dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-cell gene expression data set We used the data set generated by  Sasagawa et al. (2013)  retrieved from the Gene Expression Omnibus repository (accession number GSE42268). We analyzed expression data corresponding to embryonic stem cells (ESC) in G1 phase, for which more individual cells were sequenced. A total of 17,063 genes had non-zero expression in at least one of the 20 single cells. Similar to  Shalek et al. (2014) , a filtering procedure was performed where only genes whose expression level satisfied log[fragments per kilobase of transcripts per million mapped fragments (FPKM) + 1]. 1. 5 in at least one single cell were kept for further analyses. This filtering step resulted in a total of 13,660 appreciably expressed genes for which transcriptional noise was evaluated.
Genome architecture The mouse proteome from Ensembl (genome version: mm9) was used to get coordinates of all genes. The Hi-C data set for ESCs from  Dixon et al. (2012)  was used to get 3D domain information. Two genes were considered in proximity in one dimension (1D) if they are on the same chromosome and no protein-coding gene was found between them. The primary distance (in number of nucleotides) between their midpoint coordinates was also recorded as 1D a distance measure between the genes. Two genes were considered in proximity in 3D if the normalized contact number between the two windows that the genes belonged to was non-null. Two genes belonging to the same window were considered to be in proximity. We further computed the relative difference of SGE between two genes by computing the ratio ðF * 2 2 F * 1 Þ ðF * 2 þ F * 1 Þ: For each chromosome, we independently tested whether there was a correlation between the primary distance and the relative difference in SGE with a Mantel test, as implemented in the ade4 package  (Dray and Dufour 2007).
To test whether genes in proximity (1D and 3D) had more similar transcriptional noise than distant genes, we contrasted the relative differences in transcription noise between pairs of genes in proximity and pairs of distant genes. As we test all pairs of genes, we performed a randomization procedure to assess the significance of the observed differences by permuting the rows and columns in the proximity matrices 10,000 times. Linear models accounting for "spatial" interactions with genes were fitted using the generalized least squares (GLS) procedure, as implemented in the nlme package for R. A correlation matrix between all tested genes was defined as G ¼ fg i;j g; where g i;j is the correlation between genes i and j. We defined g i;j ¼ 1 2 expð2ld i;j Þ; where d i; j takes 1 if genes i and j are in proximity, and 0 otherwise (binary model).
TFs and histone marks TF mapping data from the Ensembl regulatory build  (Zerbino et al. 2015)  were obtained via the biomaRt package for R. We used the Grch37 build as it contained data for stem cell epigenomes. Genes were considered to be associated with a given TF when at least one binding evidence was present in the 3-kb upstream flanking region. TFs associated with more than five genes for which transcriptional noise could be computed were not considered further. A similar mapping was performed for histone marks by counting the evidence of histone modifications in the 3-kb upstream and downstream regions of each gene. A logistic principal component analysis (PCA) was conducted on the resulting binary contingency tables using the logisticPCA package for R  (Landgraf and Lee 2015) , for TF and histone marks separately.
Biological pathways, PPIs, and network topology We defined genes either in the top 10% least noisy or in the top 10% most noisy as candidate sets, and used the Reactome PA package  (Yu and He 2016)  to search the mouse Reactome database for overrepresented pathways with a 1% false discovery rate (FDR). Centrality measures were computed using a combination of the igraph  (Csardi and Nepusz 2006)  and graphite  (Sales et al. 2012 ) packages for R. As the calculation of assortativity does not handle missing data (that is, nodes of the pathway for which no value could be computed), we computed assortativity on the subnetwork with nodes for which data were available. Reactome centrality measures could be computed for a total of 4454 genes with expression data. PPIs were retrieved from the iRefIndex database  (Razick et al.
Linear modeling We simultaneously assessed the effect of different factors on transcriptional noise by fitting linear models to the gene-specific F* estimates. To avoid colinearity issues of intrinsically correlated explanatory variables, we conducted a data reduction procedure using multivariate analysis. We used variants of PCA on explanatory variables in three groups: network centrality measures, Ka/Ks and gene age with standard PCA, and TF-binding evidence and histone methylation patterns using logistic PCA, a generalization of PCA for binary variables  (Landgraf and Lee 2015). In each case, we used the most representative components (totaling $75% of the total deviance) as synthetic variables. PCA analysis was conducted using the ade4 package for R  (Dray and Dufour 2007)  and logistic PCA was performed using the logisticPCA package  (Landgraf and Lee 2015).
We built a linear model with F* as a response variable and 13 synthetic variables as explanatory variables. As the synthetic variables are PCs, they are orthogonal by construction. The fitted model displayed a significant departure to normality and was further transformed using the Box-Cox procedure ["boxcox" function from the MASS package for R  (Venables and Ripley 2002) ]. Residues of the selected model had normal, independent residue distributions (Shapiro-Wilk test of normality, P-value = 0. 121; Ljung-Box test of independence, P-value = 0. 2061) but still displayed significant heteroscedasticity (Harrison-McCabe test, P-value = 0. 003). To ensure that this departure from the Gauss-Markov assumptions does not bias our inference, we used the "robcov" function of the rms package to get robust estimates of the effect significativity  (Harrell 2015).
Additional data sets The aforementioned analyses were additionally conducted on the bone marrow-derived dendritic cell (BMDC) data set of  Shalek et al. (2014). Following the filtering procedure established by the authors in the original paper, genes that did not satisfied the condition of being expressed by an amount such that log(TPM + 1). 1 in at least one of the 95 single cells were further discarded, where TPM stands for transcripts per million. This cut-off threshold resulted in 11,640 genes being kept for investigation. The rest of the analyses were conducted in the same way as for the ESC data set.
2010) , we asked whether, at the gene level, there is a link between the centrality of a protein and the amount of transcriptional noise. We study six centrality metrics measured on two types of network data: (1) pathway annotations from the Reactome database  (Fabregat et al. 2016 ) and (  2 ) PPI data from the iRefIndex database. PPI data are typically more complete (5553 genes with gene expression data) but do not include information on functional interactions. The Reactome database is based on published functional evidence, but encompasses less genes (4454 genes for which expression data are available). In addition, graphs representing PPI networks are not oriented while graphs representing Pathway annotations are, implying that distinct statistics can be computed on both types of networks.
Position in the protein network is the main driver of transcriptional noise To jointly assess the effect of network topology, epigenomic factors, Ka/Ks ratio, and gene age, we modeled the patterns of transcriptional noise as a function of multiple predictive factors within the linear model framework. This analysis could be performed on a set of 2794 genes for which values were available jointly for all variables. To avoid colinearity issues because some of these variables are intrinsically correlated, we performed data reduction procedures prior to modeling. For continuous variables, including pathway and PPI network variables, Ka/Ks ratio, and gene age, we conducted a PCA and used as synthetic measures the first eight PCs, explaining together. 80% of the total inertia (Figure  S2A ). The first PC (PC1) of the PCA analysis is associated with pathway centrality measures (degree, hub score, authority score, and transitivity, Figure  S2B ).
Analysis of BMDCs supports the generality of the results We assessed the reproducibility of our results by analyzing an additional single-cell transcriptomics data set of 95 unstimulated BMDCs  (Shalek et al. 2014). After filtering (see Materials and Methods), the data set consisted of 11,640 genes. Using the same normalization procedure as for the ESC data set, we nonetheless report a weak but significant negative correlation between F* and mean expression, even with a degree 5 polynomial regression (20. 0459, P-value , 1. 13e213). This effect is due to cell RFKM values being extremely skewed in this data set, due to the distribution per gene. To assess the impact of the residual correlation with the mean, we computed a value of F* (noted F R *) on a restricted data set where the variance was between one-eighth and eight times the mean (75% of all genes) using a quantile regression on the median instead of a linear regression.
In agreement with results from the ESC analysis, many of the most significantly enriched pathways relate to gene expression, including translation and splicing. Interestingly, the two most significant pathways are "Vesicle-mediated transport" and "Membrane trafficking," two essential pathways for the functioning of dendritic cells. Analyses of network centrality measures also generally showed consistent results with the ESC data set, with more central genes displaying reduced gene expression noise (Figure S6, B-N and Table  S1 ). Quantitative differences consisted of PPI betweenness, as well as pathway closeness and betweenness being highly significantly negatively correlated with F* while they were only weakly significant or nonsignificant with the ESC data set. The only discrepancies that we report between the two data sets relate to pathway-level statistics.
Pathway size appeared to be significantly positively correlated with mean F*, while it was negatively correlated on the ESC data set, yet with a comparatively higher P-value. Similarly, pathway diameter was significantly positively correlated with mean F* in the BMDC data set, while it was not significant with the ESC data. We currently have no hypothesis to explain this particular discrepancy. While these results support the generality of our observations, they also illustrate that, in detail, the fine structure of translational noise may vary in a cell type-specific manner. We fitted linear models as for the ESC data set, with the exception that no epigenomic and 3D genome data were available for this cell type. Data reduction was performed using PCA, with the eight first PCs explaining 81% of the total deviance (Figure  S8A ).
0062), and "Leading strand synthesis" (adjusted P-value = 0. 0062), which all relate to DNA replication. While it is unclear why genes involved in these pathways would display higher technical variance in RNA sequencing, these results differ strikingly from our analyses of single-cell RNA sequencing and therefore suggest that technical variance does not act as a confounding factor in our analyses. Because only three replicates were available in the pooled RNA sequencing data set, we asked whether the resulting estimate of mean and variance in expression is accurate enough to allow proper inference of noise and its correlation with other variables. We conducted a jackknife procedure, where we sampled the original cells from the ESC data set and reestimated F* for each sample. We tested combinations of 3, 5, 10, and 15 cells, with 1000 samples in each case.
Network-based statistics were first tested by  Li et al. (2010)  using PPI data in Yeast. While we are able to extend these results to mouse cells, we show that more detailed annotation, as provided by the Reactome database, can lead to new insights into the selective forces acting on expression noise. Our results suggest that pathways constitute a relevant systemic level of organization, at which selection can act and drive the evolution of SGE at the gene level. This multi-level selection mechanism, we propose, can be explained by selection against noise propagation within networks. It has been experimentally demonstrated that expression noise can be transmitted from one gene to another with which it is interacting  (Pedraza and van Oudenaarden 2005). Large noise at the network level is deleterious  (Barkai and Leibler 1999)  but each gene does not contribute equally to it, thus the strength of selective pressure against noise varies among genes in a given network.
Acknowledgments The authors thank  Rafiq Neme-Garrido ,  Frederic Bartels , and  Estelle Renaud  for fruitful discussions about this work;  Andrew Landgraf  for help with the logistic PCA analysis; and  Diethard Tautz  for comments on an earlier version of this manuscript. They also thank two anonymous reviewers and the editors for their constructive comments. J. Y. D. acknowledges funding from the  Max Planck Society. This work was supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft ), within the  priority program  ( SPP) 1590.
The impact of changes in incoming solar irradiance on stratospheric ozone abundances should be included in climate simulations to aid in capturing the atmospheric response to solar cycle variability. This study presents the first systematic comparison of the representation of the 11-year solar cycle ozone response (SOR) in chemistry-climate models (CCMs) and in pre-calculated ozone databases specified in climate models that do not include chemistry, with a special focus on comparing the recommended protocols for the Coupled Model Intercomparison Project Phase 5 and Phase 6 (CMIP5 and CMIP6). We analyse the SOR in eight CCMs from the Chemistry-Climate Model Initiative (CCMI-1) and compare these with results from three ozone databases for climate models: the Bodeker Scientific ozone database, the SPARC/Atmospheric Chemistry and Climate (AC&C) ozone database for CMIP5 and the SPARC/CCMI ozone database for CMIP6.
The peak amplitude of the annual mean SOR in the tropical upper stratosphere (1-5 hPa) decreases by more than a factor of 2, from around 5 to 2 %, between the CMIP5 and CMIP6 ozone databases. This substantial decrease can be traced to the CMIP5 ozone database being constructed from a regression model fit to satellite and ozonesonde measurements, while the CMIP6 database is constructed from CCM simulations. The SOR in the CMIP6 ozone database therefore implicitly resembles the SOR in the CCMI-1 models. The structure in latitude of the SOR in the CMIP6 ozone.
A. C. Maycock et al. :  Solar signals in stratospheric ozone -Part 2: Global models database and CCMI-1 models is considerably smoother than in the CMIP5 database, which shows unrealistic sharp gradients in the SOR across the middle latitudes owing to the paucity of long-term ozone measurements in polar regions. The SORs in the CMIP6 ozone database and the CCMI-1 models show a seasonal dependence with enhanced meridional gradients at mid-to high latitudes in the winter hemisphere. The CMIP5 ozone database does not account for seasonal variations in the SOR, which is unrealistic. Sensitivity experiments with a global atmospheric model without chemistry (ECHAM6. 3) are performed to assess the atmospheric impacts of changes in the representation of the SOR and solar spectral irradiance (SSI) forcing between CMIP5 and CMIP6.
The larger amplitude of the SOR in the CMIP5 ozone database compared to CMIP6 causes a likely overestimation of the modelled tropical stratospheric temperature response between 11-year solar cycle minimum and maximum by up to 0. 55 K, or around 80 % of the total amplitude. This effect is substantially larger than the change in temperature response due to differences in SSI forcing between CMIP5 and CMIP6. The results emphasize the importance of adequately representing the SOR in global models to capture the impact of the 11-year solar cycle on the atmosphere. Since a number of limitations in the representation of the SOR in the CMIP5 ozone database have been identified, we recommend that CMIP6 models without chemistry use the CMIP6 ozone database and the CMIP6 SSI dataset to better capture the climate impacts of solar variability.
e. with sufficiently high spectral resolution) represented. A small but growing number of CCMs also include the chemical effects of galactic cosmic rays and solar energetic particles, though these effects are not explicitly considered in this study. Conversely, global climate models do not routinely include interactive chemistry and must therefore prescribe a pre-calculated ozone distribution to the radiation scheme, which is usually taken from observations and/or chemical models. Thus, if climate models without chemistry are to capture the full atmospheric response to solar cycle variability, they must prescribe an ozone dataset that includes a representation of the SOR. Understanding and constraining the SOR is a longstanding scientific issue, and numerous studies have analysed its representation in observations (see  Maycock et al.
Thus there has been no detailed comparison of the SOR in a larger set of CCMs since  Austin et al. (2008). Furthermore,  Hood et al. (2015)  compared the few CMIP5 models with older versions of satellite datasets that have since been updated and extended, leading to pronounced changes in their representation of the SOR  (Maycock et al. , 2016; Dhomse et al. , 2016). Hence one goal of this study is to provide an important update by evaluating the SOR in the latest models from the SPARC/IGAC Chemistry-Climate Model Initiative (CCMI-1) and comparing them to some of the recently updated and extended satellite datasets discussed in Part 1. A further motivation for this study is the recent analysis of the climate response to the solar cycle in CMIP5, which included models with and without interactive stratospheric chemistry.
The CMIP5 models showed a large spread (∼ 0. 3-1. 2 K) in the peak amplitude of the tropical stratospheric temperature response between the minimum and maximum phases of the 11-year solar cycle  (Mitchell et al. , 2015). This spread may be due to differences in the prescription of SSI, in the accuracy of model radiative transfer schemes  (Nissen et al. , 2007; Forster et al. , 2011) , and/or in the representation of the SOR. However, the quantitative importance of any one of these factors is unclear. All CMIP5 models were recommended to use the Naval Research Laboratory Spectral Solar Irradiance 1 (NRLSSI-1) dataset  (Wang et al. , 2005). Those without chemistry were further recommended to prescribe ozone from the SPARC/Atmospheric Chemistry and Climate (AC&C; www. igacproject. org, last access: 17 May 2018) ozone database  (Cionni et al.
, 2011 ; hereafter referred to as CMIP5 ozone database). The historical part of this ozone database was largely based on a multiple-regression model fit to satellite and ozonesonde observations (see Sect. 2. 1. 2). It is therefore plausible that differences in the representation of SOR made an important contribution to the spread in atmospheric temperature and dynamical responses to the solar cycle in CMIP5 models; we investigate this hypothesis further in this study. As was the case in CMIP5, CMIP6 will include a mix of models with and without explicit stratospheric chemistry. A new ozone database has been created for CMIP6 models without chemistry (hereafter referred to as CMIP6 ozone database; see https://esgf-node. llnl. gov/ projects/input4mips, last access: 17 May 2018). It is therefore important to document the SOR in the new CMIP6 ozone database and compare it to the previous CMIP5 database, since these fields are routinely employed in climate models and differences may lead to changes in the modelled responses to solar forcing.
In addition to documenting the SOR in the CMIP6 ozone database, this study is published with the ozone coefficients derived from the analysis (https://doi. org/10. 5518/348), so they can be used in other modelling projects (e. g. Jungclaus et al. , 2017). Another factor to consider for modelling solar cycle effects on the atmosphere is the representation of the annual cycle in the SOR  (Soukharev and Hood, 2006; Hood et al. , 2015; Maycock et al. , 2016). Hood et al. (2015)  found that the three CMIP5 CCMs that simulated large horizontal gradients in the SOR in the upper stratosphere in early winter also showed Northern Hemisphere high-latitude dynamical responses over the 11-year solar cycle that compared more closely with reanalysis data. The enhancement of the SOR at high latitudes is related to coupling between chemistry and transport processes for ozone and may play a role in driving the "top-down" mechanism for the solar cycle influence on high-latitude regional surface climate (see e.
g. Gray et al. , 2010). It is therefore also important to compare the representation of the annual cycle in the SOR in current CCMs and in the pre-calculated ozone databases used in climate models. The objectives of this study are therefore as follows: to provide an update to  Austin et al. (2008)  by analysing the SOR in CCMI-1 models, to document the SOR in the new CMIP6 ozone database and compare this to previous pre-calculated ozone databases including CMIP5, to compare the SOR in CCMs and ozone databases with recently updated and extended satellite records, to perform atmospheric model experiments to quantify the impact of differences in the SOR between CMIP5 and CMIP6 on the simulated atmospheric response to the 11-year solar cycle. Collectively these objectives provide a comprehensive assessment of the representation of the SOR in current CCMs and global climate models.
Models and ozone datasets 2. 1. 1 The CCMI-1 models Data are analysed from eight CCMI-1 models downloaded from the British Atmospheric Data Centre  (Hegglin and Lamarque, 2015). The models analysed are CCSRNIES-MIROC3. 2, CESM1(WACCM), CMAM, CNRM-CM5-3, EMAC(L90), LMDz-REPROBUS-CM5 (L39), MRI-ESM1r1 and SOCOL3 (see Table  1 ). These models include the minimum requirements for capturing the SOR (i. e. a prescription of SSI variability in the chemistry scheme). A detailed description of the models is given by  Morgenstern et al. (2017). Data are analysed from the hindcast simulations (REF-C1), which include observed time-varying sea surface temperatures (SSTs) and sea ice, well-mixed greenhouse gases, volcanic aerosols and SSI forcing from NRLSSI-1  (Eyring et al. , 2013b). Thus, in contrast to the coupled atmosphereocean CMIP5 models analysed by  Hood et al.
The CMIP5 ozone database The CMIP5 ozone database consists of monthly mean ozone mixing ratios on 24 pressure levels spanning 1000-1 hPa for the period 1850-2100  (Cionni et al. , 2011). Data are provided on a regular 5 • × 5 • longitude-latitude grid. Ozone values are provided as a 2-D (i. e. zonal mean) field in the stratosphere (at pressures less than 300 hPa) and as a 3-D field in the troposphere, with a blending across the tropopause. The tropospheric part of the database was constructed from CCM simulations. For the stratosphere, the historical portion of the database (1850-2009) was constructed from observations using an MLR model (that includes solar variability as one of the independent variables) fit to Stratospheric Aerosol and Gas Experiment (SAGE) I and II version 6. 2 satellite data and polar ozonesondes following the method of  Randel and Wu (2007).