dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  M. Kuhn  and  E. Mavrodiev  for assembling trait data. S. Epstein ,  T. Girard-Ang , P. Pezzi, and  L. Xiao  assisted with lab work. J. Barber ,  M. Brownlee ,  S. Cinel ,  J. Daniels ,  R. Godfrey ,  H. Gough ,  C. Hamilton ,  G. Hill ,  P. Houlihan ,  C. Huang ,  J. Miller ,  K. Miner ,  C. Mitter ,  K. Mitter ,  A. Renevier-Faure ,  J. Rubin ,  M. Scallion ,  Y. Sondhi , and  L. Wu  provided specimens or helped improve the manuscript. RAPiD Genomics (Gainesville, FL, USA ) conducted sequencing. K. Meusemann  ( 1KITE ) provided unpublished FcLM scripts. T. Barve ,  C. Couch ,  H. Dansby ,  K. Casarella ,  R. Merritt , and  X. Zheng  helped create figures. High performance clusters at  Brigham Young University, Smithsonian Institution, University of Florida , and  Zoological Research Museum Alexander Koenig  provided computational support.
Funding: Funding came from the  US National Science Foundation (NSF)   GoLife "ButterflyNet" collaborative grant  ( DEB-1541500 ,  1541557 ,  1541560 ) to AK, RG, DL, and NEP. Specimen collection and preservation was funded by  NSF   DBI-1349345 ,  1601369 ,  DEB-1557007  and  IOS-1920895  (AYK),  NSF   DEB-1120380  (DJL), Grants  9285-13  and  WW-227R-17  from the  National Geographic Society (DJL) ,  NSF   DBI-1256742  (AYK and KRW),  NSF   DEB-0639861  (KRW) and  NSF   SES-0750480 ,  DEB-0447244  and  DEB-9615760  to NEP. ME was supported by the  Research Council of Norway  (# 204308 ), and the  Hintelmann Scientific Award for Zoological Systematics. FLC is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  GAIA , # 851188 ).
42. Published sequences comprised: 1) genome assemblies; 2) genomic reads; and 3) paired or 4) single-end transcriptomes. Three sequence datasets were created for this study: nt123 (a nucleotide dataset with all codon positions), degen (a nucleotide dataset that excludes all synonymous changes, created using the perl script, Degen1 v. 1. 4  43, 44 ), and aa (an amino acid dataset translated from the nt123 dataset) (Data S3).
Phylogenetic analysis and dating. Maximum likelihood (ML) tree inference was conducted on all three datasets in IQ-TREE 2. 0 (nt123, degen, and aa), and parameter settings for each analysis can be found in Table  S16. Branch support was calculated with 1,000 ultrafast bootstrap replicates (UFBS; '-B 1000' command)  45, 46  , and Shimodaira-Hasegawa approximate likelihood ratio tests (SH-aLRT; '-alrt 1000' command)  47. Quartet sampling (see Section 8. Sequence quality check) was performed on the most-likely degen359 and aa154 trees to examine the topology and provide more comprehensive and specific information on branch support. Four-cluster likelihood mapping (FcLM) analyses  48  were performed on the degen and aa datasets to assess the placement of particular butterfly clades that have been the subject of previous phylogenetic studies.
Then, by studying the distribution of unspliced and spliced reads across all cells (unsplicedspliced RNA space), it is possible to estimate if a specific gene is being transcribed or inactivated in any single cell. This can be simultaneously applied to all genes, in all cells of a population. With such analysis, cell-cycle-related genes would be expected to undergo activation and deactivation phases within a single cell cycle, resulting in a circular pattern in the unspliced-spliced RNA space. Thanks to the depth of the scRNA-seq datasets generated in this study, such circular patterns in the unspliced-spliced RNA space can be observed clearly for a subset of genes, and exploited to naturally stratify cells across the cell cycle. The challenge is then to assign, in a reliable and robust manner, a single parameter to each cell that describes its cell-cycle state combining the information in the cycling patterns.
To accomplish this, we designed DeepCycle (https://github. com/andreariba/DeepCycle), a deep learning method to ascribe a continuous high-resolution cell cycle trajectory to single cells based on RNA velocity. The approach applies to different cell types and has self-consistency checks to establish whether the analysis worked properly. Deep-Cycle allows us to fit the dynamics of gene activation and inactivation in the unspliced-spliced RNA space with minimal assumptions, and assign cells to cell cycle stages, generating gene expression series. Different cell types have specific cell cycle dynamics  10, 11. For example, the cell cycle is deeply affected by the degree of stemness, such that pluripotent and neural stem cells have short G1 phases, while committed cells extend their G1 phases and present with longer overall cell cycles  [11] [12] [13].
Thanks to DeepCycle, we not only recapitulate these findings in mESCs and human fibroblasts, but also extend the analysis to a public dataset of ductal cell progenitors. This allows us to suggest underlying regulatory mechanisms involved, highlighting different genes and transcription factors that are active in the different cellular models across the cell cycle. Finally, as most of the cells within multicellular organisms are not actively cycling, tight control over cell cycle entry and exit is critical, as seen for example in embryonic development, hematopoiesis, activation of adaptive immune responses, and wound healing  14. However, in diseases like cancers, cells do not consistently respond to the normal regulatory cues and signals. It is therefore important to understand the processes that determine cell cycle entry, cell cycle progression, and exit to quiescence  14.
Results Generation of deep-sequenced single-cell RNA-seq datasets. To robustly study the cell cycle, we reasoned that the dataset should be enriched for proliferating cells. The majority of public scRNAseq datasets have been generated to study the overall population of cells in a given condition, and, typically, they contain heterogeneous cell types. Therefore, we compared three distinct populations of proliferating cells. First, we cultured mouse embryonic stem cells (mESCs) in 2i+LIF medium to maintain the ground state of pluripotency by blocking differentiation  [15] [16] [17]  , and generated a scRNA-seq library from more than five thousand mESCs (Fig. 1A ). Then, we included ductal cell progenitors from pancreas development in mice in our scRNA-seq analysis (henceforth referred to as ductal cells) (Fig.
1B )  18. These cells have been linked to a proliferative cell state by specific marker genes  19. Finally, to compare the results to a different cell type from another organism we also sequenced 5367 human fibroblasts (Fig. 1C ). These fibroblasts separate into two subpopulations, only one of which expressing cell cycle genes (Supplementary Figs. S1, S2, 16 out of the top 25 genes belong to the DAVID Keywords Cell cycle, Benjamini = 2e-15), therefore we first focused on the proliferative subpopulation (n = 3086). The three datasets (mESCs, ductal cells, and fibroblasts) present different sequencing depths: mESCs and fibroblasts have ~30 thousand unique molecular identifiers (UMI) per cell, median values of 31977 and 27319 UMIs, respectively, a depth that is high for the recent standards; while the ductal cells are as low as 8 thousand UMIs per cell, the median value of 8043 (Fig.
1D ). Similarly, the median number of genes identified per cell varies from 2840 in the ductal cells to 5161 and 5630 in fibroblasts and mESCs (Fig. 1D ). These differences across samples might be partially explained by their respective sequencing depths: total spliced and unspliced reads of 8 M, 210 M, and 220 M in ductal cells, fibroblasts, and mESCs, respectively (Fig. 1D ). Overall, they have similar fractions of unspliced reads (Fig. 1E ). All the datasets contain genes with circular patterns in the spliced-unspliced read space in accordance with the RNA velocity theory (Fig. 1F ). Cycling genes are expected to be characterized by fully circular patterns as they complete both their activation and deactivation phases (Fig. 2A ). Overall, these datasets constitute a unique opportunity to study gene regulation throughout the cell cycle in different mouse and human cell types.
Inference of a cell-cycle transcriptional phase from single-cell RNA-seq data. The dynamical state of a gene can be inferred by comparing its unspliced and spliced reads  9. Unspliced reads indirectly measure the nascent transcripts, and the spliced ones the mature messenger RNAs (see Figs. 1F  and 2A ). The comparison of the two quantities at the single-cell level allows the inference of the transcriptional activation, or deactivation, of a gene. The original RNA velocity framework proposed by La Manno et al. 9  assumed either constant velocity or constant unspliced molecules; to overcome this limitation, Bergen et al. 19  developed an extension of the original model to include intermediate states and more flexible dynamical parameters (scVelo). However, the extended model was unable to fit the actual dynamics for the genes in our datasets, while the inferred latent time did not capture the correct dynamics of the cells (see Supplementary Fig.
DeepCycle is constructed as an AE with a single latent variable representing the cell-cycle transcriptional phase θ that is then transformed with cosine and sine functions in the first layer of the decoder (Fig. 2C  and Supplementary Fig. S4 ). To train DeepCycle, we used the expression of unspliced and spliced RNAs of the genes in the GOterm:cell_cycle (n = 532, see 'Implementation of DeepCycle' in Methods) determining circular paths for cycling genes in the unspliced-spliced space and removing technical noise or biological fluctuations associated with stochastic gene expression (see examples in Fig. 2C, D  and Supplementary Fig. S5 ). Finally, a transcriptional phase is assigned to each cell in the dataset (see 'Implementation of DeepCycle' in Methods) and the dynamics of unspliced and spliced RNA with respect to the transcriptional phase can be further analyzed.
It is important to note that the transcriptional phase is a nonlinear monotonic function of time that can be arbitrarily complex, so we cannot directly infer temporal dynamics with it. Importantly, DeepCycle robustly returns very similar transcriptional phases by selecting as input the genes showing multiple maxima in the unspliced-spliced space (Supplementary Fig. S6  and 'Implementation of DeepCycle' in Methods). The genes presenting multiple maxima (n = 158) are listed in Supplementary Data 1 that includes cycling genes not yet considered in the GO term:cell_cycle that could be added as markers of the cell cycle. Finally, we compared DeepCycle with Cyclum 7 , a recent method developed for the analysis of the cell cycle in scRNA-seq data also based on an AE. Strikingly, Cyclum was not able to place cells consistently in a circular 1D manifold and therefore could not correctly identify the cell-cycle progression of single cells when applied to our datasets (Supplementary Fig.
C DeepCycle infers the transcriptional phase of each cell. It takes as input the spliced-unspliced reads (s i , u i ) for a set of n genes (i = 1,…,n). By fitting the transcriptional phase θ, it can denoise and predict the unspliced-spliced expressions for each transcriptional phase. D Examples of cycling genes and fits of the RNA velocity patterns (red lines) in mESCs and human fibroblasts. E Cyclin E and B levels along the transcriptional phase in the three datasets. Negative controls for non-cycling genes have been added for comparison in Supplementary Fig. S10. the spliced from the input to DeepCycle, the inferred transcriptional phase becomes inconsistent (see Supplementary Fig. S8  for details). Another recent method to analyze the cell cycle at the singlecell level is Revelio 8. This method is based on the inference of a "cylindrical" manifold in the multidimensional gene expression space.
3A ). Other possible marker genes show cycling patterns as expected, e. g. Orc1/ORC1, Mcm6/MCM6, Ccne1/CCNE1, Ccna2/CCNA2, and Ccnb2/CCNB2 (Fig. 3A ). To simplify the comparison of the cell cycles across datasets, the transcriptional phases were normalized between 0 and 1 and were aligned such that mitosis occurs at θ = 1. The paths of the cells around the cell cycle can easily be identified in the 2-dimensional projections (Fig. 3B ). Though the extended RNA velocity model  19  did not capture the correct dynamics at the level of the single gene (Supplementary Fig. S3 ), it could infer the correct dynamics of transcriptional changes at the cell level (see the velocity plots in Supplementary Fig. S12 ). To add a complementary experimental validation independent of the scRNA-seq, we designed a series of bulk RNA-seq in a cell cycle-sorted population of cells (G1, S, G2/M).
The mRNA expression of the Cdc25 family of proteins shares the same behaviour across the datasets, i. e. Cdc25a/CDC25A increases at the G1/S transition while Cdc25bc/CDC25B-C at the G2/M (Supplementary Fig. S16 ), consistently with the function of their protein products  41. The minichromosome maintenance protein complex (Mcm) is a heterohexamer, formed by Mcm2-7/MCM2-7 proteins, which works as a helicase that unwinds the double-stranded DNA and powers the replication fork progression during the S phase  42. As expected, the mRNA levels of all subunits of the Mcm peak at the beginning of the S phase for all the datasets (Supplementary Fig. S16 ). Cdk1/CDK1 mRNA level increases in G2 and M phases as required by its protein function  43  (Fig. 3A ). The other main Cdk mRNAs (Cdk2-4-6/CDK2-4-6) show lower expression levels across phases and are less consistent across the datasets, they might rather be regulated at the protein level, translationally or post-translationally (Supplementary Fig.
The motif analysis predicts that most TF activity takes place in the G1 phase when cells need to decide whether to go into another round of replication or to arrest the cycle in order to accomplish a new function (Fig. 4A ). Among the most significant activities, Yy1/YY1 targets are upregulated in the G1 phase in all the datasets, suggesting a general role during the cell cycle (Fig. 4A, B ). Indeed, Yy1 is known to induce proliferation and maintain pluripotency of mESCs through the BAF complex  46. Interestingly, Yy1 binds to chromosomes during mitosis  47  and, accordingly, its transcription starts already in the G2/M phase suggesting a pioneering activity at the of a new cycle (Fig. 4B ). For both mouse datasets (mESCs and ductal cells), the E2f family appears as a critical group of regulators.
Discussion We generated scRNA-seq datasets in mouse embryonic stem cells and human fibroblasts with high sequencing depth. The circular RNA velocity patterns emerged clearly in cell-cycle regulated genes revealing the activation/inactivation phases that these genes undergo during the cell cycle. We developed DeepCycle, a deep learning approach, to exploit the RNA velocity patterns and study gene regulation dynamics during the cell cycle. DeepCycle assigns a cell-cycle transcriptional phase for each cell by fitting the RNA velocity patterns. Furthermore, the inferred transcriptional phase can be associated with cell-cycle phases thanks to known gene markers. Thus, DeepCycle allows us to determine the cell-cycle progression state of each cell from scRNA-seq data and identify genes involved in the cell cycle.
The analysis highlighted known and unknown cell cycle regulators in established cell lines, identifying two major waves of transcription in the G1 phase of differentiated cells while pluripotent cells seem to undergo a single wave of transcription during G1. The two waves are likely to be associated with the restriction point where the cells finally commit to undergoing another cell cycle. Further, for the first time, we could observe single cells exiting from the cell cycle in a scRNA-seq sample and disentangle the underlying regulations, thereby providing lists of targets for the regulation of the cell cycle and the quiescent states in mammalian cells. We envision that our approach will facilitate the characterization of the branching point between the S and G0 phases in multiple cellular models by applying it to other scRNAseq datasets.
For the human fibroblasts, the single-cell 3 prime mRNA seq library was generated according to 10x Genomics User Guide Chromium NEXT GEM Single Cell 3' Reagent Kits v3. 1 (P/N CG000204 Rev D). The raw and processed data for both libraries were stored on the GEO (Accession number: GSE167609). CellRanger outputs have been processed with velocyto 9 (version 0. 17. 17) and analyzed using scanpy  84  (version 1. 4. 4. post1) and scvelo 19 (version 0. 2. 2) and imputed spliced and unspliced reads from scvelo. pp. moments have been used for the analysis. Cell cycle assay and flow cytometry. Cells were harvested by trypsin as before and washed once with PBS. About 2×10 6 cells were resuspended in 100 µl PBS and added drop-by-drop to 900 µl 95 % ethanol, while mixing, then stored at +4 °C overnight.
Cells were then collected by centrifugation, washed once with PBS, resuspended in 1 ml staining buffer (50 µg/ml propidium iodide, 2 mM MgCl 2 , 50 ng/ ml RNaseA [EN0531, ThermoScientific] in PBS) and incubated for 20 min at 37 °C. Stained cells were washed once with PBS and analyzed on BD LSRII flow cytometer. The fcs files were processed with fcsparser (https://github. com/eyurtsev/ fcsparser). For the mESCs, the debris in the data was removed by filtering SSC-H and SSC-W values higher than 140,000 and 100,000, respectively, and by selecting cells with a Hotelling T 2 value lower than 6 in the FSC-A SSC-A space, see Supplementary Fig. S15. The filtering retained more than 80% of the original cells (~26k out of 31k). For the human fibroblasts, SSC-H values lower than 25,000 and SSC-H and SSC-W values greater than 150,000 and 110,000, respectively, were excluded.
2C , D and black in Supplementary Fig. S5 ) for all the genes used for the training (GO term: cell_cycle, GO:0007049). DeepCycle implementation was stored in the GitHub repository https://github. com/andreariba/DeepCycle. Detection of the cell cycle phase transitions. The transcriptional phase contains only the information about the succession of states composing the cell cycle. To annotate the different cell cycle phases, we analyze the expression of cell cycle markers  30  and the number of RNA counts per cell. We computed 2 scores based on the marker genes in the S and G2M phases  30. For all the expressed genes in the two lists we computed the z-score expression across all the cells and then calculated an average z-score per cell for all the genes. Similarly, we computed a CyclinE score by considering only Ccne1-2/CCNE1-2 genes.
Finally the transitions are defined as follows. • M/G1: the first bin across the transcriptional phase when the number of RNA counts (UMI) per cell drops; • G1/S: the first bin across the transcriptional phase after the CyclinE score reaches its maximum; • S/G2: the theta where the G2M score becomes greater than the S score. The detailed analysis for the three datasets is shown in Supplementary Fig. S11. Validation bulk RNA-seq in cell cycle-sorted populations of cells. The three samples were sorted as in section 'Cell cycle assay and flow cytometry' and the libraries have been prepared as follows. Total RNA from 5×10 5 cells in G0/G1, S and G2/M phases was extracted using the Machery-Nagel NucleoSpin RNA kit (740955. 50) according to the manufacturer's instructions. Total RNA-Seq libraries were generated from 500 ng of total RNA using TruSeq Stranded Total RNA Library Prep Gold kit and TruSeq RNA Single Indexes kits A and B (Illumina, San Diego, CA), according to manufacturer's instructions.
The path detection was implemented into two steps. First, coarsegrained paths were drawn by slicing the spliced-unspliced landscape and connecting the minima found across the successive slices. To refine the identified paths we implemented the Nudged Elastic Band 65 with the addition of a viscosity term to stabilize the dynamics, we called the method Viscous Nudged Elastic Band (VNEB). The VNEB was also applied to the fibroblasts dataset to identify the paths connecting the G1 phase to the S and G0 phases in Fig. 5E.
Acknowledgements We thank the cell culture service, the flow cytometry service and the imaging center of the  Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC). We thank specially the national platform GenomEast for the sequencing and the 10x Genomics reagents. Special thanks to  Deepak Alapatt  for helping during the implementation of DeepCycle and  Alastair McEwen  for carefully reading the manuscript. This study was supported by funds from  Conseil National de la Recherche Scientifique ,  Institut National de la Santé et de la Rechrche Médicale  and  Université de Strasbourg ; the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02  (NM); the fellowship «IDEX chaires attractivitérecherche» granted by the  University of Strasbourg (NM) ; and, the  USIAS  fellowship granted by the  University of Strasbourg (NM).
Code availability The code of DeepCycle can be downloaded from the GitHub repository https://github. com/andreariba/DeepCycle. Data analysis was perfomed using python 3. 7. 9 and the following pyhton packages: scipy 1. 5. 2, numpy 1. 19. 1, pandas 1. 1. 1, scikit-learn 0. 23. 2, tensorflow 2. 2. 0, anndata 0. 7. 4, matplotlib 3. 3. 1, seaborn 0. 10. 1, scanpy 1. 4. 4. post1, scvelo 0. 2. 2 and velocyto 0. 17. 17.
[31]. This seasonal temporal profile used for the temporalization of emissions-the same one applied to the entire country-leads to a maximum in NH 3 emissions systematically in April  [32]. NH 3 emissions probably present a diurnalvariability, especially due to higher afternoon temperatures favoring ammonium evaporation  [33]. This feature is however not included in our emission database, which keeps daily and monthly constant values. The annual budget for NH 3 emissions over our domain is 4114 ktNH 3 in 2008. The chemical scheme used in CHIMERE is MELCHIOR-2, with more than 100 reactions  [18] , including 24 for inorganic chemistry. For inorganic species, aerosol thermodynamic equilibrium is achieved using the ISORROPIA model  [34]. The parameterization of NH 3 dry deposition is unidirectional in CHIMERE.
IASI-v3R NH 3 VCD To quantify and understand the recent variability of NH 3 over the 2008-2015 period, we use data from the IASI-A instrument, flying on a low sun-synchronous polar orbit since October 2006, with equator crossing times of 09:30 (descending mode) and 21:30 (ascending mode) LST  [37]. The algorithm used to retrieve NH 3 columns from the radiance spectra is described in Whitburn et al. [38] , Van Damme et al. [39] , and Franco et al. [40]. We use the ANNI-NH3-v3R-ERA5 dataset, called IASI-v3R in the following, which is relevant for inter-annual comparison and trend analyses  [11]. It relies on ERA5 ECMWF meteorological input data  [41]. The IASI total columns are averaged into "super-observations" (average of all IASI data within the 0. 5 • × 0. 5 • resolution and within the physical time step of CHIMERE).
Acknowledgments: This research has been supported by the  Centre National d'Etudes Spatiales ,  CNES. The research in Belgium was funded by the  Belgian Science Policy Office (BELSPO)  (Prodex HIRS and FED-tWIN ARENBERG) and the  Air Liquide Foundation  ( TAPIR  project). L. Clarisse is Research Associate supported by the  Belgian F. R. S. -FNRS. This work was granted access to the HPC resources of TGCC under the allocation  A0070107232  and  A0080107232. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The authors acknowledge the AERIS data infrastructure for providing access to the IASI data in this study and  ULB-LATMOS  for the development of the retrieval algorithms.