dataesr/scientific-paragraphs-categorization huggingface dataset:
History The first real quantifier elimination procedure was published by Tarski at the end of the 1940s  [65]. 7 As early as 1954, concluding remarks in a technical report by Davis to the US Army on an implementation of a similar procedure for Presburger arithmetic point at a very early interest in software implementations of real quantifier elimination  [16]. During the 1970s Collins developed the first elementary recursive real quantifier elimination procedure  [11, 12] , which was based on cylindrical algebraic decomposition (CAD). An implementation by Arnon was available around 1980  [1]. CAD has undergone many improvements since and establishes an active research area until today  [7, 14, 40]. Hong reimplemented CAD in an interactive software Qepcad  [13]. Qepcad developed into Qepcad B, which is now maintained by Brown  [6].
In the satisfiable case we obtain exact symbolic sample solutions. Figure  6b  gives float approximations of such a sample solution for φ9. Alternative reductions to real quantifier elimination of our model have been discussed in  [71]. The vast majority of chemical and biological models as they can be found, e. g. , in the BioModels database 23 is considerably larger with respect to both dimension and degrees than our example here. in Sect. 5 we are going to discuss such models based on a different translation into logic. For deciding satisfiability we will use a heuristic method, which is not based on quantifier elimination. We are going to introduce this method in the following section.
Acknowledgements Open access funding provided by  Max Planck Society. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Methods Mice. Six-weeks-old female C57BL/6 (H-2 b ) (B6) were purchased from Charles River Laboratories. All the mice were kept under biosafety level 2 facility and the experiments were performed according to European guidelines (EC2010/63) and acceptance by Paris Descartes University ethical committee (CEEA 34): access number project 14-009. Cells. TC1 cells expressing the HPV16 E6-E7 proteins were developed in the laboratory of T. -C. Wu (Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD). Cells were cultured as previously described  21. Briefly, they were cultured in RPMI 1640 (Life Technologies, ref 7240054 ) supplemented with 10% heat-inactivated fetal calf serum (GE Healthcare, ref A15-101), 1 mM sodium pyruvate (Life Technologies, ref 11 360088), 1 mM non-essential amino acids (Life Technologies, ref 11140035), 100 U ml -1 penicillin and 100 mg ml -1 streptomycin (Life Technologies, ref 10378016),0.
For each slide, counts were done by a senior pathologist on at least five representative fields with a magnification of Â 200. Gene expression analysis. Data were normalized using RMA algorithm in Bioconductor with the custom CDF versus 18 (Nucleic Acid Research 33 (20), e175). Statistical analysis was carried out with the use of Partek GS. All the data have been deposited in NCBI's Gene Expression Omnibus  44  and are accessible through GEO Series accession number GSE77366: http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?token=ghsrcweibruzvsj&acc=GSE77366. Fisrt, variations in gene expression were analysed using unsupervised hierarchical clustering and PCA to assess data from technical bias and outlier samples. To find differentially expressed genes, we applied a one-way ANOVA for each gene and made pairwise Tukey's post hoc tests between groups.
Then, we used P values and fold changes to filter and select differentially expressed genes. DEG enrichment analysis was carried out using Ingenuity (Ingenuity Systems, USA; www. ingenuity. com). Statistical analysis. For mice data, statistical analyses were performed with GraphPad Prism Version 6 software (GraphPad Software Inc. ). Data were expressed as means ± s. e. m. and are representative in every experiment of at least three independent experiments (number of experiment indicated in each figure). Significance was assessed with the Mann-Whitney test to compare two different groups and Kaplan-Meier curves to compare the survival of the different mice groups. For human results, CD8 þ T cells, CD103 þ T cells and CD8 þ CD103 þ T cells assessed by immunofluorescent staining were firstly analysed as continuous data and then dichotomized using the minimum Pvalue approach in order to determine the optimal cut-off for overall survival analysis.
Acknowledgements This work was supported by a grant from the  Institut National du Cancer (INCA) PLBIO IDF  (L. J. and E. T. ), Ligue contre le Cancer-EL2014 (E. T. ),  UniversiteSorbonne Paris Cite(E. T. ) ,  ANR-15-CE17-0023-04  (Selectimmunco) (E. T. ),  Labex Immuno-Oncology  (E. T. ), SIRIC CARPEM (E. T. ) and  ERC  advanced grant (project  340485 ) (L. J. ). M. N. was a fellow of  Fondation ARC  and  M. O. D. obtained a fellowship from  FAPESP.
Data availability. All data have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE77366: http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?token=ghsrcweibruzvsj&acc=GSE77366. The LUAD and LUSC data referenced during the study are available in a public repository from the Broad Institute website (https://gdac. broadinstitute. org).
ACRI-ST | CALCON Technical Meeting, Aug 23 rd 2017 Radiative transfer modeling Radiative Transfer simulations • ARTDECO (Dubuisson et al. ) -http://www. icare. univ-lille1. fr/projects/artdeco  Atmospheric Radiative Transfer Database for Earth Climate Observation  Access to a library of  predefined or specific (atmosphere profile, surface, aerosol and cloud description, filter transmission, etc. ) conditions  models (truncature of phase function,… 1D RT equation solver) » We use the DISORT 2. 1 discrete ordinate solver (Stamnes et al. , 1998) TOA signal simulation  A series of assumptions are made on the physics of the problem and geophysical conditions + uncertainties  Decoupling of the stratosphere (mostly O3 absorption) and troposphere  Tgas finely modeled to take into account absorption lines and continuum  Main absorption by O 3 , O 2 , H 2 O, NO 2  Tgas LUT computed at nadir, corrected by airmass factor for the other geometries  Tropospheric signal (Atmos + DCC) = « Top-of-DCC reflectance »  Tropospheric signal dominated by DCC signal (including multiple-scattering)  Mainly by ice crystals scattering at the top of the cloud  Atmospheric signal dominated by Rayleigh scattering within and above the cloud  Aerosols are distributed in the lowest layers of the atmosphere (except in case of volcanic eruption) no aerosol contribution to the TOA signal  No ground contribution     ,   , ∆,  =     ,   , ∆,  +     ,   , ∆,.
| Calculation of incorporated 13 C and 15 N For all plant and soil samples, we expressed the 13 C isotope content as incorporated 13 C (mg 13 C/m 2 , μg 13 C/m 2 or ng 13 C/g dry matter ), which refers to the total amount of 13 C found in a certain C pool: with atom% labelled being the 13 C atom% of the labelled samples, atom% unlabelled being the 13 C atom% of natural abundance samples and C pool being the respective C pool (mg C/m 2 for bulk and carbohydrate data from shoots and fine roots; μg 13 C/m 2 or ng C/g dry matter for NLFAs and PLFAs from soil). Incorporated 15 N of plant samples was calculated in a completely analogous fashion. Root respired 13 C (μmol 13 C m -2 hr -1 ), which corresponds to the amount of 13 C released incorporated 13 C = (atom% labelled -atom% unlabelled ) × C pool 100% in respired CO 2 from roots during a certain time, was calculated similar to incorporated 13 C: with CO 2, resp.
15 N uptake was calculated as average overall sampling times because the signal was stable over the experimental time. All statistical analyses were done using the r 3. 3. 2 software (R Core Team, 2016). The effects of drought treatment, land-use type and their interaction on soil water content, fine root biomass, carbohydrate concentrations, NLFA and PLFA concentrations, (A+S)-fungi:bacteria ratio as well as 13 C and 15 N tracer uptake were evaluated for each labelling campaign separately using ANOVA from the R base package and permutational ANOVA from the "lmPerm" package  (Wheeler & Torchiano, 2016). We used the standard ANOVA to estimate effect sizes based on F-values and the permutational ANOVA to obtain exact p-values. Permutation tests do not require assumptions about the statistical distribution and are more sensitive with small sample sizes  (Ernst, 2004).
Time series (in hours after pulse labelling) of 13 C tracer data were tested for each labelling campaign separately for the effects of drought, land-use type, sampling time and their interaction using linear mixed-effect models from the "lme4" package  (Bates, Maechler, Bolker, & Walker, 2015). In the mixed-effects models treatment, land use and sampling time (as factor) were set as fixed effects, while rain-out shelter and monolith were set as random effects. All models were assessed for violations of normality, heteroscedasticity and independency, and if necessary, 13 C tracer data were log (+1) or square root (+1) transformed.
Identification of FAMEs was done by comparison of chromato-grams with different known FAME mixtures (Supelco 37 Component FAME Mix; Sigma-Aldrich Chemie GmbH; BR2 and BR4 mixture, Larodan Fine Chemicals AB) and an in house database (Kramer & Gleixner, 2006; Mellado-Vázquez et al. , 2016; Thoms, Gattinger, Jacob, Thomas, & Gleixner, 2010). Compound-specific 13 C isotope analysis of NLFAs and PLFAs was done by GC-IRMS (GC 7890A with PTV injector; Agilent Technologies; coupled via a Conflo IV/GC IsoLink to a Delta V Plus IRMS; Thermo Fisher Scientific) using a DB-1MS UI column (60 m × 0. 25 mm internal diameter × 0. 25 μm film thickness; Agilent.
Acknowledgements This study was supported by the  Swiss National Science Foundation  awarded to CS ( PZ00P3 148261 ). JB is supported by the  European Research Council  through an  Advanced Grant. We thank L. Dutoit for helping with data collection,  M. Furler  for drawing Figure 1 and the  Sierra Nevada National Park  for providing sampling permissions. Thanks to  Hannes Baur ,  Andreas Müller ,  Martin Schwarz  for helping with further species identification.
The indices p and r reflect the number of RNAP and repressor molecules, respectively, that are bound to the gene in a given occupational state with Z(p, r) the relevant part of the canonical partition function. The factor Z is given by the product of all the Boltzmann exponents of the individual binding free energies of the DNA-bound transcription factors, and of the interactions that take place between them when they are bound in that arrangement. All other internal degrees of freedom remain constant, and therefore do not contribute to the weight of a configurational state. This modular approach allows the framework to be used in conjunction with automated scripts to calculate the statistical weight of a configurational state. While for simple promoter architectures it is possible to write down the statistical weights for all the individual configurational states, this quickly becomes cumbersome when the complexity of the promoter architecture increases.
Similar state-weight scripts have been demonstrated for models based on the canonical ensemble, for example in ref  [47]. For the motif of simple repression Z(0, 0) = 1, Zð1; 0Þ ¼ e À b P ¼ x P and Zð0; 1Þ ¼ e À b R ¼ x R. Binding of both RNAP and repressor is prohibited by excluded volume interactions, effectively meaning that the weight of Z(1, 1) is zero and that term is excluded in Eq (1). In the case of N statistically independent gene copies we have X s = X N as in our previous work (Eq (2) in  [38] ). It can immediately be checked that X is given by adding up the weights in the third column in Fig 1. Similarly, the relevant canonical partition function in  [4, 7, 8]  is given by adding up the weights in the right-hand column in Fig 1. The fraction of binding sites occupied by its cognate transcription factor is calculated by  [48] y i ¼ 1 N @ log X s @ log l i ; ð2Þ with λ i the fugacity of the cognate transcription factor i.
Acknowledgments We would like to thank  Manuel Razo-Mejia  for his help in selecting which states are allowed in the lac operon. This work was supported by the  Netherlands Organisation for Scientific Research  grant  022. 004. 016 (JL), by the  European Synchrotron Radiation Facility  (JL), by  La Fondation Pierre Gilles de Gennes (RP) , by the  National Institutes of Health  through the  Directors Pioneer Award   DP1 OD000217  (RP) and through  R01 GM085286  (RP), and by the  John Templeton Foundation  (RP and FMW). The funders had no role in study design, data collection and.
Analysis of intact RIP2K by Liquid Chromatography/Electrospray Ionization Mass Spectrometry (LC/ESI-MS) LC/ESI-MS was performed on a 6210 TOF mass spectrometer coupled to a HPLC system (1100 series, Agilent Technologies). All solvents used were HPLC grade; the HPLC mobile phases were A: H2O 95%, ACN 5%, TFA 0. 03%, B: ACN 95%, H2O 5%, TFA 0. 03%. Protein samples were desalted on-line on a C8 reverse phase micro-column (Zorbax 300SB-C8, 5μm, 5x0. 3mm, Agilent Technologies) for 3 minutes at 100 μl/min with 100% of mobile phase A, then eluted at 50 μl/min with 70% of mobile phase B. MS acquisition was carried out in the positive ion mode in the 300-3000 m/z range and the data processed with MassHunter software (v. B. 02. 00, Agilent Technologies). The mass spectrometer was calibrated with tuning mix (ESI-L, Agilent Technologies).
MALDI TOF mass spectrometry Peptide samples after IMAC enrichment were analysed on a MALDI TOF mass spectrometer (Autoflex, Bruker Daltonics) operated in the reflectron positive ion mode previously calibrated with peptide calibration standard I (Bruker Daltonics) in the 500-5000 m/z range. The mass spectra were acquired, and the data were processed with Flexcontrol and Flexanalysis software (v. 3. 0) (Bruker Daltonics, Bremen, Germany). Peptides were identified by Mascot server search against RIP2K sequence and using the following parameters: trypsin enzyme, missed cleavages: 3, modifications: methionine oxidation, cysteine carbamido-methylation, serine and threonine phosphorylation, singly protonated monoisotopic mass (M+H) 1+ , peptide tolerance: 0. 2 Da.
Analytical ultracentrifugation (AUC) RIP2K was de-phosphorylated and re-phosphorylated again prior to use. RIP2K and RIP2K K47R buffer was freshly exchanged in 20 mM Tris pH 7. 5, 50 mM NaCl, 5% Glycerol and 250 μM TCEP. Various protein concentrations from 4 to 18 μM were tested. Sedimentation-velocity experiments were performed for 22 hours at 42,000 r. p. m and 4˚C in a Beckman Coulter XL-I, using a rotor Anti-50 and double-sector cells (Nanolytics) of optical path length 12 mm with sapphire windows. Data were obtained by monitoring as a function of the radial position the absorbance at 280, 260 or 300 nm. The analyses were done with the Sedfit software, v14. 7g, and figures made with Gussi v1. 0. 9e, freely available at http://www. analyticalultracentrifugation. com, and http://biophysics.
swmed. edu/MBR/software. html, respectively. Partial specific volumes, " v, of 0. 732and 0. 733 mL g -1 , for RIP2K and RIP2K K47R , respectively, and buffer density, ρ of 1. 02 g mL -1 and viscosity η, of 1. 96 cp, were calculated from the composition with the programs sedfit and sednterp (http://sednterp-app. sr. unh. edu/ ). Sedimentation velocity profiles were analyzed in terms of continuous distribution, c(s), of sedimentation coefficients, s  [50]. The experimental s-values were corrected to s 20w , in the conditions of water at 20˚C, and interpreted through the Svedberg equation: s = M (1 -ρ " v) ⁄ (N A 6π η R H ) , where M is the molecular mass, N A the Avogadro's number, R H = f/f min R min , the hydrodynamic radius, with f/f min the frictional coefficient and R min the radius of the anhydrous volume.
Data collection, structure solution and accession numbers Diffraction data were collected at beamlines ID23-1  [51] , ID29  [52]  and MASSIF  [53]  at the ESRF (Grenoble, France). Diffractions limits were 2. 0 Å (RIP2K L294F -AMPPCP), 2. 8 Å (RIP2K D146N -staurosporine) Å and 2. 6 Å (RIP2K K47 ) and. Data were processed with XDS  [54]  or iMOSFLM  [55] , and further analysed using the CCP4 suite  [56]. The structures were solved by molecular replacement using PHASER  [57]. For the RIP2K L294F -AMPPCP structure, BRAF (PDB accession code 2FB8) was used as molecular replacement search model and the RIP2K model so obtained was used to solve all other structures. Refinement was carried out alternately using REFMAC5  [58]  and manual rebuilding with COOT  [59]. Models were validated using Molprobity  [60]  from the PHENIX interface and structure figures were produced with PyMol  [61].
In agreement with previous published data  [10, 39]  MS analysis indicated that RIP2K was heterogeneously phosphorylated during expression in insect cells (Fig  1A ). Prior to in vitro kinase assay, RIP2K was treated with lambda protein phosphatase to remove residual phosphorylation and the reaction monitored by ESI-TOF MS. Mass spectra showed that   to the fact that RIP2K stays in an ATP-rich environment for days instead of minutes, which potentially enhance RIP2K auto-phosphorylation activity. MS data were confirmed by the kinase domain activity (Fig 1D ), indicating that de-phosphorylated RIP2K was capable of selfactivation. In contrast, MS data showed that purified RIP2K K47R and RIP2K D146N were not phosphorylated (Fig 1E  and 1F ). An auto-phosphorylation assay indicated that both proteins were inactive, but they could be phosphorylated in trans by purified full length RIP2 (Fig  1G ).
Structures of WT RIP2K and RIP2K D146N are in the active state conformation As expected from the phosphorylation profile, RIP2K-AMPPCP show a typical active state, within AMPPCP bound the active state between the N-and C-lobes (Fig  2C ). As we solved the RIP2K-AMPPCP structure at 2. 0 Å, significantly higher than the published equivalent one solved at 3. 2 Å (PDB: 5AR3,  [39] ), we will use our structure for comparison purposes. The R-spine is aligned, the DFG motif is in the expected IN conformation, the Helix αC is inwardly displaced  [32]  and Glu66 in Helix αC (Glu91 in PKA) forms a salt bridge with Lys47 from strand β3 (Lys 72 in PKA) (Fig 3A  -3C ). Crystals of RIP2K D146N have been obtained in the apo state and with AMP-PCP or the promiscuous inhibitor staurosporine  [68]  bound, without significant differences in the resulting structures.
The best crystals diffracted to 2. 8 Å resolution and were obtained with staurosporine. Each monomer has an inhibitor molecule bound in the active site, whose configuration presents all the features of a kinase in the active state (Fig 2D  and 2G ). Differently from the ligand in the RIP2K-AMPPCP structure, staurosporine is coordinated by Gln150 backbone, while Gln150 sidechain is hydrogen bonded to the Tyr192 phenyl ring in the AS (Fig 3D  -3F ). Therefore, the enzyme assumes an active conformation without being phosphorylated, as has been described for IRAK4-KD D311N in complex with staurosporine (PDB code 4U97,  [68] ). Differently from the IRAK4-KD D311N structure, we did not observe a possible prone-to-autophosphorylate conformation in cristallo. Both the RIP2K-AMPPCP and RIP2K D146N structures exhibit a disordered AS.
Structure of RIP2K K47R is in the inactive conformation The inactive state of RIP2K resembles the Src/CDK-like inactive conformation, with the DFG motif in the IN state and the Helix αC outwardly displaced (Fig 2A  , 2B  and 2E )  [33, 34]. Helix αC has rotated by ~17 degrees out from its active state position (calculated using the   [33] ) and the monomeric BRAF (S3 Fig)  [43]. The same AS Helix, together with the Helix αC inwardly displaced are also present in the structure of the inactive state of RIPK1 bound to an inhibitor (PDB code: 4NEU,  [70]  and in RIP3K in complex with its substrate MLKL (PDB code: 4M69  [71]. The recent structure of RIP2K in complex with the Type I inhibitor biphenylsulfonamide (PDB code: 5AR8)  [39]  shows also an outwardly displaced Helix αC, while the AS in the active conformation.
Kinase activation by upstream oligomerisation or crowding events has already been described  [68, [76] [77] [78]. In the context of the entire RIP2 protein, the oligomerization event might be driven by NOD2 oligomerisation. Comparison of our RIP2K structures reveals differences in the conformation of the Lys209 loop. In all structures, Lys209, at the C-terminal end of the Lys209 loop (residues 200-210), is well-ordered with its side-chain engaged in hydrogen bonds with the main-chain carbonyloxygens of P277 and R280 thus stabilising a helical turn conformation of residues 277-280. Generally the rest of the Lys209 loop is disordered, notably in the active, AMPPCP bound, structures. However in chain A of the RIP2K K47R model the loop is structured and contains a short helix (Fig 3A ). A similarly structured loop is observed in one monomer of the RIP2K D146N -STAU bound dimer and in recently described structures with bound inhibitors ponatinib, SB2 or biphenyl-sulfonamide (PDB codes: 4C8B, 5AR4, 5AR8 respectively)  [38, 39].
Acknowledgments We used the mass spectrometry and analytical ultracentrifugation platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). We thank  Sylvie Kieffer-Jaquinod  and  Christophe Masselon  ( CEA, Grenoble, France ) for LC/MS-MS experiments and  Aline Le Roy  and  Christine Ebel  for assistance with AUC. We thank members of the  ESRF-EMBL Joint Structural Biology Group , in particular  Matthew W. Bowler , for access to ESRF beamlines, the staff of the  EMBL eukaryotic expression (EEF)  and high-throughput crystallization (HTX) facilities and  Stephanie Gaudon  for help in crystal harvesting. This work was partly financed by  ANR  grant  CARDINNATE  ( ANR12-BSV3-0010-01 ) to SC.
afro. who. int/en/ yellow-fever/sitreps. html For the Database of Global Administrative Areas see http://www. gadm. org/ See Online for appendix must also coincide with patterns of human movements for a successful viral transmission cycle to be established. 12, 13 his ecological landscape has been shown to be strongly aff ected by temperature, precipitation, humidity, vegetation coverage, and degree of urbanisation. 14 n this study, we jointly analysed datasets describing the epidemic and spatial spread of yellow fever virus, vector suitability, human demography, and mobility in central Africa, aiming to better understand, quantify, and predict the spread of yellow fever virus in this region. By identifying the areas that are at the highest risk of yellow fever virus transmission, such a framework could inform the response to ongoing outbreaks.
Epidemiological and vector data We obtained epidemiological data on confi rmed and suspected yellow fever virus cases in Angola and the DR Congo (the most aff ected countries) from the WHO situation reports for the period Dec 4, 2015, to Aug 4, 2016. We extracted the fi rst and last dates of disease onset for each district in Angola (n=163) and for each commune in the DR Congo (n=150). We used numbers of cases per week in Angola, but weekly case numbers were not available for the DR Congo at the time of data collection (Aug 4, 2016). We matched locations with administrative unit fi les available from the Database of Global Administrative Areas. To generate district-level ecological risk of disease transmission, we extracted estimated suitability values for the primary urban vector A aegypti at the lowest administrative level (ie, all 313 districts in Angola and communes in the DR Congo).