dataesr/scientific-paragraphs-categorization huggingface dataset:
We chose a maximum of 10 random sites for computational efficiency reasons. No sample sites were allocated to locations within strata where no species occurred. We obtained 322 sample sites for amphibians, 253 for squamata, 326 for mammals, and 326 for birds, respectively. For each sample point, we recorded the presence and absence of each species for further analyses. Phylogenetic turnover and macroecological characteristics. To calculate phylogenetic turnover (phylo-ß) between neighboring strata, we grouped all sampled sites per stratum and calculated mean phylo-ß from all pairwise dissimilarity calculations (Simpsons pairwise dissimilarity index) of sample sites between the two strata using the betapart package  61  in R. To remove the effect of geographic distance on phylo-ß we performed a generalized linear model with logit-transformed phylo-ß as dependent variable and geographic distance (linear and quadratic terms) between sampled sites as explanatory variable.
Ellipse areas were set to encompass 95% of each species' data points when mapping their ranges onto these two niche axes and calculations were performed using the car package  64  in R. Our calculations may underestimate the niche size of species that also occur outside Europe. Interpolation into environmental space. We interpolated phylo-ß (that is the residual values that represent the phylo-ß fraction that is independent of varying geographic land distance between sample sites), species richness, relative phylogenetic diversity, and niche size into environmental space defined by the temperature and moisture gradients. To do so, we rasterized each variable in the raster package  43  using bilinear interpolation. Variable importance for explaining phylo-ß. We used standardized linear regression models to explain phylo-ß based on (standardized) species richness, relative phylogenetic diversity, and niche size (linear and quadratic terms) and to quantify the variable importance in the phylo-ß models for each taxon group separately.
Acknowledgements This study was supported by the  Swiss National Science Foundation  (Grant  31003A_149508/1 ). We would like to thank  Rafael O Wüest  for helpful discussions. We thank the following persons for silhouette images:  Pedro de Siracusa  (amphibians), Ghedo, and  T. Michael Keesey  (squamates) under CC BY-SA 3. 0 (creativecommons. org/licenses/by-sa/3. 0/), Shyamal (birds)  under  CC BY 3. 0  (creativecommons. org/licenses/by/3. 0/) and  Steven Traver  (mammals) under  CC0 1. 0.
Data availability The source data underlying Figs 2-4 and Supplementary Figs 1-11 and 13 are provided as a Source Data file, which is available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. sr7818c [10. 5061/dryad. sr7818c] 67. The original tetrapod phylogenies are available from: https://doi. org/10. 5061/dryad. 11609 [10. 5061/dryad. 11609] 45 and the phylogenies for Supplementary Figs 10-11 are available from the Center for Biodiversity [http://www. biodiversitycenter. org/ttol]. A reporting summary for this Article is available as a Supplementary Information file.
, 2012; Ding et al. , 2017]. However, these inversion problems may be impacted by the non-linearity of atmospheric chemistry, for which variational methods are more suitable than KFs by design. Here, we present the Bayesian variational inverse system PYVAR-CHIMERE, that has reached a good level of maturity (robustness of calculations, clarity, portability, modularity of the code) and that is adapted to reactive species such as CO and NO2. This adaptation for reactive species takes advantage of the previous developments for the quantification of fluxes of long-lived GHG species such as CO2  [Broquet et al. , 2011] and CH4 [Pison et al. , 2018 ] at the regional to the local scales, paving the way for monitoring anthropogenic emissions at high spatial resolutions. The PYVAR-CHIMERE inverse modeling system is based on the Bayesian variational assimilation code PYVAR  [Chevallier et al.
2005 ] and on the regional state-of-the-art CTM CHIMERE, dedicated to the study of regional atmospheric pollution events  [Menut et al. , 2013 , Mailler et al. , 2017]  possessing its adjoint code (e. g. , for global models: IMAGES  [Stavrakou and Muller, 2006] , TM5  [Krol et al. , 2008] ,  GELKA [Belikov et al. , 2016]  and LMDz  [Chevallier et al. , 2005; Pison et al. , 2009]  ; for limited-area models: CMAQ  [Hakami et al. , 2007] , EURAD-IM  [Elbern et al. , 2007] , RAMS/CTM-4DVAR [Yumimoto et  Uno, 2006] ). Originally, the sequential adjoint code was developed at LMD  [Menut et al. , 2000; Menut et al. , 2003 ]. Then, it has been further developed and parallelized at LISA  [Pison et al. , 2007]  and LSCE (see Section 3). The principle of variational atmospheric inversion and the configuration of PYVAR-CHIMERE are described in Section 2 and in Section 3, respectively.
PYVAR adapted to CHIMERE The PYVAR-CHIMERE inverse modeling system is based on the Bayesian variational assimilation code PYVAR  [Chevallier et al. 2005 ] and on the previous inversion system based on CHIMERE  [Pison et al. , 2007]. PYVAR is an ensemble of Python scripts, which deals with preparing the vectors and the matrices for the inversion, driving the required Fortran codes of the transport model and computes the minimization of the cost function to solve the inversion. Previously used for global inversions with the LMDz model (e. g. ,  Pison et al. , 2009; Chevallier et al. , 2010; Fortems-Cheiney et al. , 2011; Yin et al. , 2015; Locatelli et al. , 2015; Zheng et al. , 2019) , PYVAR has been adapted to the state-of-the-art CHIMERE regional CTM. Including the elements of the previous inversion system built around CHIMERE (coded in Fortran90)  [Pison et al.
Development of the adjoint of CHIMERE To compute the sensitivity of simulated atmospheric concentrations to corrections to the fluxes, the adjoint of CHIMERE has been developed. Originally, the sequential adjoint was coded at LISA [  Menut et al. , 2000; Menut et al. , 2003; Pison et al. , 2007]. Then, it has been parallelized and further developed at LSCE and LISA, together with its tangent-linear (TL) code. The adjoint has been coded by hand line by line, following the principles formulated by Talagrand  [1997]. It contains exactly the same processes as the CHIMERE forward model. PYVAR-CHIMERE is currently operational for the full module of gaseous chemistry. As a compromise between the robustness of the method for reactive species, the time required coding the adjoint and the computational cost with a full chemical scheme, the aerosols modules of CHIMERE have not been included in the adjoint of CHIMERE yet and are therefore not available in PYVAR-CHIMERE.
It should also be noted that other minor changes against the CHIMERE 2013 version  [Menut et al. , 2013]  have been implemented, including: • For the geometry, the possibility of polar domains and the use of the coordinates of the corners of the cells instead of only the centers • For the transport, the non-uniform Van Leer transport scheme on the horizontal, • For chemistry, various switches have been added to avoid going into the chemistry, deposition and wet deposition routines when no species requires them (e. g. no chemistry for methane at a regional scale). It should be noted that the development and maintenance of the adjoint means that the version used is necessarily one or two versions behind the distributed CHIMERE version (http://www. lmd. polytechnique. fr/chimere/). As an example, Figure  2  presents a simplified scheme of how PYVAR scripts are used to drive this version of CHIMERE for forward simulations and inversions using satellite observations.
Numerical language The PYVAR code is in Python 2. 7, the CHIMERE CTM is coded in Fortran90. The CTM requires several numerical tools, compilers and libraries. The CTM was developed and tested using the software versions as described in Cartesian division of the main geographical domain into several sub-domains, each one being processed by a worker process. To configure the parallel sub-domains, the user has to specify two parameters in the model parameter file: the number of sub-domains for the zonal and meridional directions. The total number of CPUs used is therefore the product of these two numbers plus one for the master process.
Observations y The observational constraints for CO emissions come from the MOPITT instrument. It has been flown onboard the NASA EOS-Terra satellite, on a low sun-synchronous orbit that crosses the equator at 10:30 and 22:30 LST. The spatial resolution of its observations is about 22x22 km 2 at nadir. It has been operated nearly continuously since March 2000. MOPITT CO products are available in three variants: thermal-infrared TIR only, near-infrared NIR only and the multispectral TIR-NIR product, all containing total columns and retrieved profiles (expressed on a ten-level grid from the surface to 100 hPa). We choose to constrain CO emissions with the MOPITT surface product for our illustration. Among the different MOPITTv8 products, we choose to work with the multispectral MOPITTv8-NIR-TIR one, as it provides the highest number of observations, with a good evaluation against in situ data from NOAA stations  [Deeter et al.
Covariance matrices B and R The covariance matrix B of the prior errors is defined as diagonal (i. e. only variances are taken into account). Even though total emissions are well known, large uncertainties still affect such emission inventories  [Kuenen et al. 2014 ] at the pixel scale. Consequently, the error standard deviations assigned to the CO and NO2 prior emissions in the covariance matrix B are set at 100%. The variance of the individual observation errors in R is defined as the quadratic sum of the measurement error reported in the MOPITT and the OMI data sets, and of the CTM errors (including transport errors and representativity errors) set to 20% of the retrieval values. The representativity errors could have been reduced with the choice of a finer CTM resolution (e. g. , with a resolution closer to the size of the satellite pixel).
Acknowledgements We acknowledge  L. Menut  and  C. Schmechtig  for their contributions to the development work on the adjoint code of CHIMERE and its parallelization. We acknowledge the  TNO team (H. A. Denier van der Gon ,  J. Kuenen ,  S. Dellaert ,  S. Jonkers ,  A. Visschedijk , et al. ) for providing NOx and CO emissions over Europe. We also acknowledge the free use of tropospheric NO2 column data from the OMI sensor from http://temis. nl/qa4ecv/no2. htmlandthe free use of CO surface concentrations from the MOPITT sensor from ftp://l5ftl01. larc. nasa. gov/MOPITT/. For this study,  A. Fortems-Cheiney  was funded by the  FrenchSpace Agency-Centre National d'Etudes Spatiales CNES  and by the  H2020   VERIFY  project, funded by the  European Commission   Horizon 2020 research and innovation programme , under agreement number  776810.
Code and Data Availability OMI QA4ECV NO2 product can be found here: http://temis. nl/qa4ecv/no2. html. MOPITTv8-NIR-TIR CO product can be found here: ftp://l5ftl01. larc. nasa. gov/MOPITT/ The CHIMERE code is available here: www. lmd. polytechnique. fr/chimere/. The associated documentation of PYVAR-CHIMERE is available on the website https://pyvar. lsce. ipsl. fr/doku. php/3chimere:headpage. The documentation includes a whole description of PYVAR-CHIMERE and several tutorials on how to run a first PYVAR-CHIMERE simulation or how to run an inversion.
Peptides were enriched, separated and analyzed using a 6520 Accurate-Mass Q-TOF LC/MS equipped with an HPLC-chip cube interface (Agilent Technologies, Massy, France). The fragmentation data were interpreted using the Mass Hunter software (version B. 03. 01, Agilent Technologies). For protein identification, MS/MS peak lists were extracted, converted into mzdata. xml format files and compared with the protein database (NCBInr-Viridiplantae) using the MASCOT Daemon search engine (version 2. 1. 3; Matrix Science, London, United Kingdom). The searches were performed with no fixed modification and with variable modifications for oxidation of methionine, and with a maximum of two missed cleavage sites. MS/MS spectra were searched with a mass tolerance of 20 ppm for precursor ions and 0. 6 Da for MS/MS fragments.
Only peptides matching an individual ion score > 48 were considered. Proteins with two or more unique peptides matching the protein sequence were considered as a positive identification. The assigned protein of best match is provided alongside the UniProt or NCBI/GenBank accession number. Score, queries matched, peptide matches, different peptide matches, experimental mass and theoretical mass are also presented. In addition, other proteins identified in B. napus with the same peptides by MASCOT are presented. Protein sequences were matched against sequences of Brassicacea proteins using the NCBI BLAST Protein Database (algorithm blastp) and the best BLAST results are presented with the name of the protein, the organism, the UniProt or NCBI/GenBank accession number and the percentage of sequence identity.
ACKNOWLEDGMENTS We would like to thank  Dr. Nathalie Nési ,  INRA  ( UMR 1349 Institut de Génétique, Environnement et Protection des Plantes, INRA, Agrocampus Ouest, Université de Rennes ) who is the leader of this  ANR-program ,  Dr. Jacques Trouverie  and  Dr. Philippe Etienne  for their useful comments on this study, the  P2M2 -Metabolic and Metabolomic profiling platform (INRA, UMR 1349 Institut de Génétique, Environnement et Protection des Plantes ,  INRA ,  Agrocampus Ouest ,  Université de Rennes) for phytohormone analysis,  Dr. Laurent Coquet  for protein identification by  ESI   LC-MS/MS  on the proteomic platform at the  University of Rouen Normandie (Pissaro platform, IFR MP 23, Rouen, France ) and finally the  UMR EVA technical  staff for their skilful assistance. We are most grateful to  PLATIN' (Plateau d'Isotopie de Normandie) core facility  for all element and isotope analysis used in this study.
FUNDING This work was funded by the  French National Research Agency  ( ANR-11-BTBR-004   RAPSODYN -Investments for the Future: Optimisation of the RAPeSeed Oil content and Yield under low Nitrogen  input) and by a Ph. D. grant to MP from the  French Ministry of Research and the Ph. D. Doctoral School (EDNBISE, Ecole Doctorale Normande Biologie Intégrative, Santé, Environnement). Further financial support was provided by the  ERA-IB  project " PRODuCE ", the  Max Planck Society ,  COST CM1004  and the  University of Oxford.
Binding parameters to T. brucei PKAR(199-499) expressed in E. coli were determined by isothermal titration calorimetry (ITC). The power differential (DP) between the reference and sample cells upon injection was measured as a function of time (inset). The main plot presents the total heat exchange per mole of injectant (integrated peak areas from inset) as a function of the molar ratio of ligand to protein. One out of three independent replicates is shown. b Data for 7-CN-7-C-Ino as in a; for number of independent replicates see Table  1. The EC 50 and K D values given in a and b are rounded values from Table  1. c Thermodynamic signature (ΔG in blue, ΔH in green, and -TΔS in red) compiled from ITC measurements (mean ± SD of n = 3 independent replicates) in a and b. Source data are provided as a Source Data file bovine RIα) is one of the key replacements in kinetoplastid PKAR, but the mammalian RIα mutant of that residue retained cAMP activation at an only 5-fold decreased EC 50  54.
The frequency of individual subsets of PKA motifs (upregulated) was compared to the human PKA site motif frequency retrieved from the PhosphoSitePlus database (https://www. phosphosite. org). d Abundance changes of metacaspase 4 (MCA4) and PKAC1/2 in WT or e pkar KO cells after treatment with 4 µM 7-CN-7-C-Ino (Ino) or Toyo in a 24-h time course. Western blot signals were normalized to the loading control PFR-A/C; untreated WT cells were set to 1. Source data to a and c-e are provided as a Source Data file affinity. Of note, the residue corresponding to A336 in hRIα is V444 in TcPKAR and is part of the hydrophobic pocket formed between beta barrel and αD helix. This pocket accommodates the substituent at the C7 position of our activators and may determine their selectivity. The structural alignment between TcPKAR and bovine PKARIα shows conservation of the general polypeptide chain folding and of the putative salt bridge and capping residues linked to the extensively studied allosteric activation mechanism in mammalian PKARIα  1, 30.
3g ). GraphPad Prism 7. 0 was used for EC 50 calculation by non-linear regression analysis using an equation for a sigmoidal dose-response curve with variable slope. The same software was used for visualization of all graphs and bar charts. Inhibitors and nucleoside analogues. PKI(5-24) and myr-PKI(14-22) were obtained from Biomol. CpdA  19  (now renamed as NPD-001)  74  was synthesized by Geert-Jan Sterk, Mercachem. Dipyridamole was obtained from Sigma-Aldrich, toyocamycin and sangivamycin from Berry & Associates. All other nucleoside and cyclic nucleotide analogues were obtained from Biolog GmbH, Bremen. 7-CN-7-C-Ino was synthesized according to Hinshaw et al. 75  with 125 mg (429. 1 µmol) Toyo as starting material. By variation to the original protocol, the raw product was purified by reversed phase medium pressure liquid chromatography (MPLC).
GraphPad Prism 7. 0 was used for EC 50 calculation by non-linear regression analysis using an equation for a sigmoidal dose-response curve with variable slope. The same software was used for visualization of all graphs and bar charts. Binding studies by isothermal titration calorimetry. N-terminally truncated T. brucei PKAR (aa 199-499) was cloned into pETDuet-1 (Novagene) with an Nterminal His 6 -tag and expressed in E. coli Rosetta (DE3). The bacteria were grown in Luria-Bertani (LB) medium to OD 600 of ∼0. 4 at 37 °C, followed by overnight induction with 0. 4 mM IPTG at 20 °C. Cells were harvested by centrifugation and lysed in a French Press. Protein purification was done by Ni-NTA affinity chromatography, followed by elution in 50 mM HEPES, pH 7. 5, 50 mM NaCl, and 250 mM imidazole. Eluted protein (∼7 mg ml -1 ) was dialyzed overnight in 50 mM HEPES, pH 7.
1 M Tris, pH 8. 5 for 5 min at 95 °C (according to the protocol of Humphrey et al. 78  ). Samples were sonicated using a Bioruptor (Diagenode) (high power, two cycles of 10 min each, 30 s on/off). Protein concentration was determined by BCA protein assay and samples were adjusted to equal concentrations. Sample preparation and mass spectrometry were exactly carried out as described by Humphrey et al. 78. MaxQuant 1. 5. 2. 8  79  was used to identify proteins and quantify by LFQ with the following parameters: Database, TriTrypDB-39_TbruceiTREU927_AnnotatedProteins; MS tol, 10 ppm; MS/MS tol, 0. 5 Da; Peptide FDR, 0. 1; Protein FDR, 0. 01 Min. peptide Length, 5; Variable modifications, Oxidation (M), Phosphorylation (STY); Fixed modifications, Carbamidomethyl (C); Peptides for protein quantitation, razor and unique; Min.
peptides, 1; Min. ratio count, 2. For proteomic analysis, identified proteins were considered as statistically significant with FDR ≤ 0. 05 and s 0 = 1 (two-sided Student's T-test adjusted for multiple comparisons by Benjamini-Hochberg correction, Perseus  80  ). Phosphopeptide analysis was carried out in Perseus as suggested by Humphrey et al. 78. The mass spectrometry phosphoproteomics data have been deposited to the ProteomeExchange Consortium via the PRIDE partner repository (https:// www. ebi. ac. uk/pride/archive) with the dataset identifier PXD012245. GO enrichment analysis was performed in TriTrypDB with default settings and visualized in Revigo (http://revigo. irb. hr). The motif discovery tool MoMo (using the motif-x algorithm) implemented in the MEME suite (http://meme-suite. org/) was used for unbiased motif discovery in the phosphoproteome dataset with the T.
Data availability The coordinates of the T. cruzi PKAR crystal structure bound to 7-CN-7-C-Ino have been deposited in the Protein Data Bank under the code PDB 6FTF. The phosphoproteome and proteome datasets are available in the PRIDE partner repository with the dataset identifiers PXD012245 and PXD009073, respectively. Genome sequence and annotation information was obtained from TritrypDB (http://www. tritrypdb. org). Human PKA substrates and phosphorylation motifs were retrieved from the PhosphoSitePlus database (https://www. phosphosite. org). Gene ontology (GO) enrichment analysis was visualized using Revigo (http://revigo. irb. hr). The motif discovery tool MoMo implemented in the MEME suite (http://meme-suite. org) was used for unbiased motif discovery in the phosphoproteome dataset. The source data underlying Figs.
Acknowledgements The authors thank  P. Bastin  ( Pasteur Institute, Paris ),  A. Bindereif  ( University of Gießen ),  C. Dehio  ( University of Basel ),  K. Gull  ( University of Oxford ),  F. Herberg  ( University of Kassel ),  J. H. Leonhardt  ( LMU Munich ),  J. Mottram  ( University of York ),  E. Pays  ( Free University of Bruxelles ),  U. Walter  ( University of Würzburg ), for plasmids or antibodies. A. Mourão  ( Helmholtz Zentrum Munich ) helped with crystal handling and diffraction and  T. Wein  ( LMU Munich ) advised on molecular docking. The authors thank  U. Manzau  and  U. Havemann  for expert technical assistance with nucleoside and nucleotide syntheses. This work was supported by the  University of Munich , Grants  DFG 1100/7-1  and  BELSPO PAI 6/15  to M. B. and a fellowship from the  Science without borders/CNPq program  to Y.
Experimental setup The data were recorded with the ALICE apparatus in 6 hours of stable data-taking with 129 Xe beams (16 bunches per beam) colliding at √ s NN = 5. 44  TeV in October 2017. The data were collected with a reduced magnetic field of 0. 2 T (as compared to the nominal value of 0. 5 T) in the ALICE solenoid magnet. The performance and a detailed description of ALICE can be found elsewhere  [12]. In the following, the detector elements relevant to this analysis are briefly described. The innermost part of the tracking system of ALICE is the Silicon Pixel Detector (SPD)  [13]  which consists of two cylindrical layers of hybrid silicon pixel assemblies. The inner and outer SPD layers are placed at radii of 3. 9 and 7. 6 cm from the interaction point and cover |η| < 2 and |η| < 1. 4, respectively.
02 TeV  [8] , as a function of N part. The Pb-Pb data are fitted with various parameterisations of N part and N coll , calculated with the Glauber model. The same functions, with the values of the parameters from the Pb-Pb fit, are used for the Xe-Xe data. Also shown is dN ch /dη per participant quark, N q-part , calculated with the effective wounded constituent quarks model  [43] , as a function of N part. The number of participant quarks N q-part is normalised by the average number of participant quarks in pp collisions, µ. dN ch /dη in Xe-Xe at √ s NN = 5. 44 TeV ALICE Collaboration 〉 part N 〈 0 100 200 300 400 〉 η /d ch N d 〈 〉 q-part N 〈 µ , 〉 η /d ch N d 〈 〉 part N 〈 N part N =3. 5 µ =3, q N =3 q N =4. 3 µ =5, q N =5 q N | < 0. 5 η ALICE, | α part N β coll N coll N ) f +(1- part N f core part N core 〉 η /d ch N d 〈 + corona part N pp 〉 η /d ch N d 〈 that scatter only once (N corona part ) and those that scatter multiple times (N core part ).