dataesr/scientific-paragraphs-categorization huggingface dataset:
The PAM site is in green. Positions of selected bases refer to the nCas9 nick site (arrow). site and excluding the detection of deletions or insertions larger than 300 bp as well as chromosome rearrangements. Large deletions of dozens of kb  (Ordon et al. , 2017)  and recombination between chromosomes  (Schmidt et al. , 2020)  have been reported in Arabidopsis, although they remain considerably less frequent than small indels. For user-friendly analysis of the NGS data, a 7-step bioinformatics pipeline combining existing programs and custom-made Python scripts was developed. A first key point was the choice of the Needleman & Wunsch algorithm with a custom scoring matrix for pairwise alignments with the reference sequence to guarantee a systematic alignment over the entire length of the reference sequence and satisfactorily handle the often important size differences between the mutated and reference sequence.
Acknowledgments We thank  Justin Berger ,  Alexis Lacroix  and  Patrice Bolland  for maize culture, Herve ́Leyral and  Isabelle Desbouchages  for media preparation,  Claire Lionnet  for assistance with epifluorescent microscopy,  Ghislaine Gendrot  and  Kathy Gallay  for maize transformation, and  Antoine Heurtel ,  Marie Martelat  and  Hadrien Guichard  for initial developments of the bioinformatics pipeline. Benjamin Gillet  and  Sandrine Hughes  ( ENS de Lyon ) of the IGFL sequencing platform (PSI) are acknowledged for expert advice, library preparation and Illumina runs, and  Stefan Scholten  ( University Göttingen ) for tricks in maize protoplast isolation. Credits are attributed to  Bakunetsu Kaito ,  Yohann Berger ,  Julie Ko , Smalllike,  N. Style  and IYIKON for their vectorial images under CC BY 3.
Parameterization of the influent generator for urine separation This study was conducted using the phenomenological influent generator  (Gernaey et al. 2011 ) that was specifically modified to include urine source separation (Bisinella de Faria et al. 2020), and modified to consider the production from the work place. The tool is programmed on Matlab and is a flexible simulation tool that can be easily modified. The code is available on request from the author. In this study, the results were selected to represent typical weeks during dry weather periods. Four main streams were defined in the influent generator (different blocks): 1) the Total Wastewater stream (TWW) that includes wastewater from households, industries and other contributors without any urine separation; 2) the Total Urine stream (TU) that simulates profiles of all produced urine; 3) the Urine Stored and Collected stream (USC) that corresponds to the user-specific urine source separation and collection; and 4) the Wastewater stream (WW) that represents the influent entering the treatment plant and represents the combined contribution from households, industries, rainfall, and infiltration (Bisinella de Faria et al.
The profile of urine generation at work places has been assumed to be constant during working hours, due to lack of relevant data on urination pattern during the day. On average, the total volume of urine produced daily was equal to 1. 36 liters  (Rauch et al. 2003; Rossi et al. 2009 ), of which one-third was integrated in the calculation for profile production at work  (Jönsson et al. 2005). The resulting wastewater influent profile was obtained by subtracting the separated urine at work from the regular profile produced on a daily basis before the modelling of the sewer, for scenarios 2 and 3.
Calculation of aeration needs and methanol addition The modification of nitrogen and COD influent loads were used to estimate the reduction in aeration needs (nitrification) and methanol consumption (denitrification). Reference data were obtained from the plant operators of MAV: extensive measurements of influent ammonium, wastewater flow rates, methanol dosing and aeration flow rate were recorded with high frequency. The wastewater treatment plant is equipped with two stages of biofilters to achieve nitrification and denitrification of the influent wastewater. The first stage of biofilters (DN/N with internal recirculation of 100% of the influent flowrate) achieves 90% nitrification. The second stage of biofilters (DN with external carbon source) achieves the post-denitrification to reach 4.
Calculation of greenhouses gases emissions The overall impact of each urine diversion scenario on global warming potential was calculated. For this purpose, the effects on the treatment plant operation (reduction of energy consumption, chemicals consumption, direct emission of greenhouse gases) was translated into global warming potential thanks to the Ecoinvent database  (Wernet et al. 2016 ) (see Table  S4  in the Supplementary Information for detailed emissions factors). The following aspects were considered: -The avoided energy consumption for aeration of nitrifying biofilters was calculated with reference to the current air consumption in the plant (333 Nm 3 /kg NH 4 -N removed) and the oxygen demand calculated for the reference. French electricity mix was used (0. 109 kgCO 2 -eq/kWh) from Ecoinvent database (see Supplementary Information section 3 for detail of the calculation).
1% in the afternoon (Figure  4 ). Finally, the average influent nitrogen load reached respectively 2. 58+0. 83 10 3 kgN/d for scenario 2 and 2. 52+0. 80 10 3 kgN/d for scenario 3 (Table  2 ), which corresponds to 2. 4 and 4. 8% reduction of the influent nitrogen load compared to the reference scenario (current situation without any USS). These dynamics were calculated for each scenario (1, 2 and 3) using the simulated influent and the operational data collected from MAV plant operators. For scenario 1, the reduction in air flow rate for nitrification was 31. 5% and peaked at 32. 3%. The reduction rate was less important for scenario 2, which reached 2. 5% and peaked at 4. 5%, but it was more pronounced for scenario 3, with a maximal reduction in aeration reaching 89. 0% (Table  3 ). The average reduction in methanol consumption at the post-denitrification obtained from scenario 1 reached on average 36.
ACKNOWLEDGEMENTS This study was part of the  MOCOPEE research program  (MOdeling, Control and Optimization of wastewater treatment ProcEssEs, www. mocopee. com)and was supported by the  SIAAP (Greater Paris Sanitation Authority). The authors are very grateful to  Dr Kai M. Udert  from  Eawag  and  ETH  for his careful review and guidance during this project.
, 2019)  and  (Kurd et al. , 2020). We applied trajectory inference on these datasets to identify trajectories leading to the generation of MP cells. Using cell-cycle classification and RNA velocity algorithms, we show that the differentiation is driven by cell-cycle and immune function genes. To identify the molecular regulatory mechanisms supporting the process, we then performed a gene regulatory network (GRN) inference analysis, which allowed us to identify a cluster enriched in cells harboring transcription factors associated with MP cells. Using an MP gene signature, we confirmed that this cluster was enriched in MP cells, although cells expressing that signature were also found at multiple points along the trajectory. Finally, we used another pathogen infection and BrdU labeling to generalize and validate these results in vivo.
Trajectory inference of the CD8 T cell response to an acute infection In order to gain insight into the differentiation dynamics of CD8 T cells in response to an acute infection (LCMV-Arm), we performed trajectory inference on a scRNAseq dataset generated by  Yao et al. (2019)  using two recently published methods, Slingshot  (Street et al. , 2018)  and TinGa  (Todorov et al. , 2020). This dataset consists of measurements on 20,295 splenic CD8 T cells generated following LCMV-Arm acute infection and isolated at two different time points (4. 5 and 7 days postinfection [dpi]), in two separate replicates. We identified the 2,000 most highly variable genes in the dataset using variance modeling statistics from the Scran R package  (Lun et al. , 2016) , on which we applied both trajectory inference methods.
Slingshot was shown to be very efficient in a study that compared more than 40 methods on a large number of datasets  (Saelens et al. , 2019). TinGa is a new method for trajectory inference that showed comparable results on simple trajectories and better results on complex trajectories than Slingshot  (Todorov et al. , 2020). These two methods both share a first step in which the dimensions of the data are reduced, either by principal component analysis for Slingshot or by multidimensional scaling (MDS) for TinGa. In the two resulting representations of the data, the cells form a continuum from cells taken 4. 5 dpi to cells taken 7 dpi (Figures  1A  and S1A ). Slingshot first applies clustering to the data and then identifies transitions between these clusters. It identified a linear trajectory starting among cells from day 4.
The inferred trajectories retrace an early-late-memory differentiation pathway To further characterize the CD8 differentiation trajectory supported by the single cell transcriptomics data, we used the trajectory obtained with TinGa, as it identified more transition points along the route and hence might give a more refined definition of differentiation steps. As both the Slingshot and the TinGa trajectories were clearly driven by cell-cycle-associated genes (Figures  S1C  and S2A ), we used a classifier from the Seurat R package  (Tirosh et al. , 2016)  to allocate cells to the G1, S, or G2/M cell-cycle phases (Figure  1C ). We identified clear cycling trends along the trajectory. Cells in clusters 2, 5, and 4 were mainly classified in the S phase (Figures  1C  and 1D ), whereas clusters 7 and 3 were de facto classified in the G2/M phase (Figures  1C  and 1D ).
Gene regulatory interaction analysis reveals a memory-precursors-associated module enriched in cluster 1 To further characterize the transitional stages defined along the TinGa trajectory, we identified regulatory interactions between transcription regulators and their target genes in the dataset using the BRED tool  (Cannoodt, 2019). We identified six main GRN-modules, which we defined as groups of target genes gathered around a regulator (Figure  3A ). As expected, based on previous results on the cell cycle, three of these modules (Pcna, Hmgb2, Cenpf) were strongly enriched in genes involved in cell-cycle regulation. The Ybx1 GRN-module contained two groups of genes: one coding for proteins involved in RNA and protein synthesis metabolism that were upregulated in the cells from the cluster 2-5-7 branch and the other for immune functions that were enriched in clusters 6 and 8 (Figure  S4 ).
, 2017; Shin et al. , 2017; Yang et al. , 2011). To confirm the continuous generation of MP cells, we analyzed a second transcriptomic dataset generated by Kurd et al. that contained cells sampled at multiple time points during the primary response (day 4, 5, 6, 7, and 10 post-LCMV infection). Highly variable genes expressed by the 9,614 cells were selected and TinGa was applied (Figure  S8A ). The trajectory obtained is similar to the Yao et al. 's data  (Yao et al. , 2019) , with the first part of the trajectory being enriched in cycling cells (cluster 1, 3, 8, 6, and 4) that were sampled on day 4, 5, 6, and 7 pi. The second part contained quiescent cells sampled on day 6, 7, and 10 pi (Figures  S8A-S8C ). Similarly, 574 MP cells were found by GSEA all along the TinGa trajectory (Figures  S8D  and S8E ).
Finally, we compared protein expression of memory cells generated on day 4, 5, or day 7 pi. We thus performed a BrdU chase experiment (Figure  6A ) and measured the expression of proteins encoded by genes that were differentially expressed in the single-cell transcriptomic dataset (Figures  4D, S10A , and S10B; Table  S4 ). We found that only CCL5, which was the most differentially expressed gene in late d7 MP cluster 8 (Figure  4D ), was also expressed at a higher protein level by F5 memory cells generated at 7 dpi (Figure  6B ). Two other proteins, CD127 (IL7R) and Ly6C, were not differentially expressed in the memory phase (Figures  S10A  and S10B ). The expression of CCL5 was also measured on endogenous antigen-induced BrdU positive memory cells, identified based on their CD49d expression  (Grau et al.
DISCUSSION In this study, we have used trajectory inference tools to analyze the generation of memory precursor CD8 T cells during a primary response against an acute viral infection. A single-cell transcriptomic dataset  (Yao et al. , 2019)  generated at two time points during the primary response, was analyzed using two recently developed trajectory inference algorithms  (Slingshot [Street et al. , 2018]  and TinGa  [Todorov et al. , 2020] ). These tools allow modeling gradual transitions between cell states, as they tend to preserve the local similarities between cells, thus predicting the likely differentiation path followed by cells activated in vivo by the virus  (Saelens et al. , 2019). Trajectory inference tools have become essential, as they allow to predict the fate of cells that have to be lysed to analyze their cellular content and/or transcriptome.
, 2013; Gerlach et al. , 2013). The trajectory identified by TinGa was more refined, as it identified 8 transitional stages, one of which (cluster 1) was strongly enriched in MP cells identified using a gene signature derived from  Yao et al. (2019). We also applied scVelo  (Bergen et al. , 2020) , a method that uses the splicing state of transcripts to calculate RNA velocities. The projection of RNA velocities on the TinGa-generated map evidenced two cellular behaviors with early cycling cells that remain on a circular trajectory and later cells that follow a linear path. These two behaviors were associated with cell-cycle and immune function genes, respectively. Importantly, the linear trajectory driven by the immune effector genes started in early (d4. 5) cells underpinning cycling and quiescent cells, thus reflecting the progressive expression of effector functions by activated CD8 T cells.
These results are in agreement with the two effector compartments previously described, namely the early cycling effector cells and late quiescent effector cells expressing genes encoding immune effector functions, through which most MP cells have to go to generate the full pool of memory CD8 T cells  (Crauste et al. , 2017). We herein found that MP are present at all pseudo-times, with an enrichment in clusters 1 and 8. The majority of MP cells in clusters 1 and 8 were in the G1 phase of the cell cycle, suggesting that they were on their way to become quiescent memory cells. We confirmed the continuous generation of MP cells on another dataset from  Kurd et al. (2020). Importantly, we estimate that the number of MP cells generated on day 7 pi is around 100-fold higher than the number generated on day 4.
In vivo memory precursor cell number calculation The number of MP present in the spleen of animals at each time point was estimated for each cluster by multiplying the number of cells recovered at this time point (given by the number of cells collected in  Yao et al. (2019) /by the percentage of cells in the given cluster (given by the TinGa analysis) and the percentage of MP cells among these (given by the GSEA analysis). Then the number of MP was summed for all 8 clusters to yield the number of MP present in the spleen at a given time point. The log-normalised expression values of the 2,000 highly variable genes were used for downstream analysis. To validate our results, a second dataset from  Kurd et al. (2020)  were used (GEO, accession no. GSE131847). Pre-processed count matrices of cells sampled at day 4, 5, 6, 7 and 10 (replicate 1 only) were pooled (9,614 total cells) and genes detected in less than 1% of the total cells were removed.
Trajectory inference Two recently published trajectory inference tools, Slingshot and TinGa, were used to identify a trajectory in the data. The normalised data was first wrapped into a dataset object with the dynwrap R package. The slingshot implementation in dynwrap, as found on the github/dynverse/dynwrap github PAGE, was applied to the data using the default parameters. The TinGa implementation as found on the github/Helena-todd/TInGa repository was applied to the data using the default parameters. The dynplot R package was then used for an easy visualisation of the resulting trajectories.
Differential expression analysis The transitional populations that were identified along the TinGa trajectory were used as clusters defining similar cells. Differential expression analysis was performed between these clusters using the Seurat R package. Wilcoxon rank sum tests were applied and genes were selected as differentially expressed if the difference in the fraction of detection of the gene between the two compared groups of cells was higher than 0. 25, and if the log fold-change difference between the two groups was higher than 0. 3. The differentially expressed genes were then visualised using the triwise R package  (Van de Laar et al. , 2016)  and in a volcano plot that was generated manually in R with the ggplot2 R package.
Gene set enrichment analysis Gene rankings were computed in cells using the AUCell R package. This allowed to identify cells that showed specific gene signatures. The memory precursor signature published by  Yao et al. (Yao et al. , 2019) , consisting of a re-analysis of the data from Joshi et al. (Joshi et al. , 2007 ) (GSE8678), was used. Of the 122 genes described as associated with a memory-precursor signature, only 42 genes were present in the 2,000 HVGs that we selected. We thus decided to use all genes available instead of restricting ourselves to the 2,000 HVGs for this analysis (115 genes found). 833 cells out of the 20,196 studied acute responding CD8 T cells were assigned to a memory precursor signature. The Tpcm signature from Pais Ferreira et al. (Pais Ferreira et al. , 2020)  was also tested.
The signature consisted of 602 genes (576 genes expressed in  Yao et al. dataset)  differentially expressed in the effector-phase Tcf7 GFPhi CD8 T cells versus Tcf7 GFPÀ P14 cells at day 8 post LCMV Armstrong infection (criteria: adjusted p value <0,05 and log2FC > 1). Inferring the number of memory precursors in the spleen The number of memory precursors in the spleen was calculated based on the percentage of memory precursors identified by gene set enrichment among total day 4. 5 or day 7 cells and the average number of CD8 T cells found in the spleen of mice on those same days (Number of MP on day x = % of MP among single cell from day X * average total number of CD8 T cells in spleen on day X).
Gene regulatory network inference The BRED R package was used to identify regulatory interactions between a list of transcription factors (that was identified among the 2,000 HVGs using the database in the org. Mm. eg. db R package, and manually curated), and the 2,000 target genes. The scaled importances corresponding to these interactions were filtered, and the top 100 interactions corresponding to the 8 populations identified in the TinGa trajectory were selected, resulting in a gene regulatory network containing 800 interactions. A layout of these interactions was then generated using Cytoscape. In the resulting gene regulatory network, we define modules as groups of target genes linked to one central transcription factor.
ACKNOWLEDGMENTS We thank  C. Yao  ( NIAMS), J. O'shea (NIAMS), P. Schwartzberg (NIAID) , and  T. Wu  ( Department of Immunology and Microbiology, University of Colorado School of Medicine ) for sharing their data and advice. We acknowledge the contribution of  SFR BioSciences  ( UAR3444/CNRS ,  US8/Inserm ,  E ´cole Normale Superieure de Lyon, Universitede Lyon)  and the CELPHEDIA infrastructure (http://www. celphedia. eu/),especially the center  AniRA in Lyon (AniRA-Cytometrie and AniRA-PBES  facilities). This work was supported by  Inserm ,  CNRS ,  Universitede Lyon ,  ENS de -Lyon, region Auvergne-Rhone-Alpes  (Ingerence pack ambition recherche), and  ANR  ( MEMOIRE ANR-18-CE45-0001 ). Margaux Prieux  has a region Auvergne-Rhone-Alpes PhD fellowship.
Single-cell RNA-seq data preprocessing Existing single cell data from  Yao et al. (2019)  were used (GEO, accession no. GSE119943). A feature-barcode matrix by replicate was generated using the Cell Ranger v. 3. 1 software (10X genomics) and only effector CD8 T cells in acute infection sampled at day 4. 5 and day 7 post infection were kept for the analysis. The two replicates were pooled since no batch effect was observed. The cell filtering was made with the scater package  (McCarthy et al. , 2017). Briefly, cells with a log-library size and a log-transformed number of expressed genes that were more than 3 median absolute deviations below the median value were excluded. The cells with less than 5% of mitochondrial counts were kept. These criteria were applied separately on the cells from day 4.
Modeling of diffusion/enzymatic reaction in porous electrodes operating in direct electron transfer mode is developed. The solution at the pore-scale is extremely cumbersome due to the complex geometry of the porous material. The upscaled model is much easier to solve, while keeping the essential of the physico-chemical behavior. The method to carry out the solution can be described as follows • The effective diffusion coefficient involved in the macroscopic equations is accurately computed by solving a closure problem in a representative elementary volume. • Electrochemical parameters are identified by a direct resolution of the macroscopic model solved with a COMSOL Multiphysics code coupled to a curve fit procedure carried out on voltammetry experimental results using a Matlab code. Electrodes with different thicknesses may be considered in the fitting procedure to improve accuracy.
An alternative use of the COMSOL Multiphysics code is to predict the electrode behavior and further optimize its design, if all the electrochemical parameters are identified. • To validate the upscaled model, the pore scale model may be solved with direct numerical simulations carried out in a 3D microstructure using another COMSOL Multiphysics code to compare with the solution of the upscaled model in the 1D-reduced geometry.
Voltammetry experiments, carried with several gold porous-electrodes with different thicknesses, functionalized with a bilirubin oxidase reported in  [1]  , serve as illustrative data for the modeling procedure. The macroscopic equations solved with COMSOL Multiphysics are directly coupled with a parameter identification procedure, carried out in Matlab, in order to determine the unknown electrochemical parameters involved in the experiments. To summarize, the method reported here involves three main steps, i. e. , (i) solution of the closure problem on a representative unit cell of the electrode material (solved with a COMSOL Multiphysics code). This step provides a more accurate solution for estimating the effective coefficients, in comparison to predictive correlations for the effective diffusivity (see for instance  [ 5 , 6 ] ); (ii) solution of the macroscale problem in the reduced 1D corresponding domain (COMSOL Multiphysics code); (iii) use of this solution together with a fitting procedure (Matlab code) to identify missing electrochemical parameters, if required, and/or predict the behavior of the current produced by the electrode.