dataesr/scientific-paragraphs-categorization huggingface dataset:
Antibodies against RNAse H1 (Invitrogen PA5-30974) and GAPDH (Santa Cruz Biotechnology) were used. In the rescue experiment INO80 was expressed from pCMV-3XFLAG-hINO80 -a gift from Joan Conaway (Addgene plasmid # 44149)  62. As empty plasmid pcDNA3. 1 was used. or Dragonfly 500 microscope with iXon Camera (Andor). Fibre length measurements were carried out using AxioVision software (Carl Zeiss) or Image J. Speed conversion was carried out using conversion factor of 2. 59 kb per µm. The formula used was speed = (length × 2. 59)/time according to ref. 64. EdU and EU staining. For EdU staining cells were labelled with 25 µM of the compound as indicated. The click reaction was carried out after immunostaining in a reaction mixture containing 100 mM Tris-HCl, pH 7. 6, 4 mM CuSO 4 , 10 µM Alexa Fluor 488 azide and 100 mM sodium ascorbate for 30 min at room temperature.
Slides were then washed 3 times using 5% FBS in PBS to remove unbound reporter. To label newly synthesized RNA, cells were labelled with 1 mM EU for 3 h, fixed, permeabilized and stained in the click reaction mixture as above. Nuclei were counterstained with 0. 5 µg/ml 4′,6-diamidino-2-phenylindole (DAPI) in PBS. Flow cytometry. To analyse cell cycle profiles, cells were harvested by trypsinization and fixed in 70% ethanol. Before analysis cells were re-suspended in PBS, treated with RNAse A (20 μg/ml) and stained with propidium iodide (20 μg/ml). Data acquired using FACScalibur apparatus (Becton Dickinson). Analysis was done using FlowJo software, fitting Dean-Jett-Fox model  65  to data to determine the percentage of cells in each phase. LacI-LacO system. Cloning was performed using the Gateway Cloning system (Thermo Fisher).
Secondary antibodies for confocal microscopy were from Jackson Immunoresearch Alexa Fluor® 647 AffiniPure Donkey Anti-mouse IgG (1:500-1500); Alexa Fluor® 594 AffiniPure Donkey Anti-Rabbit IgG (for INO80 confocal and STED) or for S9. 6 STED, ATTO 647N (STED/GSD) Goat anti-mouse IgG (Active Motif Catalog No. 15038) at 1:100. DNA was labelled using Hoescht. Coverslips were mounted using Prolong Diamond (Thermo Fisher) for confocal imaging or Mowiol 4. 88 (Calbiochem) mounting media was prepared and used for STED. Mowiol mounted slides were allowed to cure for 48 h prior to imaging. Using this protocol, the non-specific S9. 6 signal was depleted from the nucleolus. Images were deconvolved prior to quantification. Deconvolution was performed with Huygens 18. 04 from SVI (www. svi. nl). Quantifications of both total nuclear R-loops and EdU associated R-loops were carried out using 3D volumes with IMARIS image analysis software.
Acknowledgements We thank  Neil Perkins  and members of the  Papamichos-Chronakis lab  for constructive discussions. We thank  Claus M. Azzalin  for the RNAse H1-His-Myc overexpression plasmid. We thank  Evi Soutoglou  for the LacOU2OS cell line. Work in MPC lab is supported by  Liverpool University, Newcastle University  and a  Wellcome Institutional Support Fund Award. Work done in A. G. 's lab was supported by  Bulgarian National Science Fund  grant #  DN11/17. Work in the PP lab is supported by grants from the  Agence Nationale pour la Recherche (ANR) , the  Ligue Contre le Cancer  (équipe labellisée), SIRIC  Montpellier Cancer  ( INCa Inserm   DGOS 12553 ) and the MSDAvenir fund. ULM was funded by the  Royal Society   RG170342. IHL was funded from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
Data availability The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary information files. All datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Espresso Database:https://www. ebi. ac. uk/teichmann-srv/espresso INO80 and DRIP ChIPseq data URLs: https://www. ebi. ac. uk/arrayexpress/ experiments/E-GEOD-49137/ https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE67581 Chromatin State data URL: http://epistemnet. bioinfo. cnio. es/download/ Source data are provided with this paper.
This seems to be an appropriate time to put together the available data and attempt to answer two major questions: 1) Can we still consider EpCAM and EpCAM2/Trop2 as genuine CAMs, and, if not, how can one explain their well-documented effect on cell adhesion? 2) Can one come up with a coherent framework that would account for the disparate, often apparently contradictory, effects of EpCAM on cell and tissue adhesive and migratory properties? To approach these goals, it was necessary to take a candid look at the published data, to critically reevaluate their interpretation, also based on recent progress made in our understanding of the cellular, molecular and biophysical mechanisms controlling adhesion and migration. Another necessary step was to compile, compare and combine information on EpCAM and EpCAM2/Trop2, which, with a few exceptions, have been studied in isolation, despite their obvious high similarity and the likely high degree of redundancy.
A-21042 diluted 1/1000 in PBS) to reveal focal adhesion points (in green). After rinsing with PBS-0. 02% Triton, the cells are incubated in a dilute solution of phalloidin tetramethylrhodamine bisothiocynate (Sigma, ref. P1951 diluted at 1/2000 in PBS) in order to display actin fibers (in red). Coverslips were mounted on a slide with the cell side down in Prolong (LifeTechnology, ref. P36974). Staining structures are observed, and images are acquired on an Axio Imager confocal microscope (Zeiss) at 63× magnification. To analyze images of the cells selected by random fields, the mean level of fluorescence of F-actin is determined in each cell by automatic quantification by ImageJ software relatively to the fluorescent background level of each image. The effective mean fluorescence of cells is obtained after subtraction of background fluorescence.
In order to distinguish the cortical and the dense actin filaments, two different fluorescence thresholds on ImageJ software were used, i. e. , 152 for the cortical actin and 534 for the dense actin, to permit the quantification of actin fluorescence at two detected levels. In the same way, different thresholds were used to distinguish different size areas of focal adhesion points (< or > to 1 μm 2 ). Finally, on the same coverslips, we used the LSM5 Pascal software to make stacks of images (every 0. 5 μm) in the vertical direction 'z' and to carry out a 3D reconstruction of the shape of the cell in order to measure the cell height in each condition.
CONCLUSION The complexity of the data provided by transcriptomic and system biology studies are unable to build a clear holistic view and a simple mechanistic interpretation of the event sequence involved in responses to nitrate availability  (Vidal et al. , 2020). Analysis of non-linear dynamics of 15 N fluxes offers an alternative and integrated approach to understand the major underlying processes. In simple terms, this corresponds to the difference between a road map and the car fluxes caused by traffic. Here, the analysis revealed the presence of a locally stable stationary state that precedes a dramatic bifurcation in 15 N uptake and translocation rates. Moreover, the results suggest that K + and water translocation associated with IAA biosynthesis in shoots probably plays a major role in nitrate-induced apical-basal break in symmetry between the root and shoot cell expansion.
ACKNOWLEDGMENTS The authors warmly thank  Prof. Michel Thellier  for early comment and constructive discussion about this work and  Prof. D. G. McNeel  of the  University of Wisconsin-Madison  for the critical reading of this manuscript. They are also grateful to  J. Pichon ,  M-P. Bataille ,  F. Le Ny  and  A. Leblanc  for their technical assistance during seedling harvest and 15 N analyses.
This maximal pixel resolution corresponded to an oversampling factor of two, compared with the diffraction-limited spatial resolution of 3. 1 μm. At this pixel resolution, the image size was 4,096 pixels. Therefore, for each type of wood, the data set was composed of 36,864 spectra (3 different trees x 3 images x 4,096 pixels). The spectra were then corrected using different functions of the SpectrumImage software (v. 1. 6. 4): background correction during acquisition, noise reduction and atmospheric correction, using default parameters. No ATR correction was performed.
Multivariate Image Analysis We analyzed ten classes of cell wall corresponding to the existing associations between cell wall layers (G-or S-layers), cell types (fiber, ray or vessel) and wood types (TW, OW or NW): TW fiber G-layer, TW fiber S-layer, OW fiber S-layer, NW fiber Slayer, TW ray S-layer, OW ray S-layer, NW ray S-layer, TW vessel S-layer, OW vessel S-layer and NW vessel S-layer. Multivariate image analysis has been performed independently for each type of wood using R (v. 3. 4. 4) (Figure  1 ). Principal component analysis (PCA) was applied on raw spectra using PCA function implemented in FactoMineR package  (v. 1. 40). False RGB images were then obtained using quilt. plot (fields, v. 9. 6). Red, blue and green intensities were based on pixel contribution to first, second and third principal component, respectively.
Based on these false RGB images, specific mean representative spectrum (MRS) were built for each cell wall class, from the average of 90 selected spectra (that is 30 spectra manually selected in one of the three images of each biological replicate of each wood type). Both MRS and pixel spectra were corrected using the detrend function implemented in the prospectr package  (v. 0. 1. 3). Correlations between each pixel spectrum and MRS of its type of wood were determined using the Spearman correlation test implemented in cor. test  (stats, v. 3. 5. 2). The correlation score was used to assign pixels to a cell wall class or to lumen. A pixel was considered as lumen when its correlation scores with all MRS were below the threshold value of 0. 90. Pixels with very similar correlation scores with all MRS (less than 1% difference) were classified as "not assigned" (NA).
Identification of Differentially Absorbed Wavenumbers (DAWNs) DAWNs were determined using the pairwise Wilcoxon test from the R stats package (v. 3. 5. 2). A Bonferroni adjusted P-value of 0. 001 was used as cut-off criterion. Only DAWNs located at a local maximum (± 3 cm -1 ) were considered for further analysis. Local maxima were identified on the average spectra resulting from all pixels assigned to a class of cell wall. A single wavenumber (s) was considered as a local maximum when its absorbance was higher compared to s-2 and s+2 cm -1. The assignation of DAWNs to cell wall components was based on an in-lab database (Table  S1 ) compiling data from the literature and from the Histochem database  (Durand et al. , 2019 ; https://pfl. grignon. inra. fr/shistochem/).
Statistical Analysis For each cell type, a PCA was carried out on the whole spectra to discriminate between the different types of wood. Loadings were calculated as follow: Loadings = Eigenvector Á ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Eigenvalues p. Due to the huge size of the data set, pixels corresponding to the cell wall of each cell type in each image were averaged to produce heatmaps using heatmap. 2 function (gplots package, v. 3. 0. 1. 1), scaling on columns. Heatmaps dendrograms were calculated considering Pearson's correlation between wavenumbers as a distance.
TW and G fibers have been extensively studied using various complementary methods (FTIR, Raman imaging, biochemical analyses, immunolocalization). These are valuable data to validate our results using in situ IR profiling. In this study, the IR spectra of TW fiber G-layer is indeed very similar to what was obtained by  Olsson et al. (2011)  on TW and isolated G-layers, with a large decrease of the bands at 1,736 and 1,236 cm -1 , no bands at 1,594 and 1,506 cm -1 , and the appearance of 3 specific bands at 1,136, 1,316, and 1,200 cm -1. Basically, it has also the same profile as the one presented in  Gierlinger et al. (2008) , except that we did not observe, the large band at 1,645 cm -1 attributed to the deformation of vibration of adsorbed and free water: this probably reflects a difference in the water status of the samples analyzed.
ACKNOWLEDGMENTS The authors gratefully acknowledge the  LICA (Laboratoire d'Ingéniérie Cellulaire de l'Arbre)  and  Phenobois (Wood and Tree Physicochemical Phenotyping Facility for Genetic Resources ) for the provision of infra-red instruments. Phenobois is supported by the programme " Investments for the Future"  ( ANR-10-EQPX-16 , XYLOFOREST) from the  French National Agency for Research. The authors would also like to thank the staff of the  INRAE experimental unit GBFOR  ( UE 911 ) for the establishment and management of the experimental plantation in Orléans, France. CC has been funded by a  University  fellowship.
Protein identification The peak list files were generated with Distiller version 2. 1. 0. 0 software (www. matrixscience. com/distiller) using the following parameters: minimum mass set to 500 Da, maximum mass set to 6,000 Da, no grouping of MS/MS spectra, precursor charge set to auto, and minimum number of fragment ions set to 5. Protein database searching was performed with Mascot 2. 6 (Matrix Science) against the L. infantum (txid5671, 16,895 proteins) protein database (RefSeq assembly accession: GCF_000002875. 2; last access November 2019). The mass tolerances for precursor and fragment ions were set to 10 ppm and 0. 6 Da, respectively. Trypsin was used as the enzyme, allowing for up to 1 missed cleavage. Cysteine carbamidomethylation was specified as a fixed modification, and methionine oxidation as variable modifications.
Data analysis was performed using Scaffold (version 4. 11. 0). Peptide identifications were accepted if they could be established with a probability greater than 80. 0%. Protein identifications were accepted if they could be established at greater than 95. 0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm  [47]. Proteins that contained similar peptides and could not be differentiated using MS/MS analysis alone were grouped to satisfy the principles of parsimony. The final number of peptides per protein was represented by the average of the three biological replicates after normalization to the total number of peptides. Normalization of total spectrum counts (TSC) for each protein was performed automatically using Scaffold.
Statistical analyses of data Statistical analyses were performed using the unpaired Student's t-test ( � p � 0. 05, �� p � 0. 01) and Shapiro-Wilk Test for normality, followed by Kruskal-Wallis rank test ( ���� p � 0. 0001) and post-hoc Nemenyi (Tukey and Kramer) test ( �� p � 0. 01, ���� p � 0. 0001). The data were analyzed using GraphPad Prism 8. 0 software (GraphPad Software, La Jolla California, USA) and R 3. 6. 1 software  [54].
01 and p � 0. 05, respectively). The next step was to evaluate if the shift in terms of average size of EVs was affecting the global distribution, or rather specific subpopulations, of said particles. To this end, the NTA datasets corresponding to sizes ranging from 0 to 500 nm, previously identified in Different quantitative analyses using NTA datasets were performed to verify the likelihood of a direct link between drug resistance and the capacity of Leishmania parasites to release different EV subpopulations. For all these comparisons, the number of particles was normalized to 10 6 parasites. Strain Sb2000. 1 was the only cell line showing a strong tendency toward reduced EV secretion. However, this difference was not significant when compared with the WT (Fig  2B ; p = 0. 065). As depicted in Fig 2C, particles < 100 nm accounted for approximately 32.
We were therefore able to identify common and unique proteins within the same strain, as well as among the different cell lines. Proteomic data was first analyzed using the Scaffold software, which was able to identify a high number of proteins and protein clusters. Subsequent analyses were performed using independent proteins rather than clusters (S1 Data). LC-MS/MS analysis identified 152, 194, 264 and 70 proteins shared through three biological replicates for the WT, Sb2000. 1, MF200. 5, and AmB1000. 1 mutants, respectively (Fig  3A ). Moreover, we evaluated the total protein diversity of EVs belonging to each strain (Fig  3B ). The global proteomic profile of the EVs released by the resistant strains showed significant differences when compared with the WT (S3 Fig). The diversity was variable depending on the replicate, especially for the WT.
2016 ) and to identify a novel fusion mechanism  (Douam et al. 2018). However, a computational strategy is needed to compute coevolution using BIS2 in large sets of viral protein sequences. The coevolution analysis of HBV sequences represents a computational challenge due to the large number of highly conserved sequences available for each HBV genotype. In this study, we designed the new algorithmic strategy BIS2TreeAnalyzer to compute coevolution using BIS2 coevolution analysis method iteratively. BIS2TreeAnalyzer was applied to chosen subsets of four large datasets of Pol sequences belonging to the major HBV Genotypes A, B, C, and D. The computational approach is illustrated in Fig. 1  and highlights the choice of appropriate subsets of sequences for coevolution analysis. We consider the full set of Pol sequences in the reference database HBVdb  (Hayer et al.
For those, 360 MSAs have been generated for datasets of sequences characterised by a different size (250, 500, 750, 1,000 sequences) and different alignment lengths  (152, 159, and 850 amino acids, respectively). All the three methods present very high specificity and very low sensitivity (not shown). The comparative analysis of their positive predictive value (PPV) is reported in Supplementary Fig. S4  and Table  S1. For alignments of medium length ($150 residues), a common trend is found for all methods: PPV increases with the number of sequences in the MSA and decreases with the percentage of identity (see the first two panels in Supplementary Fig. S4 ). In the simulated alignments based on medium sequence identity, the three methods show a very high PPV, and differences across methods are not statistically significant (P-value > 0.
05; first panel in Supplementary Fig. S4 ). On simulated alignments of high sequence identity, the differences between GREMLIN and BIS2TreeAnalizer are not statistically significant (P-value > 0. 05), whereas a statistically significant difference is shown for both GREMLIN and BIS2TreeAnalyzer with hpPCA for the alignments of 250, 750, and 1,000 sequences (second panel in Supplementary Fig. S4 ). On the datasets with very high sequence identity (third panel of Supplementary Fig. S4 ), BIS2TreeAnalyzer outperforms GREMLIN and hpPCA in the alignment of 1,000 sequences. GREMLIN outperforms the other methods in the alignment of 500 sequences. The differences between BIS2TreeAnalyzer and GREMLIN are not statistically significant on 250 and 750 sequences. The PPV of hpPCA is very low for very high sequence identity and this may be explained by the 'L ratio' requirement, defined as the ratio between the number of sequences (L) and the number of positions in the MSA: DCA based methods have been tested primarily with an MSA ratio between 5 and 25 l.
Datasets HBV genotypes show different geographical distribution, disease progression, and response to antiviral treatment  (Kramvis and Kew 2005; Sunbul 2014 ). Each HBV genotype is characterised by a very large number of protein sequences presenting high conservation. All Pol sequences for Genotypes A, B, C, and D were retrieved from HBVdb  (Hayer et al. 2013). We filtered recombinant and incomplete sequences (i. e. truncated proteins) away and obtained 972, 1,809, 2,006 and 955 sequences for Genotypes A, B, C, and D, respectively. The average identity in the datasets is $96 per cent (see Fig. 1  and Table  1 ). A MSA was built for each dataset using Clustal Omega  (Sievers and Higgins 2018). The four alignments were used as input for BIS2TreeAnalyzer. Sequence alignments of HBV Genotypes A, B, C, and D comprise 590, 705, 760, and 647 positions presenting variations over the 832 positions of the entire HBV Pol sequence.
BIS2TreeAnalyzer clusters and mutational patterns in viral sequences All BIS2 clusters were detected in a minimal alignment of twenty sequences. However, the minimal number of mutated sequences required in order to detect a mutational pattern is two. To avoid the possibility that a reported mutational pattern corresponds to viral sequences of the same patient, therefore belonging to the viral genetic background of the patient, some precautions were taken for clusters with mutational patterns supported by less than five sequences: BIS2TreeAnalyzer clusters were discarded if the mutational pattern was supported by sequences from the same patient or with consecutive accession numbers (since patient information is not always available). Patient information was searched in the original publication.
Sequence position numbering The positions of the RT domain are indicated according to a standardised nomenclature system  (Stuyver et al. 2001 ) which is genotype-independent. In this system, the Met of the conserved motif YMDD in the RT polymerase is numbered as rtM204, and the positions of RT are indicated with preposition 'rt'. The remaining positions of the polymerase are numbered according to the sequence of UniProt accession number P03156.
Reconstruction of ancestral states To investigate whether pairs of coevolving positions identified by BIS2TreeAnalyzer may be explained by shared ancestry, we performed a reconstruction of ancestral states for all phylogenetic trees associated with the datasets of sequences of HBV genotypes. Given an HBV genotype set of sequences, the distance tree generated by BIS2TreeAnalyzer was used as input to the Sankoff parsimony algorithm  (Sankoff 1975; Sankoff and Rousseau 1975)  to determine the minimum number of changes required in the given phylogeny to explain the sequences. We used the Wang and Wang matrix  (Wang and Wang 1999)  to compute the cost associated with transitions between states. Then, we analysed the internal nodes of the subtrees where coevolution clusters were detected. The reconstruction of the ancestral node of a subtree allowed us to estimate the number of substitutions and the localisation of the substitutions on the subtree which gave rise to the pattern of coevolved positions.
Generation of artificial data to test BIS2TreeAnalyzer performance Simulated alignments were generated using the MSAvolve tool  (Clark et al. 2014 ) downloaded from http://veloce. med. wayne. edu/$gatti/coevolution/msavolve-simulation-and. html (accessed 2 October 2019). Three MSAs of natural protein sequences were used as input to generate a set of artificial alignments that mimic the statistical properties (e. g. sequence conservation profile, pairwise similarity distribution, conservation at each position) of the real MSA. We used sequences from Pfam protein families PF08082 and PF10596 (FULL datasets), and HBV Genotype A sequences, characterised by 74 per cent, 88 per cent, and 96 per cent sequence identity and by an alignment length of 152, 159, and 850 amino acids, respectively. Importantly, the number and size of the main branches of the phylogenetic tree built from the real alignment are preserved by the simulation.