dataesr/scientific-paragraphs-categorization huggingface dataset:
I G U R E 2 Population structure in the Lake Victoria Basin (LVB). Analyses are based on chromosome 3 to avoid segregating inversions on other chromosomes, unless otherwise noted. (a) ADMIXTURE-inferred ancestry of individuals in LVB. Results based on analysis of LVB and Ag1000G merged dataset. Analysis is restricted to Anopheles gambiae s. s. Clustering shown for k = 6 clusters, which minimizes crossvalidation error, and k = 9 clusters, the lowest k for which island individuals have the majority of their ancestry assigned to an island-specific cluster. (b) Results of the clustering analysis with k = 9 clusters for LVB individuals, split by sampling locality. (c) Plot of first two components of PCA of Lake Victoria Basin individuals showing locality of origin. Mainland individuals are colored red, while island individuals are blue, and point shape indicates sampling locality.
F I G U R E 2 Population structure in the Lake Victoria Basin (LVB). Analyses are based on chromosome 3 to avoid segregating inversions on other chromosomes, unless otherwise noted. (a) ADMIXTURE-inferred ancestry of individuals in LVB. Results based on analysis of LVB and Ag1000G merged dataset. Analysis is restricted to Anopheles gambiae s. s. Clustering shown for k = 6 clusters, which minimizes crossvalidation error, and k = 9 clusters, the lowest k for which island individuals have the majority of their ancestry assigned to an island-specific cluster. (b) Results of the clustering analysis with k = 9 clusters for LVB individuals, split by sampling locality. (c) Plot of first two components of PCA of Lake Victoria Basin individuals showing locality of origin. Mainland individuals are colored red, while island individuals are blue, and point shape indicates sampling locality.
Indeed, analysis of single particle trajectories (SPTs)  [35] [36] [37] [38] [39]  of a tagged locus revealed a deviation from classical diffusion as measured by the anomalous exponent. We recall briefly that the MSD (Eq. (  7 )) is computed from the positions r i (t) of all monomers i = 1, …, N T. In that case, the MSD, which is an average over realization, behaves for small time t, as a power law hðr i ðs þ tÞ À r i ðsÞÞ 2 i / t α i : ð10Þ It is still unclear how the value of the anomalous exponent α i relates to the local chromatin environment, although it reflects some of its statistical properties, such as the local cross-link interaction between loci  14, 35. Thus we decided to explore here how the distribution of cross-links extracted from EP of the Hi-C data could influence the anomalous exponents.
Acknowledgements V. P. and D. N. acknowledge funding from the  Fondation pour la Recherche Médicale  ( AJE20140630069 ) and the  Agence Nationale de la Recherche  ( ANR-14-ACHN-0009-01  and  ANR-16-TERC-0027-01 ). D. H. 's research is supported by  FMR  team ( DEQ20160334882 ).
Data availability The datasets analyzed during the current study are available in the spatial organisation of the X inactivation center repository (www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE35721) for the 5C encounter matrices; and in the multi-scale 3D genome rewiring during mouse neural development repository (www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE96107) for the Hi-C data.
We compared RNA-seq profiles in both stem cell compartments and found that none of the down-regulated genes were in common in accordance with the distinct identity of the two stem cell populations (fig. S3C ). In contrast, we found approximately one-fourth of the up-regulated genes (255 genes, ≈25%) in common between ISC and HFSC (fig. S3D ). This group of genes was enriched by mainly homeobox-containing TFs involved in regulating general developmental processes (fig. S3E ). Several ZIC (Zinc finger protein of the cerebellum) TFs (Zic1, Zic4, and Zic5; fig. S3F ), as well as Sox7, Hoxa10, and Hoxb13 (fig. S3G ), were present within this list. Together, these data suggest that PRC1 plays a common general role in distinct stem cell compartments to prevent activation of non-lineage-specific, homeobox-containing TFs.
5B ). Among them, Satb1, Krtdap, Calm5, Itgam, and Gdpd2, together with Ascl2, some of the known epidermal markers that are highly activated (Fig. 5, A, C,  and D ). We further scanned the Ascl2 promoter with LASAGNA-Search 2. 0 (32) for putative TFs responsible for its activation and found several binding motifs that are recognized by the family of ZIC TFs (Fig. 5E ). Note that different members of this TF family are also directly bound and derepressed in Ring1a/b dKO HFSCs (fig. S3F ), establishing a positive feedback loop driving epidermal identity. Together, these results support a model in which PRC1 directly suppresses an epidermal-specific transcriptional program via suppression of master regulators of lineage specification, thus contributing to preserve HF identity.
Acknowledgments: We thank  M. Vidal  and  H. Koseki  for the Ring1a -/-Ring1b fl/fl mice. We thank all members of the DP group for helpful discussions. Funding: The work in the DP laboratory was supported by grants from the  Italian Association for Cancer Research (AIRC) , the  Italian Ministry of Health , the  EMBO  young investigator, and the  European Research Council (ERC). S. P. was supported by fellowship from the  AIRC. F. C. is supported by a grant from the  AIRC  (grant no. 20344 ) and by  IEO-CCM Foundation (FIEO). Author contributions: S. P. , A. D. , A. R. , D. M. , L. B. , M. Z. , and F. C. performed experimental work. D. F. -P and C. M. B. performed the bioinformatics analysis. A. S. produced anti-BMI1 antibody. S. P. , D. F. -P, C. M. B. , F. C. , and D. P. analyzed the data.
The injection volume was 10 µL. Mass detection was carried out in a negative electrospray ionization (ESI) mode. The settings of the mass spectrometer were as follows: spray voltage 3. 2 kV, capillary and desolvation temperature were 350 and 400 °C respectively, maximum injection time 200 ms. Nitrogen was used as sheath gas (50 a. u. ) and auxiliary gas (15 a. u. ). The automatic gain control (AGC) was set at 106 and resolution at 70,000 from m/z 100 to 700. MS analyses were performed by targeted selected ion monitoring (tSIM) mode with 0. 5 m/z isolation window for SIM and ± 10 ppm for inclusion tolerances. tSIM MS scans the targeted masses in different time segments selected based on the retention times of the analytes. Data acquisition was performed using Thermo Scientific Xcalibur software.
Data processing and statistical analysis MS data were processed using TraceFinder v3. 2 (Ther-moFisher), with a tolerance of 5 ppm to extract exact masses. In 13 C-ILEs, isotopologue distributions were quantified from mass fractions after correction for the presence of all naturally occurring isotopes and isotopic purity of the tracer (99%) using IsoCor v2. 0. 4  (Millard et al. 2019 ), which ensures accurate correction of high-resolution isotopic data. IsoCor also calculates the mean 13 C-enrichment (E) from the isotopologue distributions using the following formula: where M i is the proportion of the isotopologue with i 13 C atoms for a metabolite containing n carbon atoms. IsoCor is freely available at https ://githu b. com/MetaS ys-LISBP /IsoCo r and its documentation at https ://isoco r.
05) despite significant changes in pool sizes. Integration of metabolomics and isotopic datasets may provide quantitative information on the biosynthetic flux of isoprenoid precursors. The labeling dynamics of a given metabolite reflects its turnover rate, which is roughly equivalent to the ratio of the metabolite pool size and the flux through that metabolite pool. The strong increase of pools in S037 and S023 strains compared to the WT strain are expected to slow down the transient 13 C-incorporation  (Buescher et al. 2015). However, the 13 C-enrichment profiles and turnover rates were very similar in the three strains, which suggest that the flux increases roughly proportionally with the pools. The relative increase in GGPP biosynthesis in strains S037 and S023 compared to the wild-type strain could thus be estimated from the corresponding change in GGPP concentration.
Assuming a constant turnover of GGPP, its biosynthetic flux increased by a factor 9. 0 ± 2. 6 in strain S037 and 7. 0 ± 1. 6 in S023 compared to the wildtype strain (p < 0. 01), which support the relevance of the proposed strategy to enhance phytoene production. Inferring absolute flux values from these data would require more sophisticated mathematical models of isoprenoids biosynthesis, such as non-stationary 13 C-flux models. Still, these results already demonstrate the applicability of the proposed workflow to infer flux information on this pathway in yeast. Concentrations and isotopic labeling of the prenyl pyrophosphate intermediates could also complement other biochemical datasets (e. g. proteomics or enzyme activities) to infer quantitative information on the control exerted by each reaction step on the pathway flux, and thereby drive further strain optimization strategies.
The paper is structured as follows. Section 2 provides a description of the various chemistry schemes implemented in IFS. Section 3 provides an overview of the observational datasets used for model evaluation, while in Sect. 4 a basic assessment of model differences for tracers playing a key role in tropospheric ozone is provided. Section 5 contains the evaluation against observations of a full year simulation with the three atmospheric chemistry versions of IFS with a focus on tropospheric chemistry. The paper is concluded with a summary and an outlook in Sect. 6, where the recent model evolution in the various versions is also briefly described.
Key differences in chemistry modules An overview of the most important differences in the three chemistry modules described above is given in Table  1. First, there are large differences in the choices made to compile the tropospheric chemistry mechanism. IFS(MOZART) describes the degradation of organic carbon types C1, C2, C3, C4, C5, C7 and C10, together with lumped aromatics, while IFS(CB05BASCOE) only describes explicit degradation up to C3, with the same reactions as present in IFS(MOZART). Instead, emissions and degradation of higher volatile organic compounds (VOCs) in IFS(CB05BASCOE) are lumped to a few tracers. Furthermore, the parameterisation of isoprene and terpene degradation is simpler in IFS(CB05BASCOE) than in IFS(MOZART). Aromatics are currently not described in IFS(CB05BASCOE), while they are accounted for with simple approaches in IFS(MOZART).
For the total anthropogenic VOC emissions the changes between the year 1990 and 2011 are of the order of 14 %, following the Emissions Database for Global Atmospheric Research (EDGARv4. 3. 2 database). Nevertheless, the evaluations presented here are all sampling background locations or outflow regions and are hence only partly affected by such changes in anthropogenic emissions. Also, the variability as well as measurement uncertainties present in the observations are larger than 14 %, implying that we can still consider these observations representative. Finally, these data summaries are useful for providing a picture of the global distributions of NMHCs and nitrogencontaining trace gases.
Near-surface CO and ozone-sondes In situ observations for monthly mean CO for the year 2011 are used to evaluate monthly mean modelled surface CO fields. Observational data are taken from the World Data Centre for Greenhouse Gases (WDCGG), the data repository and archive for greenhouse and related gases of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme. The uncertainty of the CO observations is estimated to be of the order of 1-3 ppm  (Novelli et al. , 2003). Tropospheric ozone was evaluated using sonde measurement data available from the World Ozone and Ultraviolet Radiation Data Center (WOUDC; http://woudc. org, last access: 25 April 2019), further expanded with observations  from the Network for the Detection of Atmospheric Composition Change (NDACC) network.
Therefore, in our study we only use daytime MOPITT TIR observations. The standard deviation of the error in individual pixels for the MOPITT v7 TIR product evaluated against NOAA flask measurements is reported as 0. 13 × 10 18 mol cm -2  (Deeter et al. , 2017) , i. e. of the order of 10 % of the observation value. Daily mean model CO columns have been gridded to a 1 • × 1 • spatial resolution, and for our analysis we applied the MOPITT averaging kernels to the logarithm of the mixing ratio profiles, following  Deeter et al. (2012). OMI retrievals of tropospheric NO 2 were taken from the QA4ECV dataset  (Boersma et al. , 2017). For this evaluation the 3-hourly model output of NO 2 was interpolated in time to   Emmons et al. (2000). Each field campaign is represented by a different colour. Further information on the campaigns is found in  Emmons et al.
This holds for all versions for CO, O 3 , CH 2 O, NO 2 and HNO 3 , while model versions have more difficulties with CH 3 OOH. For SO 2 CBA is the only model version to deliver a weighted bias that is larger than -1. For C 2 H 4 and C 2 H 6 none of the versions are able to match the observations to an acceptable degree. Remarkably, C 2 H 4 is the only trace gas for which values for the weighted R 2 are lower than the normal R 2 values, suggesting fundamental problems representing this trace gas properly in any of the chemistry versions. The inability of the model versions to reproduce the observed magnitude of C 2 H 6 and the vertical distribution of C 2 H 4 , as indicated by the relatively low correlation with all aircraft measurements included in the database, requires a more detailed analysis.
57), giving overall confidence in the model ability to describe ozone variability. Figure  6  shows annually averaged model biases and root mean square errors (RMSEs) for various latitude bands and for altitude ranges 900-700, 700-500 and 500-300 hPa against WOUDC sondes. In this evaluation we also present data from the CAMS interim reanalysis (CAMSiRA) for the year 2011 to put the current model evaluation into perspective. This summary analysis shows averaged biases within ±10 ppbv, which is also in line with the O 3 bias statistics against the aircraft climatology. At lower altitudes the model biases are mostly equal to or better than those from CAMSiRA, while above 500 hPa CAMSiRA delivers mostly smaller biases thanks to the assimilation of satellite ozone observations. The RMSE shows a larger spread in the lower troposphere of the NH, while at higher altitudes above 500 hPa the overall magnitude of the RMSE for the three chemistry versions converges to values ranging from 10 to www.
Still, the diversity of chemistry versions will be useful to provide a quantification of uncertainties in key CAMS products due to the chemistry module compared to other sources of uncertainties. Code and data availability. The source codes of the chemistry modules are integrated into ECWMF's IFS code, which is only available subject to a licence agreement with ECMWF. The IFS code without modules for Research Atmospheric Science Data Center assimilation and chemistry can be obtained for educational and academic purposes as part of the openIFS release (https://confluence. ecmwf. int/display/OIFS, ECMF, 2019). Detailed documentation of the IFS code is available from https://www. ecmwf. int/en/forecasts/documentation-and-support/ changes-ecmwf-model/ifs-documentation (ECMF, 2019). The CB05 chemistry module of IFS was originally developed in the TM5 chemistry transport model.
Readers interested in the TM5 code can contact the TM5 developers (http://tm5. sourceforge. net, TM5-community, 2019). The BASCOE stratospheric chemistry module can be freely obtained from the BASCOE developers (http://bascoe. oma. be, BIRA-IASB, 2019). The MOCAGE chemistry module of IFS is developed at Météo-France on the basis of the MOCAGE chemistry transport model (http://www. umr-cnrm. fr/spip. php?article128, CNRM, 2019). The MOZART code can be obtained by contacting the developers via https://www2. acom. ucar. edu/gcm/mozart (NCAR, 2019). The MOZART and CB05BASCOE chemistry schemes are also freely available through the  Sander et al. (2019)  publication. The model simulation datasets used in this work are archived on the ECMWF archiving system (MARS) under the experiment IDs listed in Table  3.
Readers with no access to this system can freely obtain these datasets from the corresponding author upon request. Author contributions. VH designed the study, contributed to the evaluations against sondes and satellite retrievals, and wrote large parts of the paper. VH, SC, YC and JF developed the IFS(CB05BASCOE) chemistry module; VM, JA, TD, JG, BJ and SP developed the IFS(MOCAGE) chemistry module; IB and GB contributed to the development of the IFS(MOZART) chemistry module; and AP and VK performed the evaluation against aircraft observations and contributed to the writing. vations. MOPITT data were obtained from the NASA Langley Research Atmospheric Science Data Center. We acknowledge the free use of tropospheric NO 2 column data from the OMI sensor from the QA4ECV project. Review statement.
Acknowledgements. We acknowledge funding from the  Copernicus Atmosphere Monitoring Service (CAMS) , which is funded by the  European Union 's  Copernicus Programme. We are grateful to the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC)  for providing ozone-sonde observations. We thank the  Global Atmospheric Watch  programme for the provision of CO surface obserwww. geosci-model-dev. net/12/1725/2019 /Geosci. Model Dev. , 12,  1725-1752 , 2019.
1 Aircraft measurements Aircraft measurements of trace gas composition from a database produced by Emmons et al. (2000) were used for the evaluation of distributions of collocated monthly mean mod- elled fields. Although these measurements cover only limited time periods, they provide valuable information about the vertical distribution of the analysed trace gases. The database is formed with data from a number of aircraft campaigns that took place during 1990-2001 which are gridded onto global maps, forming data composites of chemical species important for tropospheric ozone photochemistry. These are used to create observation-based climatologies (Emmons et al. , 2000). Here we use measurements of ozone, CO, CH 2 O, C 2 H 6 , C 2 H 4 , methyl hydroperoxide (CH 3 OOH), NO 2 , ni- tric acid (HNO 3.
5; 5), (7; 2) and (6; 6)] or [(2; 5), (4; 2) and (4; 4)]; • Markov chain parameters: Finally, we take M max = 50 and K max = 5 the maximum values of M and K for which we will compute the estimators. Q * =   The simulation codes are available in MATLAB at https://www. normalesup. org/ ~llehericy/HMM_order_simfiles/.
3. 1), all at the same time. However, the minimization of the (non-convex) empirical contrast is a time-consuming step, especially for large samples and large models. Choosing the right method is thus a question of computational power and amount of available data. For small datasets where one wants to get accurate results, the least squares method is best. Conversely, on large datasets and large models, the spectral method is a good choice in order to obtain many estimators in a reasonable amount of time.
Acknowledgment We would like to thank Elisabeth Gassiat for her precious advice and Yohann de Castro for his codes which were at the root of our numerical experiments. which means that 1 2 ĝ -g * 2 2 ≤ 3 2 g * K,M -g * 2 2 + 2pen(n, M, K) + 2A x n and finally ĝ -g * 2 2 ≤4 inf K≤n, M ≤n { g * K,M -g * 2 2 + pen(n, M, K)} + 4A x n which is the expected inequality.
In silico analyses Contigs belonging to the UbiA superfamily in RNA-seq libraries of fig latexes were screened by TBLASTN searching using BIOEDIT software (http://www. mbio. ncsu. edu/BioEdit/bioedit. html). Contigs homologous to FcPT1 were obtained from the RNA-seq library of Ficus religiosa leaves available in OneKP (https://site s. google. com/a/ualberta. ca/onekp/) (Johnson et al. , 2012; Matasci et al. , 2014; Wickett et al. , 2014; Xie et al. , 2014). BIOEDIT software was also used to calculate amino acid identities among polypeptide sequences. The transmembrane regions and the transit peptides of FcPTs were predicted by TMHMM SERVER v. 2. 0 (http://www. cbs. dtu. dk/services/TMHMM/) and CHLOROP (http://www. cbs. dtu. dk/services/ChloroP/), respectively. Phylogenetic trees were constructed based on CLUSTALW multiple alignments using MEGA7 (http://www.
S3a ). UDT-candidate1 contains a partial PT sequence lacking the 5 0 -terminal region; however, it was complemented based on a homologous contig (Fr2001904) identified in an RNA-seq of F. religiosa, another Ficus species accumulating FCs  (Singh et al. , 2011) , in the OneKP database (Fig. S4 ). Another contig (Fr2007013) identified in the F. religiosa RNA-seq allowed the extension of the 3 0 -UTR sequence of UDT-candidate1 (Fig. S4 ). Using the combined sequence information from these three contigs (Fig. S4 ), two full CDSs were isolated from fig mRNA by RT-PCR-based cloning, and these CDSs were named FcPT1a and b. UDT-candidates2 and 3 were found to encode identical PT genes harbouring a single silent mismatch in their CDSs and to have highly homologous UTR sequences (Fig. S3 ). Based on the sequence of UDT-candidate2, which was longer than that of UDT-candidate3, two additional CDSs were cloned by the same RT-PCR approach and named FcPT2a and b.
Expression profile of FcPT1 Furanocoumarin contents vary among fig organs  (Oliveira et al. , 2009) , but no reports have described the distribution of FC molecules through latexes from fig fruits, petioles and trunks (Fig. S2 ), from which comparable RNA-seq datasets were constructed  (Kitajima et al. , 2018). We therefore measured the total FC contents in these three latex preparations by quantification of psoralen and bergapten, considering the large majority of FC derivatives in fig  (Oliveira et al. , 2009). The total FC contents in the petiole and trunk latex preparations were 24-and 35-fold higher, respectively, than those in fig fruit  latex (Figs 4, S9 ), suggesting large variations in FC production by these tissues. Subsequent qRT-PCR analysis revealed similar relative levels of FcPT1 expression in these latex preparations  (Figs 4, 5) , which suggested that this gene is involved in FC biosynthesis.
6b ). The high RPKM subgroup contained seven contigs annotated as U6DT, which encode FcPT1a/b, partial CDSs almost identical (> 99%) to FcPT1a/b, and partial CDSs of FcPT1a/b with yet-to-be spliced introns (highlighted in red in Fig. 6 ; Table  S3a ). These in silico analyses provided further evidence that FcPT1a/b are the most promising candidates for UDT. The remaining six contigs with low RPKM values were mapped to two close genomic loci (accession ID: BDEM01000105. 1), one of which contains a full gene structure. However, its gene product encoded by 36524_c3_g2_i2 is rather divergent (< 55% identity) from FcPT1 or 2, which is the similar divergence from Moraceae PTs for other phenolic groups, that is, Morus alba isoliquiritigenin dimethylallyltransferase (MaIDT) and Cudrania tricuspidata IDT (CtIDT), both specific to flavonoids, and M.
FcPT1a and b correspond to nucleotides 9576-12 597 and 9576-12 602, respectively, in a fig genome scaffold (accession ID: BDEM01000717. 1). Both FcPT2a and b correspond to nucleotides 198 283-200 428 in another scaffold (accession ID: BDEM01000270. 1). These findings indicate that each pair of variants represents allelic pairs.