dataesr/scientific-paragraphs-categorization huggingface dataset:
toxostoma and 192 bases for Q. pubescens, while depth of coverage was obviously variable. A diversity of metrics should be used when assessing the quality of inferred transcriptomes. Although these metrics have not been definitively established, accuracy, completeness, contiguity, chimerism and variant resolution capture all essential elements of transcriptome quality  [45]. These metrics are implemented in Rnnotator  [46] , and were used in the comparative study of de novo and genome-guided assembly by Lu et al. [35]. We have adapted some of these metrics for assessing transcriptome assemblies as we focus on genes and not on transcripts. We define: 1. The number of identified genes (NIG). A gene is considered as identified if at least one contig is assigned to this gene. 2. The Completeness (Cp g ) for a gene g corresponds to the proportion of the length for the longest reference transcript (L g , in number of bases) covered by the whole set of aligned contigs for the gene g (C gj , j = 1.
n g ). The completeness is maximal (equal to 1) when the combined length of the aligned contigs (using ProbCons,  [47] ) matches the longest reference transcript. Cp g ¼ cardfð[ i ¼ 1n g c gi Þ \ Lggcard L g ð1Þ 3. The Contiguity (Ct g ) for a gene g corresponds to the proportion of the longest reference transcript (L g , in number of bases) covered by the longest contig for this gene (C gjmax ). The contiguity is maximal (equal to 1) when there is a perfect match between the longest assigned contig and the longest reference transcript. Ct g ¼ cardfmaxiðC gi Þ \ L g gcard L g ð2Þ These last two metrics are adapted from Martin & Wang  [45]  because we focus on gene identification for non-model organisms and not on resolving the whole set of transcripts (i. e. the complete transcriptome). We used the longest reference transcript for each gene in the reference transcriptome (D.
rerio or V. vinifera) as an upper bound of transcript length for the contigs from RNA-seq. Variation in contiguity and completeness was analyzed using a linear mixedeffect model with divergence, assembly approach (de novo assembly or transcriptome-guided assembly using blastn) and their interaction treated as fixed factors, and random variation attributed to gene. This was done both for simulated and empirical data. For simulated data, we modeled the four divergence levels (0%, 5%, 15%, 30%) as fixed factors. For empirical data, we characterized the distribution of the divergence between each species and its reference transcriptome for each gene and did not model it explicitly. Finally, biological processes associated with each gene was inferred based on annotations from the Protein ANalysis THrough Evolutionary Relationships database (http://pantherdb.
Biological material & empirical data All sampling and experimental protocols were reviewed and approved by local regulatory agencies (ONEMA and the DDT from Alpes-de-Haute-Provence, Hautes-Alpes and Vaucluse; authorization number 2007-573 and 2008-636, following national regulations. Samples of Parachondrostoma toxostoma (3 males) were collected from two rivers: the Durance (southern France) and Ain (eastern France). Eight tissues were sampled (liver, hindgut, midgut, heart, brain, gill, caudal fin, spleen) from one specimen (euthanized by decapitation), only the caudal fin was sampled from the two other males (non-invasive sampling). Samples were ground in liquid nitrogen and total cellular RNA was extracted using the RNeasy Plus Universal kit (Qiagen). The TruSeq Stranded mRNA Library Preparation kit (Illumina Inc.
, USA) was used according to the manufacturer's protocol, with a few modifications (see S2 Protocol for details). Samples were uniquely barcoded for individuals and tissues, and pooled cDNA libraries were sequenced using a Miseq Illumina sequencer with the 2 x 250 paired-end cycle protocol (see S2 Protocol for details). In total, 16,216,379 reads were used to reconstruct the transcriptome of P. toxostoma. All data acquired for this study are available as an SRA archive at https://trace. ncbi. nlm. nih. gov/Traces/sra/sra. cgi?study=SRP091996 (SRX2266500 to SRX2266509). For plant samples, we used five leaves belonging to a single Quercus pubescens specimen from the Oak Observatory at the Observatoire de Haute Provence (France). Total RNA was extracted using the RNeasy Plant mini Kit (Qiagen).
The TruSeq Stranded mRNA Library Preparation kit (Illumina Inc. , USA) was used according to the manufacturer's protocol. Libraries were sequenced using a Next-Seq-500 Illumina sequencer with the 2 x 150 pairedend cycle protocol. In total, 46,881,297 reads were used to reconstruct the transcriptome of Q. pubescens. All data acquired for this study are available as an SRA archive at https://trace. ncbi. nlm. nih. gov/Traces/sra/sra. cgi?run=SRR5410765.
Evaluation of the read assignment pipeline with simulated data Assigning reads to a reference transcriptome was done by finding regions of similarity between the reads and the reference transcriptome through blastn. This was computed on simulated data based on Danio rerio with a range of sequence lengths (100 to 350bp) and simulated divergence (0% to 30% with regard to the original D. rerio sequences) for a 10X uniform coverage. The performance of read assignment was assessed with two metrics. First, the recovery rate, defined as the proportion of reads from a given gene-id and correctly assigned to this gene-id. Second, the specificity rate, defined as the proportion of reads assigned to a gene-id that were simulated from this gene-id relative to the total number of reads assigned to this gene-id.
As before, this effect was substantially reduced under the high divergence scenario. The mixed model for specificity rate (sr) was also highly significant (X 2 = 1,625,516, df = 8, P< 2. 2e-16). Specificity rate displayed median values near to one (S2 Table ), with the random effect being greater (standard deviation estimate = 4. 00) compared to that for rr (standard deviation estimate = 2. 341). Each term of the model appeared significant (S2 Table ). Relations between factors and sr are the same as for rr, with greater read and gene lengths being associated with higher values of sr, and lower sr under higher divergence. The dataset of simulated reads used to evaluate read assignment performance consisted of 31,944 genes of varying length (Fig 3A  and 3B ). In general, the proportion of 'perfect' genes (rr = 1, sr = 1) increased with read length.
Comparison between assembly approaches based on simulations For all classes of simulated reads (range of read length and sequence divergence relative to D. rerio), we reconstructed a de novo transcriptome assembly with Trinity. It was compared to a transcriptome-guided assembly based on the combination of the de novo and D. rerio assemblies as a reference for read assignment. We used three metrics for transcriptomes comparison: i) the number of identified genes (NIG), and for each gene ii) the completeness (cp), defined as the proportion of the length for the longest reference transcript covered by the whole set of aligned contigs for the gene and iii) the contiguity (ct), defined as the proportion of the longest reference transcript covered by the longest contig for this gene. For reads of 100 bases, de novo assembly alone recovered a marginally higher number of genes (NIG) than the transcriptome-guided assembly pipeline based on blastn, irrespective of divergence with the reference transcriptome (Fig 4A ): numbers for de novo ranged from 31,930 to 31,938 and from 31,147 to 31,944 for guided assembly.
Assembling non-model species transcriptomes We compared the efficiency and performance of the de novo assembly and the new combined guided method using empirical data from two non-model species. Datasets consisted of 16,216,379 reads for a fish species (Parachondrosto ma toxostoma) and 46,881,297 reads for a tree (Quercus pubescens). The same metrics (NIG, Cp and Ct) were used to compare the performance and efficiency of the two approaches. For P. toxostoma, a total of 18,519 genes were common to both methods. Nevertheless, we observed a clear difference in the number of detected genes between de novo and guided assemblies: a significantly higher proportion of genes were detected for P. toxostoma (X 2 = 4. 65, P<10 -5 ) when using the guided assembly (20,605 genes) than for the de novo approach alone (20,032 genes).
e. they would be removed/filtered prior to analyses), they could nevertheless contribute to an underestimation of the true levels of expression for donors. As such, we highly recommend identifying such problematic genes as a mean of distinguishing biologically meaningful signals from artefact in the interpretation of differential expression profiles from RNAseq data. To this end, we have made all scripts used for simulating reads from a reference genome available on https://github. com/egeeamu/voskhod; these scripts can be modified for use on any other reference transcriptome used to annotate RNA-seq data, with read length and divergence parameters adjusted to match those under investigation. Our analyses also highlight the difficulties that can arise when working with a reference transcriptome that is highly divergent from one's focal, non-model species-this was facilitated by our in silico generation of reads that in turn enabled development of a matrix of gene categories (given the length of the transcripts fragments) based on recovery and specificity rates (parameters rr and sr).
1%) than for the de novo assembly approach alone (81. 5%). It should also be noted that any other alignment software would be equally as efficient as long as it allows for reliable alignment of queries against distantly related references. Thus, the particular tool used (i. e. blastn) is not the primary message of this study; one should feel free to implement his/her application of choice within the pipeline. As far as performance is concerned, the guided-assembly outperforms de novo assembly alone for both contiguity and completeness. When divergence increases towards 30%, both approaches converge to the same results. This overall greater efficiency and performance of the transcriptome-guided assembly based on blastn allows identifying more genes with a higher degree of confidence in associated assignments.
All scripts used are freely available at https://github. com/egeeamu/ voskhod. All data acquired for this study are available as an SRA archive, for fish sample at https://trace. ncbi. nlm. nih. gov/Traces/sra/sra. cgi? study=SRP091996 (SRX2266500 to SRX2266509) and for plant sample at https://trace. ncbi. nlm. nih. gov/Traces/sra/sra. cgi?run=SRR5410765.
A fresh as-synthesized SAPO-37 sample was placed in a 0. 5 mm quartz capillary and mounted on a flow cell that kept the capillary in controlled atmosphere. The sample was heated up to 1070 K at 2 K min -1 using a hot air blower in flowing synthetic air. Temperature calibration was performed by measuring a sample of high purity Ag and Al 2 O 3. The SR-PXD patterns (exposure time of 10 s) were collected using a Dectris Pilatus 2 M photon counting pixel area detector  [21, 22]  and then integrated with the PyFAI program  [23]. The wavelength, λ = 0. 71210 Å was calibrated from individual runs with LaB 6. The patterns were analyzed by parametric Rietveld refinement  [24]  using TOPAS  [25]  to extract the unit cell parameters, framework atom positions, the isotropic thermal parameters (B-iso) and the template occupancy as a function of temperature.
Acknowledgements The research leading to these results was funded by the project " Catlife: Catalyst transformation and lifetime by in situ techniques and modelling ", P# 233848 " of the  Research Council of Norway. We acknowledge the use of the Norwegian centre for X-ray diffraction and scattering (RECX). The authors thank the project team at the  Swiss-Norwegian Beamline  at the  ESRF, France  for their skillful assistance.
Acknowledgments This study was funded by the  ERC DROUGHT-HEAT  project (contract  617518 ). We thank all modeling groups which participated to the GLACE-CMIP5 experiments, i. e. ,  ARC Center of Excellence for Climate System Science, Sydney ;  European Centre for Medium-Range Weather Forecasts , Reading;  Geophysical Fluid Dynamics, Princeton, Laboratory Institute Pierre-Simon Laplace, Paris; Max Planck Institute for Meteorology, Hamburg ; and  National Center for Atmospheric Research, Boulder. The GLACE-CMIP5 data are hosted at  ETH Zurich  and are available upon request (see http://www. iac. ethz. ch/GLACE-CMIP). We also thank  Dave Lawrence  and  Arndt Meier  for allowing using their model data and  Alexis Berg  for helpful comments on the manuscript. We thank  Martin Hirschi  and  Micah Wilhelm  for help with access to the GLACE-CMIP5 data at ETH Zurich.
Ocean surface forcing For the atmosphere-only reference (REF-C1 and REF-C1SD) and sensitivity (SEN-C1) simulations (Sect. 3), ocean surface forcing (sea surface temperatures, sea ice) need to be imposed  (Eyring et al. , 2013a). Most modelling groups used the Hadley Centre Ice and Sea Surface Temperature (HadISST) dataset  (Rayner et al. , 2003)   S27 ). For the atmosphere-ocean coupled reference (REF-C2) and sensitivity (SEN-C2) simulations (Sect. 3), those models that do not couple to an interactive ocean/sea ice module require climate model fields to be imposed. A substantial variety of different climate model datasets were used for this purpose. In the ULAQ CCM simulations, an ocean surface dataset was used that was derived from a climate model simulation, with mean biases relative to HadISST removed.
CCMI-1 simulations In this section, we briefly describe the motivation and some technical details regarding the experiments conducted for CCMI-1. Eyring et al. (2013a)  have given more details. The specific forcings imposed are discussed briefly in Sect. 4. -REF-C1: this experiment is analogous to the REF-B1 experiment of CCMVal-2. Using state-of-knowledge historic forcings and observed sea surface conditions, the models simulate the recent past. The models are free-running. -REF-C1SD: this is similar to REF-C1 but the models are nudged towards reanalysis datasets, and correspondingly the simulations only cover 1980-2010. ("SD" stands for specified dynamics. ) Through a comparison to the REF-C1 simulations, the influence on composition of dynamical biases and differences in variability between the reanalysis and the models can be assessed.
0, approaching 3. 8 ppmv in 2100. All other GHGs and forcings follow RCP 6. 0. The simulation covers 2005-2100. -SEN-C2-fN2O: this experiment is identical to REF-C2 but the nitrous oxide surface-mixing ratio is fixed to its 1960 value  (Hegglin et al. , 2016). -SEN-C1-fEmis/SEN-C1SD-fEmis: in these experiments, for species with explicit surface emissions such as nitrogen oxides (NO x ), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and aerosol precursors, 1960 emissions are prescribed throughout, allowing the role of meteorological variability in influencing tropospheric composition to be established. -SEN-C2-fEmis: this is similar to REF-C2 but with surface and aircraft emissions fixed to their respective 1960 levels. -SEN-C1-Emis/SEN-C1SD-Emis: in these experiments the recommended emission dataset is replaced with an emission dataset of the modellers' choice, to assess the impact of alternative emissions on tropospheric composition.
-SEN-C2-RCP: this is the same as REF-C2, but with the GHG scenario changed to either RCP 2. 6, 4. 5, or RCP 8. 5. The simulations start in 2000. This means these scenarios differ in their assumptions on GHGs and surface emissions (but not regarding the ODSs). They also require adequate sea surface and sea ice conditions corresponding to the variant climate scenarios, for the atmosphere-only models. -SEN-C2-GeoMIP: these simulations link CCMI-1 with the Geoengineering Model Intercomparison Project (GeoMIP;  Tilmes, 2015a). They are designed to test the impact of proposed efforts to actively manage the Earth's radiation budget to offset the impact of increasing GHGs using sulfur injections. -SEN-C1-SSI: this is the same as REF-C1 but using a solar forcing dataset with increased UV intensity.
Sea surface temperature and sea ice For the REF-C1 and SEN-C1 experiments, sea surface conditions need to be prescribed. For this, as for CCMVal-2, the HadISST climatology  (Rayner et al. , 2003)  is recommended (Sect. 2. 20). Variance correction for this monthly mean climatology is recommended following the AMIP II method (http://www-pcmdi. llnl. gov/projects/amip/ AMIP2EXPDSN/BCS/bcsintro. php). Between 1960 and 2010 there is some warming in the HadISST dataset over various areas of the ocean, but also widespread cooling of the Southern Ocean (Fig. 3 ). Arctic annual minimum sea ice extent reduces considerably in this period, whereas Antarctic sea ice expands slightly (Fig. 4 ). HadISST is heavily based on satellite observations which are non-existent for the early part of the record, meaning that trends derived over this period have to be viewed with caution.
Stratospheric aerosol loading Figure  5  shows the aerosol surface area density at 22 km as recommended by CCMI-1 and imposed by most models  (Arfeuille et al. , 2013). In comparison to the dataset used for CCMVal-2  (Morgenstern et al. , 2010) , the most signif- Figure  5. Zonal-mean aerosol surface area density (µm 2 cm -3 ) at 22 km. The discrete events are due to volcanic eruptions, superimposed on a much smaller non-volcanic background. icant difference is the insertion of a major volcanic injection of aerosol into the stratosphere in 1974/1975, due to the Fuego (Guatemala) eruption. This had been ignored before. Furthermore, note the increase in aerosol density during the last decade attributed to a series of small volcanic eruptions  (Vernier et al. , 2011).
Solar forcing The recommended solar forcing dataset contains daily solar irradiance, ionization rates by solar protons, and the geomagnetic activity index A p (http://solarisheppa. geomar. de/ccmi). Spectrally resolved solar irradiances for 1960-2010 were calculated with the empirical NRLSSI model  (Lean et al. , 2005). The spectral grid width (1 nm bins from 0 to 750 nm, 5 nm bins from 750 nm to 5 µm, 10 nm bins from 5 to 10 µm, 50 nm bins from 10 to 100 µm) allows for easy calculations of the spectral SSI for any specific model spectral grids, which should be applied to calculate the shortwave heating rates in the radiation module and the photolysis in the chemistry scheme. The ionization rates caused by solar protons for the same time period are calculated using the Jackman et al. (  2009 ) approach based on the proton flux measurements by several instruments onboard the GOES satellites.
The recommended coefficients for the conversion of the ionization rates to in situ HO x and NO x production intensity are also given by  Jackman et al. (2009). For the models extending only to the mesopause, a time-varying geomagnetic activity index A p is provided as a proxy for the thermospheric NO x influx, which is used to include indirect energetic particle effects using an approach similar to that defined by  Baumgaertner et al. (2009). These datasets are recommended to be applied in REF-C1 simulations covering 1960-2010. For SEN-C1-SSI simulations, the SATIRE SSI dataset  (Krivova et al. , 2006)  should be used instead of the NRLSSI data described above. This SSI dataset exhibits larger UV variability, which can have consequences not only for atmospheric heating but also for ozone chemistry  (Ermolli et al.
Availability of simulations Tables 4-6 summarize the available simulations at the time of writing this paper. As recommended, a large majority of models have performed the reference simulations. A subset has produced REF-C1SD, reflecting that not all models have the capability to be nudged to meteorological fields. The sensitivity simulations, both SEN-C1 and SEN-C2, are less consistently covered, ranging from 1 to 15 simulations. Most of the model output can be accessed via the British Atmospheric Data Center (BADC), which hosts the CCMI-1 data archive. Some model institutions provide their data however directly via their local Earth System Grid Federation nodes (see the list provided on http://www. met. reading. ac. uk/ccmi/?page_id=251). CESM1 CAM4-chem and CESM1 WACCM data are provided via https://www.
Code and data availability Readers should contact the model PIs to enquire about conditions of code availability for the 20 models documented in this paper (Table  1 ). No model output was used in this paper. For CCMI-1 model data, see Sect. 5. Forcing data used in this paper are described and can be downloaded at http://blogs. reading. ac. uk/ccmi/reference-simulations-and-forcings. Appendix A: Individual model descriptions A1 ACCESS-CCM and NIWA-UKCA NIWA-UKCA is a coupled atmosphere-ocean CCM, based on the HadGEM3-AO model (revision 2) coupled to the NIWA-UKCA gas-phase chemistry scheme. It is identical to ACCESS-CCM, except that ACCESS-CCM uses prescribed sea surface conditions in all simulations. Relative to the UMUKCA models used for CCMVal-2, both models now feature a medium-complexity tropospheric hydrocarbon oxidation scheme, including the Mainz Isoprene Mechanism  (Pöschl et al.
B2 CCSRNIES MIROC3. 2 HadISST1 data were used for REF-C1 and REF-C1SD simulations. Chemical reactions important in the troposphere are not included, but the stratospheric chemistry scheme is just used in the troposphere. Solar radiation at wavelengths shorter than 177. 5 nm is not considered except for Lyman-α. Atmospheric ionization by solar protons is not included.
B3 CMAM The ACCMIP historical database of emissions  (Lamarque et al. , 2010)  was used for the REF-C1 and REF-C1SD simulations up to the year 2000, with the RCP8. 5 emissions used for the following years. It was also used up to 2000 for the REF-C2 and associated scenario simulations. Emissions at intermediate years were linearly interpolated from the years given in the database. An additional emission of CO of 250 Tg (CO) year -1 was included to account for CO from isoprene oxidation, with the emissions distributed following the monthly emissions of isoprene from  Guenther et al. (1995). No variance correction was applied to the specified SSTs.
nesi. org. nz). The SOCOL team acknowledges support from the  Swiss National Science Foundation  under grant agreement  CRSII2_147659  ( FUPSOL II ). CCSRNIES's research was supported by the  Environment Research and Technology Development Fund  ( 2-1303 ) of the  Ministry of the Environment, Japan , and computations were performed on NEC-SX9/A(ECO) computers at the CGER,  NIES. Wuhu Feng (NCAS)  provided support for the TOMCAT simulations. Neal Butchart ,  Steven C. Hardiman , and  Fiona M. O'Connor  and the development of HadGEM3-ES were supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). Neal Butchart and  Steven C. Hardiman  also acknowledge additional support from the European Project  603557-STRATOCLIM  under the  FP7-ENV. 2013. 6. 1-2programme.
Fiona M. O'Connor  acknowledges additional support from the  Horizon 2020 European Union 's  Framework Programme for Research and Innovation CRESCENDO project  under grant agreement no. 641816. Slimane Bekki  acknowledges support from the European Project  603557-STRATOCLIM  under the  FP7-ENV. 2013. 6. 1-2programme  and from the  Centre National d'Etudes Spatiales (CNES, France)  within the  SOLSPEC project. Kane Stone  and  Robyn Schofield  acknowledge funding from the  Australian Government 's  Australian Antarctic science grant program  ( FoRCES 4012 ), the  Australian Research Council's Centre of Excellence for Climate System Science  ( CE110001028 ), the  Commonwealth Department of the Environment  (grant  2011/16853 ), and computational support from  National computational infrastructure INCMAS  project  q90.