dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Rogelj et al. , 2015; Mengis et al. , 2018; Tokarska et al. , 2018). The requirement for specific information regarding ZEC to assess remaining carbon budgets was identified in the IPCC Special Report on Global Warming of 1. 5°C  (Rogelj et al. , 2018 ). An initial paper exploring ZEC in this context, explicitly on timescales of relevance to 21 st century carbon budgets, is planned on a timeline that could support an improved assessment of the ZEC and its influence on carbon budgets in the IPCC Sixth Assessment. All participating model groups who are able to complete and provide data for simulation A1 in time will be invited to join this analysis. ZECMIP welcomes community engagement in the participation of simulations and their analysis, and input to future analysis and experimental design.
Acknowledgements This protocol was devised at a Global Carbon Project workshop supported by  H2020   EU  project  CRESCENDO  under grant agreement No  641816. CDJ was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). TLF acknowledges support from the  Swiss National Science Foundation  under grant  PP00P2_170687. KZ and AHMD acknowledge support from the  National Sciences and Engineering Research Council of Canada's Discovery Grant program. CDK acknowledges support from the  US DOE BER Regional and Global Model Analysis Program  through the  Early Career Research Program  and the RUBISCO SFA projects. K. B. T. was supported by the  UK NERC -funded  SMURPHs  project ( NE/N006143/1 ). We gratefully acknowledge  Friedrich Burger  for providing the GFDL-ESM2M data.
coli thioredoxin (1 mg•ml -1 ), and 20 mM DTT, at different reduced-to-oxidized ratios, was incubated for 3 h at 25 °C before measuring PRK activity. The obtained curves were fitted by nonlinear regression (CoStat; Cohort Software) to the Nernst equation. Determination of the pK a Values of the Catalytic Cysteines. The determination of pK a values for both CrPRK and AtPRK was performed as previously described  (39). Briefly, PRK activity was measured on reduced PRK samples (2 μM) following 20 min of incubation at 25 °C in the presence or absence of 0. 2 mM iodoacetamide (IAM). Incubations were performed in different buffers within a 4. 5 to 10 pH range. Aliquots of 10 to 20 μL were used to measure PRK activity in 1 mL of assay mixture. The residual activity was expressed as the percentage of inhibition between IAM-treated and untreated samples, and expressed as a function of pH.
The obtained curves were fitted by nonlinear regression (CoStat; Cohort Software) to a modified Henderson-Hasselbalch equation. Crystallization and Structure Determination. Reduced CrPRK was concentrated to 10 mg•mL -1 in 30 mM Tris•HCl, 1 mM EDTA (pH 7. 9). Reduced AtPRK was concentrated to 10 mg•mL -1 in 25 mM potassium phosphate buffer solution (pH 7. 5). They were crystallized by the vapor-diffusion method at 293 K with sitting drop in 22% (wt/vol) PEG MME 5K, 0. 1 M Mes (pH 6. 5), 0. 2 M ammonium sulfate for CrPRK, and hanging drop in 1. 5 M sodium malonate (pH 5. 0) for AtPRK. The crystals obtained were analyzed by X-ray diffraction, and the structures were solved by molecular replacement. The refinement was performed with REFMAC 5. 8. 0135  (40) , selecting 5% of reflections for R free (for details, see SI Appendix, Materials and Methods).
Small-Angle X-Ray Scattering Data Collection and Analysis. Small-angle X-ray scattering data were collected at the BioSAXS beamline BM29 (41) at the European Synchrotron Radiation Facility (ESRF, Grenoble), and the datacollection parameters are reported in SI Appendix, Table  S4. A size-exclusion chromatography-SAXS experiment was performed using an HPLC system (Shimadzu) directly connected to the measurement capillary. All of the details about experimental procedures and data analysis are listed in SI Appendix, Materials and Methods. Modeling from SAXS Data. The experimental SAXS profile of the CrPRK dimer was fitted with the theoretical profile calculated from the atomic coordinates using CRYSOL3  (42)  with default parameters. In addition, lowresolution models of the CrPRK dimer based on the SAXS profile were built using the ab initio program GASBOR  (43).
All details are reported in SI Appendix, Materials and Methods. Data Availability. Atomic coordinates and structure factors have been deposited in the Protein Data Bank under PDB ID codes 6H7G  (44)  and 6H7H  (45)  for CrPRK and AtPRK, respectively. The SEC-SAXS data of CrPRK have been deposited in the Small Angle Scattering Biological Data Bank with ID code SASDDH9  (46).
Bacteria and Archaea. Cyanobacterial and eukaryotic PRKs emerged as the third clade from Archaea (SI Appendix, Fig. S6B). Single PRK structures from the anoxygenic photosynthetic bacterium Rhodobacter sphaeroides [RsPRK; Protein Data Bank (PDB) ID code 1A7J] (21) and the nonphotosynthetic Archaea Methanospirillum hungatei (MhPRK; PDB ID code 5B3F) (22) have been reported.
ACKNOWLEDGMENTS. We thank  Elettra  and the  European Synchrotron Radiation Facility  for allocation of X-ray diffraction and SAXS beam time (BAG Proposals  MX686  and  MX1750 ) and the staff of beamlines XRD1 (Elettra) and  ID14-4  and  BM29  ( ESRF ) for technical support. S. F. thanks the  Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (CIRCMSB). This work was supported by  Italian Ministry of Education, Research and University (MIUR)  Grant  FIRB2003  (to P. T. );  University of Bologna  Grant  FARB2012  (to F. S. , S. F. , and M. Z. );  CNRS, Sorbonne Université ,  Agence Nationale de la Recherche  Grant  17-CE05-0001 ; and  CalvinDesign  and  LABEX DYNAMO ANR-LABX-011  (to J. H. , S. D. L. , and P. C. ).
1 HS u and 1 he u change depending on the heating system type and size that are chosen. The u1-values include the material, the labor and are representative for the localization of the building (Quebec City, Canada). They have been determined from the "Residential Cost Data RSMeans online Advanced" database  [24]  and are reported in Tables 3, 4 and 5. The cost related to the heating energy demand depends on the energy source. To evaluate the cost of electricity, Hydro-Québec "Tarif D" is selected, which applies for domestic use  [25]. For natural gas cost evaluation, Gaz Métro "Tarif D1 is selected  [26]. This tariff applies for general distribution, mainly used for residential buildings. For the purpose of this study, the unit price of natural gas has been set as shown in Section 15. 2. 2.
Seven hours after the light was switched on, an aliquot was sampled. It was first dark adapted for 1 hour, and then introduced into the 10 mm quartz glass cuvette of the PAM fluorometer controlled by WinControl-3 software. Measurements were performed on the same amount of Chl a and cultures were diluted in N-or N+ artificial sea water medium depending on the treatment. Maximum PSII quantum efficiency (Eq 3) was then measured using a 600 ms saturating pulse (2500 μmol photons. m -2. s -  ): Where F0 is the minimum fluorescence yield for dark adapted cells, Fm, the maximum fluorescence yield for dark adapted cells during the saturating flash and Fv the variable fluorescence.
rETR value data were fitted using the Eilers and Peeter model  [48]  in order to obtain the initial slope (α), the light saturation parameter (Ek) and the maximum relative electron transport rate (rETRmax). Ek was derived from rETRmax and α (Eq 5): Ek ¼ rETRmax=a ð5Þ Pigment composition. To estimate chlorophyll a (Chl a) and the total amount of carotenoid pigments (Car) per cell, 2 mL of culture were sampled and centrifuged at 11200 g. for 5 min at the end of the growth phase. After supernatant removal, pigments were extracted by adding 2 mL of methanol (99. 9%) to the pellet. To remove cell debris, methanol suspension was centrifuged for 5 min at 11200 g. Absorbances at 665, 632, and 480 nm were measured by spectrophotometer (SHIMADZU, UV-1900) on the clean supernatant to calculate pigment content, expressed in pg.
Data processing Each treatment (N+, N-, LL, ML and HL) was done in triplicate for each strain. Data were expressed as the mean of each triplicate ± standard deviation (SD). Two-way ANOVAs with a 5% significant level were carried out after checking normality and homogeneity using Shapiro-Wilk and equal variance tests. Tukey's least significant tests was used to determine which experimental conditions were significantly different. All statistical analyses were carried out using SigmaPlot software.
Comparison with Zaytsev For NaOH, data in Zaytsev  [1]  are available between 0 and 200°C and 0-0. 50 of mass fraction. The two models are Zaytsev's (25) and Olson's model (  27 ). The Zaytsev's model  [1]  has an average difference of 5% whereas Olson's one is 6% over the range T=[60-105°C] and YNaOH=[2-0. 40]. Also, for Olson's model the difference reaches 15% for 100°C and 18% and this model has the particularity to have an interval of validity depending the mass fraction. This domain of validity is presented in the table5. For both KOH and NaOH, triangle and the solid line represents the experimental point and their correlation of Zaytsev  [1]. For KOH (left) the dotted line is the correlation from Guo  [5]. For NaOH (right) the dotted line represents the correlation from Olsson  [11].
Static reference gas standards in nitrogen are available for ammonia (produced by VSL), hydrogen chloride (commercial supplier and certified by VSL and static reference gas standard from, CEM (Spain) and formaldehyde (commercial supplier and certified by VSL). These are diluted with high purity hydrogen for calibration. For formic acid a commercial mixture in a hydrogen matrix is available. This mixture has  been diluted with nitrogen to enable a comparison with the formic acid spectrum contained in the PNNL spectral database  [27].
Static reference gas standards and dynamic dilution system using mass flow controller system (Bronkhorst, NL) were used to generate calibration curve ranging from 1 to 75 μmol/mol of oxygen and 2-150 μmol/mol of nitrogen. The method can separate argon from oxygen. Argon amount fraction was calibrated using a NPL PRM in helium matrix with 5 μmol/mol of argon. The data were scrutinised however no result was discarded without a technical reason. The calibration curve, results of analysis and uncertainties associated were determined using NPL software XLGENline  [28]. An expanded uncertainty using a coverage factor of 2 was used. In some cases, a more conservative uncertainty was derived from scientist expertise. CEM measured oxygen, Argon and Nitrogen with an Agilent 6890 GC with a PDHID detector (Pulsed Discharge Helium Ionization Detector) from VICI-VALCO.
The capillary column was a HP-MOLESIEVE 5A (30 m � 0. 53 mm x 50 μm). The injection temperature was 50 � C and the GC oven was set at 26 � C. The PDHID detector was set at 100 � C. Helium BIP® (Air Products, Spain) was used as carrier gas with flow rate of 1. 5 ml/min. Pressure regulators were used with samples and PRMs and 2 bar outlet pressure was used for injection. The lines and regulators are purged before each analysis (3 times). An automatic sampler (designed by VSL) is programmed using AGILENT-Chemstation software to inject all the samples and the PRMs in the same analytical sequence. Oxygen and argon are not completely separated but it is possible to asses if any of them is present in the sample at the required level. For oxygen calibration, the PRMs were prepared gravimetrically in accordance with ISO 6142-1:2015  [29]  from some PRMs (CEM, Spain).
NPL PRMs in hydrogen matrix containing nitrogen (N 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), methane (CH 4 ), ethane (C 2 H 6 ) and oxygen (O 2 ) were used to calibrate the GC. The calibration curve was from 20 nmol/mol to 10 μmol/mol for CO, CO2, CH 4 and non-methane hydrocarbons (reported on methane basis). The data were scrutinised however no result was discarded without a technical reason. The calibration curve, results of analysis and uncertainties associated were determined using NPL software XLGENline  [16]. An expanded uncertainty using a k value of 2 was used. In some cases, a more conservative uncertainty was derived from scientist expertise.
e. , question 8)  [60]. The characteristics presented above also reflect on how the models are used in decision-making. MARKAL and TIMES are used by numerous countries and organizations for energy planning at different geographical scales  [68, 69]. Both models belong to the linear programming-based optimization group using GAMS as a programming language. Their main objective is finding a combination of energy technologies ensuring energy security, energy affordability and reduction of CO 2 emissions at the lowest possible costs. MESSAGE is another widely used energy optimization model  [71]. It is often employed for determining cost efficient technological portfolios allowing for GHG emissions reduction. PRIMES is another technology-rich partial equilibrium energy model. It looks for an equilibrium solution for energy supply, demand, cross-border energy trade and emissions in European countries.
Therefore, each model has certain strengths and weaknesses, as well as it makes it possible to address and answer different questions of the current energy paradigm. The following examples will illustrate the broad range of their scope. MESSAGE-MACRO  [76]  is an energy partial equilibrium model connected to a general equilibrium macroeconomic model. The solution method of this model combines linear optimization for the MESSAGE module and non-linear optimization for the MACRO module. Inputs for the model are very detailed on the energy supply side (MESSAGE) and very aggregated for the energy demand side (MACRO). The main goal of this hybrid is examining the interrelations between energy supply costs as well as technologies and major macroeconomic parameters in order to provide the best short-and especially long-term policy.
Hence, it is focused on addressing question 8  [76]. MESSAGE-MAGICC  [77]  is not a pure energy model but it is still seen as a relevant hybrid energy climate model. It is a hybrid that combines the bottom-up energy system structure with a more macro-level climate model structure. MESSAGE-MAGICC estimates the effects of the energy-use-caused GHG emissions on the global climate system; hence, its primary objective is providing answers to question 1. Outputs of this model, together with the other models, are used as inputs for assessments and scenario studies by the Intergovernmental Panel on Climate Change (IPCC), the World Energy Council (WEC) and other organizations. The MAGICC module represents the climate and is based on a global average energy balance equation integrating atmosphere and ocean climate dynamics  [77].
[78]  also does not correspond to the commonly understood definition of a hybrid energy model and Access could be seen as a simple extension of MESSAGE. However, if a hybrid is broadly defined as two or more fully functioning individual models that produce more insightful results when combined [90], MESSAGE-Access can be counted as a hybrid. The Access module represents a choice of energy technologies in the residential sector. The output of MESSAGE-Access  [78]  looks at the consequences of a transition to clean cooking fuels and electricity in the poorest world regions and implications of this for the global energy supply. The model particularly looks at the costs of health, environmental and economic consequences of different energy transition pathways. Currently, MESSAGE-Access is used by the United Nations Secretary General's Sustainable Energy for All (SE4All) initiative aiming at meeting Goal 7 of the SDGs of clean and affordable energy  [92].
The limits for the non-renewable energy resources are modelled in the form of the region-specific extraction cost-curves. Similarly, the limits for the renewable energies are modelled in REMIND as the maximum technical resource potentials in different regions. The feedback from climate change to energy resource availability is not modelled in REMIND (i. e. , question 3). REMIND incorporates a sophisticated emissions sector which includes those of aerosols and ozone precursors (i. e. , question 1). Also, additional land use CO 2 and agricultural non-CO 2 emissions are incorporated in the MAgPIE module. In addition to already mentioned environmental impacts considered a water sector is present in REMIND. It aims for accounting the water use associated with different energy technologies (i. e.
Acknowledgements This work was carried out in the framework of  EJD-FunMat (European Joint Doctorate for Multifunctional Materials ) and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  641640. In addition, the authors from IREC thank  Generalitat de Catalunya  for financial support through the  CERCA Program  and  M2E  ( 2017SGR1246 ), and support by the  European Regional Development Funds (ERDF, FEDER)  and  MINECO  project  ENE2017-85087-C3-2-R. S. M. -L. thanks  European Union's Horizon 2020  and the  Agency for Business Competitiveness of the Government of Catalonia  for funding under the  Marie Sklodowska-Curie  grant agreement No. 712939  (TECNIOspring PLUS).
e. 'type II intermediates';  Monson et al. , 1988; von Caemmerer, 1992; Sage et al. , 2012; Lundgren et al. , 2016). This results in a reduced CO 2 compensation point and oxygen inhibition  (Lundgren et al. , 2016) , as observed in other species acquiring part of their carbon via a weak C 4 cycle  (Ku et al. , 1991). In addition to the seven A. semialata populations, we included one population of each of the C 4 congeners A. angusta (AANG1 from Uganda) and A. cimicina (from Madagascar) to enable comparison of convergent C 4 -related changes in gene expression (Supplementary Fig. S1 ). Finally, an Entolasia marginata population from Australia was included as a C 3 outgroup. Three distinct genotypes for eight of the 10 populations described above were retrieved from a recent data set  (Dunning et al.
The raw RNA-Seq data were cleaned using the Agalma pipeline v. 0. 5. 0 to remove low quality reads (Q<30), and sequences corresponding to rRNA or containing adaptor contamination  (Dunn et al. , 2013). De novo transcriptomes were assembled using Trinity (version trinityrnaseq_r20140413p1;  Grabherr et al. , 2011). All raw data and transcriptome assemblies have been submitted to the NCBI repository (Bioproject PRJNA401220). Coding sequences (CDS) longer than 500 bp were predicted for each population using OrfPredictor  (Min et al. , 2005) , which uses homology to a user-supplied reference protein database or ab initio predictions if no suitable match is found. The protein database used comprised the complete coding sequences of eight model species: Arabidopsis thaliana, Brachypodium distachyon, Glycine max, Oryza sativa, Populus trichocarpa, Setaria italica, Sorghum bicolor, and Zea mays.
Phylogenetic reconstruction using core orthologs Single-copy orthologs were extracted from the newly and previously published transcriptome assemblies  (Dunning et al. , 2017)  to infer phylogenetic relationships among individuals. Homologous sequences to 581 single-copy plant core orthologs previously determined in the Inparanoid ortholog database  (Sonnhammer and Östlund, 2015)  were identified using a Hidden Markov Model-based search tool  (HaMSTR v. 13. 2. 3; Ebersberger et al. , 2009). Sequences of the single-copy plant core orthologs were subsequently aligned using a previously described stringent alignment and filtering pipeline  (Dunning et al. , 2017). In brief, the CDS were translationally aligned and filtered using T-COFFEE v. 11. 00. 8cbe486 (Notredame et al. , 2000)  before trimming with gblocks v.
Differential expression analyses For differential expression analysis, we used the 45 144 cDNA sequences from the A. semialata reference genome  (Dunning et al. , 2019 ; accession number QPGU00000000) as a reference. Cleaned reads were mapped to the reference using Bowtie2 v. 2. 3. 4. 1  (Langmead and Salzberg, 2012 ) recording all alignments. Counts for each transcript were then calculated using eXpress v. 1. 5. 1  (Roberts and Pachter, 2013)  with default parameters, and are reported in reads per kilobase of transcript per million mapped reads (RPKM). A multivariate analysis was used to assess similarities and differences in overall transcriptome expression profiles between samples. Clustering of expression profiles based on the biological coefficient of variation (BCV) were identified with multidimensional scaling (MDS) in edgeR v3.
Acknowledgements This paper is dedicated to the memory of  Mary Ann Cajano , from the  University of the Philippines at Los Banos , who helped with the identification of plant specimens. The authors thank  John Thompson  who helped with plant collection. This work was funded by the  Royal Society University Research Fellowship  (grant no. URF120119 ) and the  Royal Society Research Grant  (grant no. RG130448 ) to PAC. LTD is funded by an  NERC  grant (grant no. NE/M00208X/1 ), and JKO and MRL are funded by an  ERC  grant (grant no. ERC-2014-STG-638333 ).
In Situ FTIR Spectroscopy The FTIR instrument used for the in situ study is a Bruker Tensor 27 equipped with a mid-IR source, swappable room-temperature deuterated triglycine sulfate detector, and liquid nitrogen-cooled narrow-band mercury-cadmium-telluride (MCT) detectors. The MCT detector was used with the SpectraTech high-pressure IR cell. The Bruker OPUS program (version 7. 2. 139. 1294) was used for data collection and data analysis on a Windows 7 computer system. The higher IR throughput of the ZnSe crystal led us to use a 2-mm aperture setting to avoid oversaturating the MCT detector. Background spectra with mesitylene solvent were collected and stored at the various temperatures planned for the sample spectra. Solvent backgrounds were then loaded and automatically subtracted from sample spectra at the corresponding temperatures.
Synchrotron X-Ray Total Scattering The high-energy X-ray total scattering experiments were conducted at the beamline ID15A at the European Synchrotron Radiation Facility, Grenoble. Data were collected at room temperature using high-energy photons of 75 keV (l = 0. 164 A ˚), with appropriate statistics to achieve high momentum transfer values Q (Q max = 4psinq/l). Solid samples were packed into Kapton tubes with inner diameter of 1. 5 mm and mounted in transmission geometry. The intensity scattered from the unfilled Kapton tubes was measured separately for background subtraction. Intensities were recorded using a PILATUS3X CdTe 2M solid-state detector with sample-to-detector distance of z200 mm. The experimental 2D images were processed using the pyFAI program  66  to yield 1D (Q, I) diffraction patterns.
The PDFgetX3 program  67  was used to subtract the background (empty Kapton tube), to perform instrument and sample corrections, and to obtain the corrected-normalized diffraction intensity of the total scattering structure factor S(Q) and the X-ray structure factor F(Q). The S(Q) was converted into the pair distribution function (PDF), G(r), through the sine-Fourier transform according to GðrÞ = 2 p Z Qmax Q min Q½SðQÞ À 1 sin QrdQ; G(r) represents the probability of finding an atom i at a given distance r from another atom j. The relative intensity in G(r) relates directly to the number of these pairs and their scattering power. For amorphous materials, the intensity in G(r) peaks diminish as r increases due to the finite range of structural correlations, and defines the atomic-range order in the material.