dataesr/scientific-paragraphs-categorization huggingface dataset:
These analyses complement earlier studies assessing the SOR in previous generations of CCMs (e. g. Austin et al. , 2008; SPARC CCMVal, 2010). In a novel step, we also analyse and compare the SORs in three pre-calculated ozone databases that are prescribed in climate models without interactive chemistry: the Bodeker et al. (  2013 ) Tier 1. 4 ozone database and the CMIP5 ozone database  (Cionni et al. , 2011) , which are both based on regression models fit to ozone measurements, and the CMIP6 ozone database, which is created from simulations from two CCMs (CESM1(WACCM) and CMAM). The CCMI-1 models simulate an annual mean SOR with a peak amplitude of 1-2 % in the upper stratosphere (∼ 3-5 hPa). This is more than a factor of 2 smaller than the SOR found in SAGE II v6. 2 mixing ratio data and is more consistent with results from SAGE II v7.
0 and SBUV satellite datasets  (Maycock et al. , 2016; Dhomse et al. , 2016)  and with previous CCM studies (e. g. Austin et al. , 2008; Sekiyama et al. , 2006; Lee and Smith, 2003; Egorova et al. , 2014; Dhomse et al. , 2011 Dhomse et al. , , 2016;; Hood et al. , 2015; SPARC CCMVal, 2010). Many of the CCMI-1 models show larger fractional monthly SORs in the high latitudes during winter and spring, which are likely to be strongly coupled to dynamical processes such as the formation and evolution of the polar vortex. The spread in the best-estimate SOR across the CCMI-1 models is around 4 times larger in the tropical lower stratosphere than in the middle and upper stratosphere, and the statistical uncertainties in the SOR are also substantially larger in the lower stratosphere. There are strong differences in the representations of the SOR in the pre-calculated ozone databases.
The peak amplitude of the SOR in the tropics in the CMIP5 and Bodeker ozone databases is substantially larger (5 %) than in the CMIP6 database (1. 5-2 %). This is because the former databases are derived from observations that include SAGE II v6. 2 mixing ratios, which exhibit a larger SOR than found in other satellite ozone datasets (see Part 1). In contrast, the CMIP6 ozone database was constructed from CCM simulations, and thus its SOR generally resembles the CCMI-1 models in terms of both its broad structure and magnitude and the fact that it exhibits some variation over the annual cycle. Note that the amplitude of the SOR in the CMIP6 ozone database may have been slightly larger if both of the constituent CCMs had used the CMIP6 SSI forcing rather than the NRLSSI-1 forcing from CCMI-1  (Matthes et al.
, 2017). The CMIP5 database exhibits spurious sharp horizontal gradients in the SOR across the extratropics, which were partly alleviated by a simple poleward extrapolation in the Extended CMIP5 ozone database, albeit with considerable uncertainties in the detailed spatial structure. Furthermore, the CMIP5 and Extended CMIP5 ozone databases include a fixed SOR throughout the year, which is unrealistic. Sensitivity experiments were performed using a comprehensive global atmospheric model without chemistry (ECHAM6. 3) to test how the changes in the recommended SOR and SSI between CMIP5 and CMIP6 affect the simulated annual mean temperature response over the 11-year solar cycle. The larger amplitude of the SOR in the CMIP5 ozone database compared to CMIP6 causes a larger tropical stratospheric temperature response between 11-year solar cycle minimum and maximum by up to 0.
Parts 1 and 2 of this study have shown that uncertainties remain in understanding the SOR, which present a challenge for including these effects in model simulations. However, given the inclusion of variations in the SOR over the annual cycle, as well as the greater consistency of the amplitude of the SOR with CCM results, CMIP6 models without chemistry are encouraged to use the recommended CMIP6 ozone database. The CMIP6 solar-ozone coefficients are published with this paper (https://doi. org/10. 5518/348) and have already been used in other modelling projects such as PMIP4  (Jungclaus et al. , 2017). Nevertheless, whatever approach is employed, all CMIP6 modelling groups are encouraged to document the representation of the SOR and SSI in their simulations to facilitate future analysis of solar-climate impacts.
Acknowledgements. Amanda C. Maycock  acknowledges funding from an  AXA   Postdoctoral Fellowship , the  ERC ACCI  Grant project no. 267760  and a  NERC   Independent Research Fellowship  ( NE/M018199/1 ). Amanda C. Maycock  also acknowledges funding from the  COST Action   ES1005  " Towards a more complete assessment of the impact of solar variability on the Earth's climate" (TOSCA) for a Short-Term Scientific Mission  to GEOMAR in September 2014, which initiated this work. We are grateful to support for scientific meetings from the  WCRP/SPARC  SOLARIS-HEPPA activity. Parts of the work at the  GEOMAR Helmholtz Centre for Ocean Research Kiel  were performed within the Helmholtz University Young Investigators Group NATHAN, funded by the  Helmholtz Association and GEOMAR. Hiderahu Akiyoshi acknowledges the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, Japan  ( 2-1709 ), and  NECSX9/A(ECO ) computers at CGER,  NIES.
The  National Center for Atmospheric Research (NCAR)  is sponsored by the  US National Science Foundation (NSF). WACCM is a component of the Community Earth System Model (CESM), which is supported by  NSF  and the  Office of Science of the US Department of Energy. The EMAC simulations have been performed at the  German Climate Computing Centre (DKRZ)  through support from the  Bundesministerium für Bildung und Forschung (BMBF). DKRZ  and its scientific steering committee are gratefully acknowledged for providing the HPC and data archiving resources for the consortial project  ESCiMo (Earth System Chemistry integrated Modelling ). The SOCOL team acknowledges support from the  Swiss National Science Foundation  under grant agreement  CRSII2_147659  ( FUPSOL II ). Rémi Thiéblemont acknowledges his funding by the LABEX L- IPSL  project (grant  ANR-10-LABX-18-01 ).
Slimane Bekki  has been partially supported by the European project  StratoClim  ( 603557  under programme  FP7-ENV. 2013. 6. 1-2 ). We acknowledge the modelling groups for making their simulations available for this analysis, the joint  WCRP SPARC/IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organizing and coordinating the model data analysis activity and the  British Atmospheric Data Centre (BADC)  for collecting and archiving the CCMI model output. We are grateful to  Greg Bodeker  ( Bodeker Scientific ) and  Birgit Hassler  ( NOAA ) for providing the combined vertical ozone profile database from Bodeker et al. (2013). Edited by: Peter Hess Reviewed by:  Joanna D. Haigh  and one anonymous referee.
Data availability. The CCMI dataset and the CMIP5, CMIP6 and Bodeker ozone databases are accessible from the sources stated in the paper. The ECHAM6. 3 model simulations are available from the authors on request. The solar-ozone response coefficients extracted from the CMIP6 ozone dataset can be accessed from https://doi. org/10. 5518/348 (Maycock, 2018). The Supplement related to this article is available online at https://doi. org/10. 5194/acp-18-11323-2018supplement.
487; p-value < 0. 001) significantly reduced the bumblebee wing size. Regarding the thermic stress, one-way ANOVA showed significant differences between the different treatments (Fig. 1E ; F 2,147 = 127; p-value < 0. 001). The males emerging from the micro-colonies reared at 33 °C were significantly smaller than those from the control micro-colonies (Tukey's HSD test; F 1,49 = 60. 65; p-value < 0. 001). By contrast, the cold exposure (i. e. thermic stress at 21 °C) had no significant impact (Tukey's HSD test; F 1,49 = 0. 2; p-value > 0. 05). Shape -The PCA displayed large overlap of stressed and control groups, except for the inbreeding stress (i. e. slight overlap between inbred and outbred bumblebees; Fig. 2 ). The perMANOVA on the PCA axes revealed significant differences between each treatment inside each sub-dataset (Table  1 ).
These mated queens were used to produce inbred generation colonies. During two months, the mated queens overwintered following the rearing method of  72. Fifteen out of 29 mated queens initiated colonies. For this inbreeding stress, we used workers (n = 50) to perform the subsequent morphometric analyses instead of haploid males because the inbred generation produced only few haploid males. Workers from the parent (outbred) generation were used as control (n = 50). Our total dataset contains 650 individuals. The right and left forewings of each specimen were removed, placed on a slide and photographed using an Olympus SZH10 microscope with an AF-S NIKKOR 18-105 millimetres (Shinjuku, Japan) and GWH10X-CD oculars coupled with a Nikon D200 camera (Shinjuku, Japan). Wing size and shape analysis.
Acknowledgements This work was partly supported by the  "Fonds de la Recherche Scientifique -FNRS " and the  Fonds "Wetenschappelijk Onderzoek -Vlaanderen (FWO)"  under  EOS  Project  n°30947854  and the  FRFC  project  n°2461310. MG was supported by a grant from the  Belgian Fonds pour la Recherche dans l'Industrie et l' Agriculture (FRIA)  and MV was  FNRS grant fellowship  " Chargé de recherches ".
Data production Photographs reproduced in Fig. 2B ,D were taken using a Canon EOS 5D Mark III equipped with a Canon MP-E 65 macro lens (Canon, Tokyo, Japan). A set of photographs necessary to produce two RTI files (overview, and details of the left hind wing base) were obtained with the same photographic equipment driven by a hand-made Portable Light Dome, and then computed using RTIBuilder software (see  Béthoux et al. 2016). We provide an online Dryad dataset, cited below as "  Cui et al. (2018) ", in which the RTI files (to be opened using the freely accessible software RTIViewer) are available. The photograph reproduced as Fig. 2E  was extracted from the RTI file 'PIN 5026 75 -detail LHW base' (bookmark 2).
These equations have been solved numerically, scanning the delay between the XFEL and NIR pulses from -750 to 1500 fs and optimizing the parameters such that we obtain the best least-squares fit to the experimental data for the Xe þ and Xe 2þ yields. The Rosenbrock evolutionary algorithm for nonlinear leastsquares optimization as implemented in the DAKOTA package  [37]  has been used by imposing a set of constraints on the parameters extracted from physical considerations and classical estimations for the cluster expansion speed (see Supplemental Material  [20] ).
ACKNOWLEDGMENTS We are grateful to the late  Makoto Yao  for his invaluable contributions to the present work. The experiments were performed at SPring-8 Angstrom Compact free electron LAser (SACLA) with the approval of JASRI and the program review committee (No. 2015B8057 ). This study was supported by the X-ray  Free Electron Laser Utilization Research Project  and the X-ray  Free Electron Laser Priority Strategy Program  of the  Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) , by the  Proposal Program of SACLA Experimental Instruments of RIKEN , by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  Grants No. JP21244042 , No. JP23241033 , No. JP15K17487 , and No. JP16K05016 , and by the  Institute of Multidisciplinary Research for Advanced Materials (IMRAM) project.
A. I. K. thanks DFG  for financial support. M. S. and G. K. gratefully acknowledge financial support by  BMBF  (Grant No. 05K16RF3 ) and the  DFG Research Unit 1789  Grant No. 203306641. Y. K. was partially supported by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. K. Kooser  acknowledges the financial support provided by the  Estonian Research Council  (Grant No. PUT735 ) and from the  Väisälä Foundation of the Finnish Academy of Science and Letters.
SYSTEM MODELING In the present section, two middle moving magnets are considered as illustrated in the equivalent mechanical and electrical model (Figure  1 ). The proposed harvesting devise is modelled using two forced duffing equations. So, the governing equation of the designed harvester can be written as: 𝑚𝑥̈𝑗 + 𝑐𝑥̇𝑗 + 𝐹 𝑗 𝑚𝑒 (𝑥) + 𝐹 𝑗 𝑚𝑔 (𝑥) = -𝑚𝑌 ̈ (1) where  = 1,2 and  =   +   with   and   stand for the mechanical and electrical damping factors respectively. and    are respectively the mechanical and magnetic forces for each moving magnet. It is assumed that the two middle moving magnets have the same mass, size, and the identical mechanical and electrical damping. The mechanical force, describes the behaviour of the used elastic spring, was modelled by Ansys software in the static model with considering the geometrical nonlinearity.
magna. This species is not only one of the major genomic model organisms of the genus  (Miner et al. 2012 , GenBank accession number: LRGB00000000. 1), but also for studies on sex differentiation under ESD and other sex-related traits, including local adaptation in male production, evolution of genetic sex determination (which occurs in some genotypes of D. magna, not investigated here), and uniparental reproduction  (Kato et al. 2011; Galimov, Walser & Haag 2011; Svendsen et al. 2015; Reisser et al. 2016; Roulin et al. 2013 ). Here we present an analysis of sex-biased gene expression in D. magna, based on RNA-sequencing of males and females of four different genotypes (males and females of the same genotypes are members of the same clone). The four genotypes were used as biological replicates, that is, the expression of individual genes was classified as "sex-biased" only if the bias was consistent among all genotypes tested (i.
Mapping and counting Filtered reads were mapped to the publicly available D. magna genome assembly (NCBI database; Assembly name: daphmag2. 4; GenBank assembly accession: GCA_001632505. 1, Bioprojects accession: PRJNA298946), consisting of 28'801 scaffolds, 38'559 contigs and a total sequence length of 129'543'483 bp, as well as a genome annotation with 26'646 genes, using the RNA-Seq aligner STAR  (Dobin et al. 2013 ) using default settings. The raw counts (number of mapped reads per transcript per sample) were obtained with the software featureCounts  (Liao, Smyth & Shi 2014) , a fast tool for counting mapped paired-end reads. Counts were summarized at the gene level using the annotation file.
Differential gene expression The analysis of differential gene expression was carried out with DESeq2 (version 1. 10. 1, Love, Huber & Anders 2014) implemented in R (R Core Team 2017). Raw read counts were used as input data, and the subsequent analyses used the normalizations of read counts as performed by DESeq2, which is currently considered best practice for the analysis of RNA-sequencing data  (Conesa et al. 2016; Schurch et al. 2016). The males vs. females comparison was carried out with a two-factor design taking into account clone identity and sex. All p-values were adjusted for multiple testing with the Benjamini-Hochberg method, as implemented in DESeq2. Genes were considered differentially expressed (DE) if they had an adjusted p-value , 0. 05 (False discovery rate, FDR = 5%). The degree of sex bias was determined by the fold-change (abbreviated FC) difference between the treatments.
DE genes were classified into five groups: ,twofold,. twofold, 2-to fivefold, 5-to 10-fold and. 10-fold difference in expression (absolute changes rather than log-transformed changes). To obtain a broader overview of the expression profiles of the significantly DE genes, we performed a hierarchical clustering of the sex-DE genes, as implemented in DESeq2. The normalization in DESeq2 does not account for transcript length, hence it is possible that some differential exon usage (that could ultimately result in the existence of different isoforms) could be mistakenly interpreted as differential expression. However, because different transcripts of most genes differ only weakly to moderately in size  (Chern et al. 2006) , normalization by transcript length (which has been criticizes for other reasons,  Dillies et al.
2013)  would not strongly affect the inferred levels of fold-change in expression levels. Therefore, inferences of differential expression should typically be robust, at least in the class of genes with a greater than twofold change. Comparison of sex-biased gene expression With D. galeata and D. pulex Protein sequences of D. magna (v2. 4 GenBank: LRGB00000000), D. pulex (version JGI060905: http://wfleabase. org/release1/current_release/ fasta/) and D. galeata (http://server7. wfleabase. org/genome/Daphnia_ galeata/) reference genomes were used as input in the software Ortho-Finder  (Emms & Kelly 2015) , a fast method for inferring orthologous groups from protein sequences with enhanced accuracy. These correspond to 26'646, 30'940, and 33'555 protein sequences, respectively. We also used another software, OrthoMCL  (Li, Stoeckert & Roos 2003)  for inferring orthologs.
Because the results were qualitatively and quantitatively similar, we present only the results of OrthoFinder here. For further analysis, we retained only those genes that were identified by OrthoFinder as single copy, one-to-one orthologs in all three species. We then compared this list with our list of sex-DE genes (adjusted p , 0. 05), as well as the lists of sex-DE genes in D. galeata and D. pulex  (Colbourne et al. 2011; Huylmans et al. 2016). The R package VennDiagram  (Chen & Boutros 2011)  was used for visualization of sex-DE genes in the three species. We then focused on the core subset of 675 orthologous genes that were found to be consistently sex-DE in all three species and used BLAST2GO (version 4. 1. 9, (Conesa et al. 2016)  to perform a functional annotation. The protein sequences of the D.
magna genes in question were annotated using the NCBI nr database, allowing for 20 output alignments per query sequence with an e-value threshold of 0. 001. The mapping and annotation steps implemented in BLAST2GO were run with default settings. Additionally, InterPro IDs from InterProScan were merged to the annotation to improve accuracy. Graphical representation of GO categories belonging to the 675 core-genes was obtained using the R package metacoder  (Foster, Sharpton & Grünwald 2017). The BLAST2GO output files were searched for the terms "sex determination" (GO accession number 0007530), "sex differentiation" (0007548), "male sex differentiation" (0046661), "female gonad development" (0008585), "male gonad development" (0008584), "female sex determination" (0030237), "male sex determination" (0030238), and "female sex differentiation" (0046660).
In addition, a universal list of genes involved in sex determination and sex differentiation pathways was established by searching the entire Genbank database for genes associated with the terms "sex determination/differentiation". After removal of redundancies, we obtained a list of 541 genes (hereafter referred to as the "NCBI list of genes"). We compared the annotations of our 675 core-genes (as determined by the BLAST2GO analysis) with this list to identify any shared genes. Finally, we performed an enrichment analysis of GO terms for the 675 core genes with consistent sexbiased expression in all three species, taking the 26'646 genes of D. magna as the reference GO composition. This was done using the GOatools Python script (https://github. com/tanghaibao/goatools), which performs Fisher's exact tests for differences in frequencies of GO-terms between the two lists (with Bonferroni correction).
Data availability Table  S1  contains the initial numbers of raw reads and the percentages retained during the filtering steps. All genes found differentially expressed between sexes in D. magna are provided in Table  S2. Table  S3  contains data of sex-biased gene expression in the three Daphnia species investigated and their ortholog groups from OrthoFinder. Table  S4  contains results of the annotated genes with sex-biased expression in all three species that were found in the NCBI list of genes involved in sex determination/differentiation. Raw Illumina sequence reads from this study have been deposited in the NCBI SRA database under the BioProject accession number PRJNA437676, and SRA accession number SRP134480. Supplemental material available at Figshare: https:// doi. org/10. 25387/g3.
Comparisons of sex-biased gene expression among the three Daphnia species The software OrthoFinder identified 7453 single copy, one-to-one orthologs present in all three species (Table  2 ). Among these, 5707 (76. 5%) were sex-DE in at least one species, and 707 genes (9. 5%) were sex-DE in all three species (Figure  2 , Table  2 ). Only 32 of these 707 genes (4. 5%) showed a different direction of bias in one of the species. The remaining 675 genes were biased in the same direction in all three species, and we therefore refer to these genes as the core-set of sex-DE genes in Daphnia. Among the genes of the core-set, 75% were female-biased (Figure  2 ), and genes with a strong expressionbias between sexes were more likely to be included in this core-set (Figure  3 ). Genes that showed significant sex-biased expression in only two out of the three species showed very similar patterns: a high proportion showed consistent bias (i.
ACKNOWLEDGMENTS We thank the  Zoo of Moscow  and  N. I. Skuratov  for sampling permits, and  David Frey  for help with maintenance of indoors cultures. We thank the  Department of Biosystem Science and Engineering of the ETH Zurich , in particular  C. Beisel  and  I. Nissen  for Illumina sequencing, and we gratefully acknowledge support by  M-P. Dubois ,  Figure 4 Composition  and hierarchical organization of GO terms associated to the 675 genes with consistent sex-biased gene expression in all three species. n_obs: number of genes in the given GO category.
Ci-GFM1 retains all five cysteine residues that form intermolecular disulfide bonds essential for multimeric structures of GFM and VWF  18, 19  (Fig. 3e , Supplementary Fig. 5e ). Ci-GFM1 also harbors a CBD. We thus predict that multimeric Ci-GFM1 lines the chitinous wall of the intestinal barrier, though this needs to be tested in future. Collectively, the proteomic data support the view that the intestinal physical barrier is immunologically fortified with matrix components (Fig. 4a ). Intestinal chitinous membranes are prevalent in chordates. The finding of chitin-based barrier immunity in the gut of the tunicate Ciona raises the question of how it is related to intestinal immune mechanisms of other animal groups (Fig. 4b ). In many invertebrate groups, a membranous matrix surrounds food residues in the midgut, persists through the intestine and often accumulates in fecal pellets  21.
"Yes" or "No" indicate the presence or absence of the items listed on the right, respectively. Previously, invertebrate PMs and mammalian mucus layers were not believed to share common descent. New data on tunicates and ray-finned fish, however, fill this gap and suggest a transition from a chitin-based ancestral condition to a mucin-based derived state (top). A tree diagram of animal phylogeny (bottom) helps to infer events that account for the transition (black circles on branches). Mucus colonization in mammalian guts appears to be a novel type of animal-microbe association that was established upon loss of chitin Formvar-coated copper grids, stained with aqueous uranyl acetate and lead citrate and examined under a JEM 2000 EX II (Jeol, Japan) operated at 200 kV with a CCD camera (Keen view, Olympus soft image solutions, Germany).