dataesr/scientific-paragraphs-categorization huggingface dataset:
This inventory is only used for the sake of comparison with the more recent REACT4C_2006 inventory and as baseline for the future perturbations for which 2050 QUANTIFY inventories are used. (Wilkerson et al. , 2010; Brasseur et al. , 2016)  and Community Emissions Data System (CEDS) 2006  (Hoesly et al. , 2018)  inventories. The AEDT and CEDS inventories use the US governmental's Volpe National Transportation Systems Centre data. It should be noted that these inventories differ in terms of not only total fuel use and global emissions but also vertical distributions as described in  Skowron et al. (2013). The QUANTIFY_2000 CO 2 emissions are close to the estimate of 686 TgCO 2 yr -1 for 2000 provided by  Lee et al. (2021). In 2006,  Lee et al. (2021)  estimated an emission of 745 TgCO 2 yr -1 , larger than the REACT4C_2006, ACCRI, and CEDS inventories by 33 %, 25 %, and 4 %, respectively.
Model evaluation We mostly refer to previous publications for a general evaluation of the LMDZ-INCA model performances and comparison against observations for both gas-phase chemistry and aerosols (e. g. Brunner et al. , 2003 Brunner et al. , , 2005;; Hauglustaine et al. , 2004; Folberth et al. , 2006; Myhre et al. , 2013; Hauglustaine et al. , 2014; Koffi et al. , 2016). The version of the LMDZ-INCA model used in this study and extended to the stratosphere has been evaluated by comparing model outputs with observations, particularly in the upper troposphere and lower stratosphere, before the model has been applied to investigate the impact of aircraft emissions. In this study, we summarize this evaluation for two key observational datasets. Figure  1  summarizes the assessment of the modelled ozone seasonal cycle against ozonesondes as compiled by  Tilmes et al.
(2012)  and with respect to the latitude and pressure domains. We present Taylor diagrams to summarize the model biases and correlations with the dataset. The model results are interpolated on both the horizontal and vertical to mimic the 42 ozonesonde station observations over the 1995-2011 observational period. The pressure levels are further grouped into pressure ranges represented by different colours for the sake of clarity. In terms of yearly mean biases, we distinguish two distribution modes for the three geographical domains, although it is less visible in the tropics (0-30 • ): the 100-200 hPa pressure range (corresponding to the lower stratosphere in the middle (30-60 • ) and high (60-90 • ) latitudes), and the other pressure intervals throughout the troposphere. On the one hand, the 200-1000 hPa mean values show a tendency towards negative biases, especially at high latitudes where the modelled yearly averages spread from about 60 % to 100 % with respect to the observations, contrasting with the 80 % to 110 % interval elsewhere.
, 2018). The IA-GOS (https://www. iagos. org/, last access: 7 September 2022) is a European research infrastructure performing in situ measurements on board several passenger aircraft  (Petzold et al. , 2015). Amongst the observed variables, ozone and CO have been monitored since August 1994 and December 2001, respectively. Information on the instruments is available in  Thouret et al. (1998)  and  Marenco et al. (1998)  for ozone and in  Nédélec et al. (2003 Nédélec et al. ( , 2015) )  for CO. The model assessment against the aircraft-based measurements is performed using the Interpol-IAGOS software  (Cohen et al. , 2021)  that entails projecting the IAGOS data onto the model grid and deriving monthly means for each sampled grid cell. The model monthly averages are then derived from the daily output by applying a mask with respect to the IAGOS sampling.
Lastly, for each dataset, these monthly outputs are used to derive seasonal and annual climatologies. For this comparison we ap-ply the methodology for comparison with the global model described in  Cohen et al. (2021). Since all four cruise-altitude levels are regularly crossed by the tropopause, each of them will contain both high-ozone (low-CO) and low-ozone (high-CO) grid cells. To account for these discrepancies while deriving a mean bias, thus to avoid absolute biases in high values to govern the results, the normalized mean value shown in these graphics is derived from the modified normalized mean bias (MNMB). In yearly means, the normalized mean value for ozone spreads from about 70 % up to 125 % and increases with the altitude, consistently with Fig. 1. It is also the case with the dependence on the season, represented by a 10 % difference at 300 hPa and a 30 % difference at 180 hPa.
Model setup In this study, meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis were used. The relaxation of the GCM winds towards ECMWF meteorology is performed by applying a correction term at each time step to the GCM u and v wind components with a relaxation time of 2. 5 h  (Hourdin and Issartel, 2000; Hauglustaine et al. , 2004). The ECMWF fields are provided every 6 h and interpolated onto the LMDZ grid. We focus this work on the impact of aircraft emissions on atmospheric composition, its future evolution, and its direct RF of climate. In order to isolate the impact of aircraft emissions, all snapshot simulations are performed under present-day climate conditions and run for a period of 10 years after a 2-year spin-up. Therefore, ECMWF meteorological data for 2000-2009 are used.
Code availability. The LMDZ-INCA global model is part of the Institut Pierre Simon Laplace (IPSL) Climate Modelling Center Coupled Model. The documentation on the code and the code itself can be found at https://cmc. ipsl. fr/ipslclimate-models/ipsl-cm6/ (IPSL, 2022). The Python codes used for analysing the data and plotting the analysed data are available from the corresponding author upon reasonable request. The distribution of the IAGOS data onto LMDZ-INCA grids is based on an updated version of the Interpol-IAGOS software, available at https://doi. org/10. 25326/81 (Cohen et al. , 2020).
Acknowledgements. We thank  Yunjiang Zhang  for preparing the model aerosol optical depth evaluation against MODIS and AERONET data. The simulations were performed using HPC resources from  GENCI  ( Grand Equipement National de Calcul Intensif ). Financial support. This study was partly funded by the  Direction Générale de l'Aviation Civile (DGAC)  under the IMPACT and CLI-MAVIATION projects and by the  European Union   Horizon 2020 research and innovation programme  under the  ACACIA  project (grant agreement no. 875036 ). Review statement. This paper was edited by  Holger Tost  and reviewed by two anonymous referees.
Data availability. To access datasets of LMDZ-INCA results, please contact Didier Hauglustaine. The IAGOS data set can be found at https://doi. org/10. 25326/20 (IAGOS, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-11987-2022-supplement.
Information sources and search We conducted the first search on 25 March 2020 and updated it on 20 April 2020, 10 June 2020, 2 February 2021, and, for this update, 6 July 2021. We searched the COVID-19 living evidence database  [26] , which uses automated workflow processes to: (1) aggregate simultaneous daily searches of 4 electronic databases (Medline, PubMed, Ovid Embase, bioRxiv, and medRxiv), using medical subject headings and free-text keywords for SARS-CoV-2 infection and COVID-19; (2) deduplicate the records; (3) tag records that are preprints; and (4) allow searches of titles and abstracts using Boolean operators. We used the search function to identify studies of asymptomatic or presymptomatic SARS-CoV-2 infection using a search string of medical subject headings and free-text keywords (S1 Text).
We therefore excluded the following study types: case series restricted to people already diagnosed and studies that did not report the number of people tested for SARS-CoV-2, from whom the study population was derived. We also excluded case reports and contact investigations of single individuals or families, and any study without sufficient follow-up (Box 1). Where data from the same study population were reported in multiple records, we extracted data from the most comprehensive report. "asymptomatic" as an absence of self-reported symptoms. We did not include clinical signs observed or elicited on examination. Asymptomatic infection: a person with laboratory-confirmed SARS-CoV-2 infection, who has no symptoms, according to the authors' report, at the time of first clinical assessment and had no symptoms at the end of follow-up.
Study selection and data extraction Reviewers, including crowdsourced-trained volunteers, worked in pairs to screen records using an application programming interface in the electronic data capture system (REDCap, Vanderbilt University, Nashville, Tennessee, United States of America). One reviewer applied eligibility criteria to select studies and a second reviewer verified all included and excluded studies. We reported the process in a flow diagram, adapted for living systematic reviews  [27]  (S1 Fig). The reviewers determined which of the 3 review questions each study addressed. One reviewer extracted data using a prepiloted extraction form in REDCap and a second reviewer verified the extracted data. For both study selection and data extraction, a third reviewer adjudicated on disagreements that could not be resolved by discussion.
Synthesis of the evidence The data extracted and the code used to display and synthesise the results are publicly available: https://github. com/leonieheron/LSR_Asymp_v5. We used the metaprop, metabin, and metafor functions from the meta package (version 4. 11-0)  [32]  and the ggplot2 package (version 3. 3. 5) in R (version 3. 5. 1). The 95% confidence intervals (CIs) for each study were estimated using the Clopper-Pearson method  [33]. For review question 1, in contact or outbreak investigations, we subtracted the index cases from the total with SARS-CoV-2 infection, because these people were likely to have been identified because of their symptoms and their inclusion might lead to underestimation of the asymptomatic proportion  [16]. For all metaanalyses, we used stratified random effects models.
To compare our findings with other studies, we extracted the raw data from 5 systematic reviews  [11, [13] [14] [15] [16]  and calculated prediction intervals  [17]. For review question 2, as a measure of infectiousness, we calculated the secondary attack rate as the number of SARS-CoV-2-infected contacts as a proportion of all close contacts ascertained. For each included study, we compared the secondary attack rate from asymptomatic or presymptomatic index cases with that from symptomatic cases in the same study. If there were no events in a group, we added 0. 5 to each cell in the 2 × 2 table. We did not account for potential clustering of contacts because the included studies did not report the number and size of infection clusters consistently. We used the Hartung-Knapp method for random effects meta-analysis to estimate a summary risk ratio (with 95% CI)  [34].
This covers the period when vaccines started to be rolled out and the alpha variant of concern became dominant in high-income countries. Although we made extensive efforts to comply with the planned 3 monthly updates, with weekly searches and a continuous process of screening, data extraction and risk of bias assessment, the pace of publications about SARS--CoV-2 exceeds the capacity of our crowd of reviewers  [10, 26]. Our decision to include preprints compensates for some of the delay because these articles appear sooner than peerreviewed publications. In reviews of observational epidemiological studies, search terms are broad so the number of studies that needs to be screened is high, but the yield of included studies is low. The 4 databases that we searched are not comprehensive, but they cover the majority of publications and we do not believe that we have missed studies that would change our conclusions.
Acknowledgments We are very grateful to  Dianne Egli-Gany ,  Michel J. Counotte , and  Stefanie Hossmann  who were part of our original core team of reviewers and contributed to the first 3 versions of this living systematic review. Without their contributions, we would not have been able to complete the fourth and fifth versions. We would like to thank  Alexander Holloway  for his valuable help with programming the automated workflows in our living systematic review processes. This work reflects only the authors' view. The European Commission is not responsible for any use that may be made of the information it contains.
CanSig preprocesses, integrates and analyzes scRNA-seq data to provide new signatures of shared transcriptional states and links these states to known pathways. We show that CanSig successfully rediscovers ground truth pathways determining shared transcriptional states in two simulated and three experimental datasets; the latter spanning 135 patients and 72,000 cells. We then illustrate CanSig's investigative potential by discovering novel signatures in esophageal squamous cell carcinoma possibly linked to targeted patient treatment; we also point out a de novo signature in breast cancer predictive of patients' survival. In the cancer types studied, we juxtapose copy number variation with discovered shared transcriptional states and uncover a genetic component predisposing cancer cells to activation of specific transcriptional programs.
Therefore, a computational approach for cancer-specific signature discovery needs to be developed. We propose here a computational approach implemented in an easy-to-use tool, CanSig, to discover de novo shared transcriptional programs in cancer cells. CanSig takes raw counts from single cell RNA sequencing of several patients as input, preprocesses the data through standard quality control and copy number variation (CNV) inference, integrates the patients while removing unwanted sources of variation, and performs gene set enrichment analysis on any database of choice to uncover potential new programs, while optionally running a stability analysis to select only consistently enriched pathways. We demonstrate that our method successfully rediscovers ground truth shared transcriptional signatures in two simulated and three experimental datasets.
Materials and Methods From patient raw counts to enriched gene sets CanSig consists of three major steps: pre-processing, data integration, and postprocessing (Fig. 1 ). CanSig takes as input a set of matrices of raw counts per patient and cell type annotation and outputs coordinates of single cancer cells in the shared latent space and a list of enriched pathways linked to subspaces in this integrated latent space. The method is highly modular: the user can therefore easily adjust the default parameters and methods to their specific purposes. CanSig's pre-processing module first performs standard quality control (filter cells and genes using expression cut-offs and percentage of mitochondrial RNA). Then the CNV profile is inferred using infercnvpy and the cells are partitioned into malignant, non-malignant and undetermined using both provided cell type annotations and the CNV profile.
Finally, the cells are scored using any previously known signatures. (B) The raw counts of all malignant cells after pre-processing is given as input. CanSig's integration module uses known models to find a latent space that preserves biologically meaningful signals while removing unwanted sources of variation. The module automatically formats the provided input so that it fits the constraints of the chosen model. The latent space can be visualized using the Uniform Manifold Approximation and Projection (UMAP). (C) The full dataset raw count matrix and the latent space coordinates computed with the integration module are provided as input. CanSig's postprocessing module first performs clustering in the latent space using an algorithm of choice. Then it performs differential gene expression analysis for each cluster versus the rest of the cells.
Then the genes are ranked according to their z-score and the ranked list is used as input for the Gene Set Enrichment Analysis (GSEA) algorithm on a database of choice. For each cluster, the list of significantly associated pathways are ranked according to their Normalized Enrichment Score (NES). The cells can be optionally scored according to the set of significantly enriched pathways. Optional additional steps include performing a stability analysis to observe the consistency of the uncovered pathways through several runs of the pipeline, or performing differential Copy Number Variation (CNV) analysis to uncover potential links between signatures and CNVs.
The second step consists in CNV inference to remove cells annotated as malignant that do not present CNVs and cells annotated as non-malignant that present CNVs. To ensure we analyze only malignant cells and thus avoid contamination with healthy cells (e. g. , immune cells), we compute the copy-number variation (CNV) associated with each sample using the infercnv implementation in Python (https://github. com/icbi-lab/infercnvpy). Specifically, we run infercnv using the annotations (T cells, B cells, etc. ) as reference for healthy and unhealthy cells while removing genes with a mean read count under 0. 1. We then use Ward-linkage hierarchical clustering on the CNV profile to obtain a dendrogram of the cells. By definition, a dendrogram node only contains two child nodes that represent clusters of clusters, except leaf nodes that represent cells.
We then iteratively assign a CNV status to the nodes according to the composition of their subtress. In particular, if both the subtrees of a node present at least 60% of non-malignant cells, we annotate it as non-malignant. We traverse the tree until all nodes and/or a maximum depth of five are reached, allowing us to obtain an annotation as "no CNV" or "CNV" for every cell. We finally remove cells originally annotated as healthy that clustered with unhealthy cells (thus presenting CNVs), or cells originally annotated as unhealthy that clustered with healthy cells. Additionally, CanSig can then optionally score cells according to a predetermined set of upregulated genes, corresponding to a previously uncovered signature. To do so, the cell scoring is performed using the score_genes function of the Scanpy package  27  , originally proposed by Tirosh et al.
Integration: removing sources of unwanted variation The second module of the pipeline corresponds to projection of the dataset into a meaningful latent space (Fig. 1B ). The models currently implemented within the CanSig pipeline are Harmony 17 and scVI  15. CanSig automatically processes the full count matrix to fit the requirements of the selected model. For scVI, the most highly variable genes are selected and raw counts are inputted. The batch effect and number of counts are automatically corrected for using scVI  15  , however additional confounding factors can be accounted for at this stage. For Harmony, the data is normalized using counts per ten thousand (CPT) and log-transformation as follows: for a cell  The output of this module is the coordinates of all the cells of the dataset in a meaningful latent space.
The latent space can be visualized using the Uniform Manifold Approximation and Projection (UMAP)  28  applied on the latent space coordinates. Postprocessing: rediscovering expected signatures and their link to new transcriptional signatures and copy number variations The third and final module of CanSig consists in clustering of cells in the latent space followed by differential gene expression analysis and gene set enrichment analysis as well as differential copy number variation analysis (Fig. 1C ). The first part consists in clustering, which can be done using several algorithms currently implemented with CanSig: Leiden clustering  29  , K-Means 30 , GMM  31  , spectral clustering  32  , and HDBSCAN  33. As per the integration module, CanSig automatically processes the data so that it corresponds to the expected input, notably by computing the k-nearest neighbor graph for Leiden clustering.
The second part is differential gene expression analysis, that computes the z-score for each gene for every cluster. The z-score is computed as follows: the number of cells not in cluster. Differential gene expression can also be run with a Wilcoxon  rank-sum test rather than a t-test or a logistic regression as described by Ntranos et al. 34. The third part is gene set enrichment analysis. For each cluster, the list of genes ranked according to their z-score is used as input for the Prerank algorithm of GSEA  16  , run on any valid file in the GMT format, e. g. , any MSigDB database (http://www. gsea-msigdb. org/gsea/msigdb/), including the hallmarks of cancer. CanSig then selects the significantly enriched gene sets after False Discovery Rate (FDR) correction  16  for each cluster that had a positive enrichment score (thus positively enriched in the cluster of interest).
An optional part consists in rescoring each discovered pathway in the cells on a sample-level using the scanpy scoring function with the list of genes of the pathway. To link potential Copy Number Variations (CNVs) with parts of the latent space, another optional part consists in performing differential CNV analysis -indeed, much like differential gene expression, CNVs can be differentially present in a cluster versus the rest of the latent space. We can use the CNV profile per cell inferred by infercnvpy in the pre-processing step or any user-provided CNV profile. We give the option to compute a smoothed version of the infercnv profile by applying the following dynamic thresholding transformation for each gene (https://github. com/broadinstitute/inferCNV/wiki/De-noising-Filters): 𝐶𝑁𝑉 𝑔,𝑖 = {1 𝑖𝑓 𝐶𝑁𝑉 𝑔,𝑖 > µ 𝑔 + 1.
Stability analysis: finding consistently enriched pathways in the dataset The final step in CanSig is a stability analysis of the uncovered pathways. The underlying assumption is that if the pathways uncovered are consistently enriched independently of the choice of hyperparameters, these pathways are more likely to represent the true biology of the subset of cells, rather than a potential processing artifact. Thus, to assess the stability of the uncovered gene sets, CanSig gives the opportunity to run the pipeline with several hyperparameters to observe which gene sets systematically are significantly enriched. The normalized enrichment score (NES)  16  of the pathways can then be visualized as a heatmap (Fig. 1C ).
Datasets We used two simulated and three real-world single cell RNA sequencing datasets for our analysis. The simulated data was created using the Splatter package  35  (details in Appendix A). The first dataset is an esophageal squamous cell carcinoma (ESCC) dataset published by Zhang et al  7. The second is a colorectal carcinoma (CRC) dataset published by Pelka et al  5. The third is a breast cancer dataset published by Wu et al. 36. Known signatures were extracted from the papers of origin. Most of the analysis was conducted using the Scanpy 27 package.
Applying CanSig to simulated datasets To investigate whether CanSig is capable of uncovering true biological signals present within the data while correcting for technical bias, we applied CanSig to two simulated datasets created with Splatter  35. We sought to simulate a dataset similar to the real-world data encountered (see Appendix A). Notably, we simulated a dataset (simulated dataset A) with eight different uneven batches and three programs expressed in each batch. The programs corresponded to a gradient rather than to a discrete identity. We also used the same dataset to generate a new one (simulated dataset B) by removing two programs for three different patients, effectively simulating inter-patient heterogeneity in the dataset. We applied CanSig, without the pre-processing module, to both these datasets.