. 4. 1  (Haas et al. 2003). Protein sequences were aligned to the genome using Spaln v2. 4. 2  (Gotoh 2008)  to recover the most information from sequence similarity. The ab initio predictors used were AUGUSTUS v3. 3. 2 (Stanke et al. 2006) , Snap  (Korf 2004 ), Genemark v4.
33 (Lomsadze et al. 2014) , and CodingQuarry v2. 0  (Testa et al. 2015). Finally, the PASA assembly, Spaln alignments, and the AUGUSTUS, Snap, and CodingQuarry gene models were combined into a single consensus with EVidenceModeler v1. 1. 1  (Haas et al. 2008). Functional annotations were added using funannotate v1. 5. 3 (https://doi. org/10. 5281/zenodo. 1471785 [accessed July 12, 2022] ). Repeated sequences were masked using RepeatMasker  (Smit et al. 2013 (Smit et al. -2015)). Predicted proteins were fed to Interproscan v5. 22  (Quevillon et al. 2005) , Phobius v1. 7. 1  (Käll et al. 2007) , and Eggnog-mapper v2. 0. 0  (Cantalapiedra et al. 2021)  to generate functional annotations. Ribosomal RNA genes were annotated separately using RNAmmer v1. 2  (Lagesen et al. 2007 ) with HMMER 2. 3.
Analysis of sequence divergence To compute the proportion of substituted positions in aligned segments between the C3 and Neff strains, the two genomes were aligned using minimap2 with the map-ont preset and -c flag. The gap-excluded sequence divergence (mismatches/[matches + mismatches]) was then computed in each primary alignment, and the average of divergences (weighted by segment lengths) was computed. This is implemented in the script "04_compute_ seq_divergence. py" available in the genome analyses repository listed at Zenodo (see "Data access").