dataesr/scientific-paragraphs-categorization huggingface dataset:
Of all the eukaryotic algal groups, diatoms make the most substantial contributions to photosynthesis in the contemporary ocean. Understanding the biological innovations that have occurred in the diatom chloroplast may provide us with explanations to the ecological success of this lineage and clues as to how best to exploit the biology of these organisms for biotechnology. In this paper, we use multi-species transcriptome datasets to compare chloroplast metabolism pathways in diatoms to other algal lineages. We identify possible diatom-specific innovations in chloroplast metabolism, including the completion of tocopherol synthesis via a chloroplast-targeted tocopherol cyclase, a complete chloroplast ornithine cycle, and chloroplast-targeted proteins involved in iron acquisition and CO 2 concentration not shared between diatoms and their closest relatives in the stramenopiles.
We also identified possible loss events, defined if a chloroplast-targeted protein was not detected in any members of the clade, the clade contained multiple species and at least one genome sequence, and the protein was detected in at least two successive sister-groups to the clade. A KEGG map of two salient time points-the common ancestor of pennate and polar centric diatoms and the common ancestor of all diatoms, bolidophytes, and hypogyristea (pelagophytes and dictyochophytes)-is provided in Figure  3A. A comparison of the proteins identified through this approach, via reciprocal BLAST best-hit analysis, to analogous chloroplast protein datasets from plants, red algae, and their closest relatives  [59] [60] [61] [62]  is provided in Figure  3B. Complete outputs are provided in Table  S1.
(Kyoto Encyclopedia of Genes and Genomes) pathways that could be assigned to the chloroplasts of diatoms and their close relatives, based on the evolutionary distributions of 9531 chloroplast-targeted proteins identified by  Dorrell et al. [25, 58]. KEGG functions were identified using KEGG mapper  [63] , and key chloroplast metabolism pathways are annotated following  [60]. Blue lines show metabolic pathways present in the common ancestor of diatoms, bolidophytes, pelagophytes, and dictyochophytes; red lines show pathways subsequently gained by a common ancestor of the chloroplasts of pennate and polar centric diatoms; and green lines show pathways conserved between but performed by different enzymes in each lineage. Panel (B) compares the protein sets identified in the ochrophyte common ancestor; the common ancestor of diatoms, bolidophytes, pelagophytes, and dictyochophytes; and the common ancestor of pennate and polar centric diatoms to published experimental and phylogenomic plastid protein datasets for Arabidopsis  [60] , the green alga Chlamydomonas  [61] , the red alga Cyanidioschyzon  [62] , and the glaucophyte Cyanophora  [59].
Protein co-occurrence is detected by reciprocal BLASTp/BLASTp search best-hit between each dataset with bidirectional threshold evalue 1 × 10 -5. Complete plastid proteome outputs for each species, and reciprocal BLAST best hit analysis between proteome datasets, are provided in Table  S1. The diatom chloroplast performs effectively all of the essential pathways identified in plant chloroplasts, including photosynthesis, central carbon and lipid metabolism, synthesis of plastidial amino acids (e. g. , glutamate/ glutamine, cysteine, lysine, branched chain, and aromatic amino acids), chlorophyll and carotenoid synthesis, and essential plastid biogenesis pathways associated with expression of the chloroplast genome and protein import [25,58,61] (Figure  3A ). However, it differs substantially in protein content from that of its ancestors, including (for example) substantial numbers of proteins exclusively shared with green algae and plants and not detectable in red algae and large numbers not present in primary chloroplast lineage (Figure  3B ).
Sequence IDs shown in this figure are provided in Table  S3. Panel (D) shows a three-dimensional fluorescence microscope image of a line of F. solaris expressing a C-terminal GFP full length fusion construct of native squalene synthase protein, following previous methodology  [21, 25, 96]. Cells visualised show two localisation patterns, both previously indicated to the periplastid compartment  [97] -either (i) elongated localisation along the chloroplast periphery or (ii) across the mid-band of dividing chloroplasts. A diagram of the targeting sequence of this protein is provided in Figure  S4. Elsewhere within our dataset, we identified chloroplast-targeted isoprenoid biosynthesis enzymes in Pseudo-nitzschia sp. , Synedra acus, and Rhizosolenia setigera (Figure  4 , Figure  6Ciii ); chloroplast FPP synthesis has also been predicted in Haslea ostrearia.
Comparing the chloroplast-targeted proteins of species with industrially desirable phenotypes, such as F. solaris, may reveal candidate pathways for engineering in other organisms. For example, it will be interesting to determine if suppressing chloroplast pyruvate or BCAA metabolism in P. tricornutum can enhance lipid production, which would support a link between these proteins and the oleaginous phenotype of F. solaris. Similarly, heterologous expression of chloroplast-targeted proteins aimed at modifying isoprenoid metabolism in F. solaris and other diatoms may reveal the function of these enzymes and might also allow us to generate new and industrially relevant metabolites in easily cultivable species. Overall, the unique chloroplast metabolic pathways identified within multi-species transcriptome datasets help us understand more clearly the features underpinning diatom biology.
Fluorescence parameters For each FRR induction and relaxation curve we exported the data from the FluorWin data capture software (Photon Systems Instruments, Drasov, Czech Republic). We used the psifluo package  [42]  running under the R statistical programming environment  [43]  to fit the fluorescence rise profile using a model  [23]  with four parameters: -minimal fluorescence, F 0 , before any closure provoked by the sequence of flashlets; -maximal fluorescence, F M , once all PSII are closed by the cumulative action of the sequence of flashlets; -the effective absorption cross section for PSII photochemistry, σ PSII (A 2 PSII -1 ), which is based upon a target concept whereby incident photons are captured by the pigments serving PSII and thereby drive an increase in fluorescence; and -the coefficient of excitonic connectivity ρ which measures the departure of the rise profile from a simple exponential function, and which is believed to reflect equilibration of excitation among PSII centres during the sequence of flashlets.
We thereby determined F 0 , F M , σ PSII , ρ after the initial dark period. We then used the same measurement and fitting protocol to determine F S , F M ʹ, σ PSII ʹ ρʹ for each actinic light period; and F 0 ʹ 2 s , F M ʹ 2 s , ρʹ 2 s , σ PSII ʹ 2 s following 2 s of darkness immediately after each actinic light period (Fig. 1 ). We could then estimate variable fluorescence in the illuminated state, F V ʹ, as F M ʹ 2 s -F 0 ʹ 2 s. In parallel we used the psifluo package  [42]  to fit the re-opening curves with a single phase exponential model to estimate an overall lifetime for re-opening of PSII  [44] , τ, or τʹ if measured immediately after illumination. The reciprocal 1/τʹ is then a rate constant for re-opening of the PSII pool following complete closure. We estimated the quantum yield for PSII photochemistry under light acclimated conditions but with all PSII opened as: ′ = ′ -′ ′ F F F F F ʹ/ ( ) ; V M s M s s M s 2 2 02 2 (1) We estimated the yield  [45, 46]  of photochemical electron transport (YPSII) under actinic illumination as: = - YPSII F F F ( ʹ ʹ) ʹ ; M S M (2) YPSII is arithmetically equivalent to F V ʹ/F M ʹ multiplied by the fraction of PSII remaining open under the given illumination (Eq.
Acknowledgements This publication was supported by the  European Commission  under the  Horizon 2020 Research and Innovation Program GHaNA  (the  Genus Haslea, New marine resources for blue biotechnology and Aquaculture)  under Grant Agreement No  734708/GHANA/H2020-MSCA-RISE-2016  (JLM); the  Pays de la Loire region programme AMI (Atlantic MIcroalgae ); The  Canada Research Chairs (DC)  and by the  European Researcher Mobility Program of the Czech Academy of Sciences  ( CZ. 02. 2. 69/0. 0/0. 0/16_027/0007990 ). Equipment used in the study was funded by the  New Brunswick Innovation Foundation , the  Canada Foundation for Innovation , and the  Natural Sciences and Engineering Research Council of Canada (DC). The authors thank  Miranda Corkum  for assistance with microbial culturing.
05° in the range of 10° -140°. The structural phase was identified in pXRD pattern using the Joint Committee on Powder Diffraction Standard International Centre for Diffraction Data (JCPDS-ICDD) and COD database  [32]. The instrumental resolution function was performed using LaB6 as a standard powder sample, prior to Rietveld refinement of pXRD spectra. The angular variation of the fullwidth at half-maximum (FWHM instrumental, βinstrumental) of the diffraction lines was represented by Caglioti relationship, implemented in the MAUD software  [33].
In both cases, exhaust gas was led towards a gas measurement system: continuously for the open-vessel condition and at the end of batch culture for the closed-vessel condition. A strip of LEDs (60 LED/m, type 3014, IP 65) was fixed on a removable cylindrical steel mesh support. This support surrounded the steel mesh shield of the glass vessel at a distance of 5 cm. Total illumination was 30,000 lx at the surface of the steel mesh shield. The photosynthetic bacterial culture was carried out in a labmade dark climatic enclosure. The light spectra of LED and Na lamps (Plantastar, 150 W, Osram) were measured by a spectrophotoradiometer (USB2000, Ocean Optics, USA) coupled with a cosine receptor (3,900 µm). Light energy was measured via the Parspec module integrated into SpectraSuite software (32-bit version: 2009).
a Labels of the d orbitals were adopted from Gaussian09 output file. The N 1s peaks are broader than expected from the instrument resolution (0. 090 eV) and core-hole lifetime (0. 132 eV)  [22] , indicating that vibrational levels in the cation are likely being accessed. However, without significant shoulders in the spectra, no experimental vibrational energy can be gleaned from a fit of the peaks. A small feature occurs at ionization energies higher than the main line. In the spectra of both molecules, the smaller peak has an ionization energy of 404. 8 eV, which matches the reported N 1s ionization energy of HN(CH 3 ) 2  [23] ; significant amounts of dimethyl amine likely resulted from a background pressure of water in the chamber during the data collection. Electronic effects of the π-interaction between the Ti and N atoms extends even to C atoms on the amino ligand, as shown by the lower C 1s ionization energy for Ti((N(CH 3 ) 2 ) 4 (Table  4 ).
Acknowledgements The staff at Synchrotron SOLEIL are gratefully acknowledged for assistance during beamtimes. This work was supported by  Vetenskapsrådet  (Grant Nos. 2010-5080 ,  2011-4241  and  2017-03871 ) and by the  European Commission  through the  Marie Curie Initial Training Network SMALL  (Grant No. MCITN-238804 ). R. T. and M. M. K acknowledge support from  DOE NERSC  resources (Contract  DE-AC02-05CH11231 ) and  University of Maryland  supercomputing resources (http://hpcc. umd. edu). A. R. H. acknowledges support from the  Center for Functional Nanomaterials , which is a  U. S. DOE Office of Science Facility , at  Brookhaven National Laboratory  under Contract No. DE-SC0012704.
ACKNOWLEDGEMENTS The authors want to acknowledge  Eric Lebraud  (ICMCB) for XRD measurements. All the TEM observations were performed on the microscopes of the  Plateforme Aquitaine de Caractérisation des Matériaux  ( UMS 3626 ,  CNRS -Université de Bordeaux, Pessac, France ) and at the  Bordeaux Imaging Center  for some of the samples. P25 from Evonik was a generous gift from  Evonik Company  which is here strongly acknowledged. Financial support from the  China Scholarship Council (CSC) of the People's Republic of China  (grant to  Q. Bai ), the  French ANR program TITAMIUMS  ( PhD Grant  to  Q. Le Trequesser ) to the Idex and the  University of Bordeaux , and the  CNRS  are gratefully acknowledged. Supporting Information Available: (XRD patterns of TiO2 anatase NNS prepared at different initial pH values.
Nowadays, renewable energy production in Tahiti mostly comes from hydroelectric stations (25. 7% of the total energy mix in 2015) and from solar panels (4. 5 % in 2015). A solar map covering the whole world with 1 km resolution is made freely available at [4] by the World Bank Group. The solar radiation data is derived by Solargis algorithms from satellite images and auxiliary atmospheric datasets. In this paper, we use ground-based measurements of solar irradiation to assess the solar resource in Tahiti and compare them with satellite-derived values from the global solar atlas.
Dataset description The Kipp and Zonen CM11 model pyranometer is installed at the meteorological station of Faaa international airport on the north-western part of Tahiti. It should be mentioned that irradiance indicates the rate of solar energy arriving at the surface per unit time and unit area, in W. m -2 while irradiation refers to the amount of solar energy arriving at the surface during a given period of time, in J. m -2. Hourly totals of global horizontal irradiation (GHI) in J. cm -2 over the 2008-2017 period have been considered in this study. Reference time is the solar time. We have discarded the nighttime measurements and selected values from 08:00 to 17:00 local time to avoid the sunrise and sunset shifting issues. Also, the instrument has been installed far from any obstructing structure and the shading due to orography does not affect it from 08:00 to 17:00.
Direct normal irradiation In this subsection, we derive direct normal irradiation from global horizontal irradiation using the empirical model of  [5]. The authors developed an empirical correlation model using global and direct irradiance data registered at Ajaccio, France, between October 1983 and June 1985. Hourly values of the clearness index k t are used to estimate the direct transmittance k b , using the following relationship: The calculation of extra-terrestrial irradiance GHI 0 and DNI 0 is presented in section 2. Then, k t is derived using computed GHI 0 time series and GHI observed records. Finally, hourly DNI data is computed from k t thanks to the Louche et al relationship.
Computational Methods Periodic DFT calculations  [53, 54]  have been performed using the Vienna Ab initio Simulation Package (VASP)  [55, 56] , where the electronic wave functions have been expanded into plane waves up to a 450 eV cut-off energy. The semi-local Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional in the generalized gradient approximation (GGA) proposed by Perdew et al. [57]  was chosen. The projector augmented wave (PAW) method  [58, 59]  was used to describe the electron-ion interactions and the Kohn-Sham equations were solved selfconsistently until the energy difference between cycles decreases below 10 -6 eV. A Gaussian smearing of σ = 0. 1 eV was applied to band occupations in order to improve the total energy convergence. The atomic positions have been relaxed until all forces were smaller than 0.
Acknowledgments Youssef Berro would like to thank the  Lebanese University  and the  Lebanese National Council for Scientific Research CNRS-Lebanon  for funding his Ph. D. The authors gratefully acknowledge  Dr Anthony Dufour  ( CNRS, Université de Lorraine ) for fruitful discussions and LUE Mirabelle+/project  Lignin  and  PHC CEDRE Future Materials  for financial support. HPC p. 24 resources provided by  PMMS (Pôle Messin de Modélisation et de Simulation)  and  GENCI-CCRT/CINES  (Grants No. A0040910433  and No. A0060910433 ) are acknowledged.
Model constraints and spatial-based approach According to Almansoori and Shah  [14] , and Parker et al. [64] , the constraints for the HSCND model could be categorized according to capacity, flow conservation, time evolution, and nonnegativity constraints. Correctly involving constraints is an essential part of the modeling work. For instance, the trucking of liquids or the use of pipelines is the only option for hydrogen delivery larger than 500 kg/d to stations  [1]. The constraints imposed by national or regional policies and regulations should also be taken into consideration. For example, the pipeline network design should exhibit conformity to government regulations and planning. Similar to the work of Johnson and Ogden  [45] , pipeline design in southwestern California follows the existing pipeline rights-ofway as defined by the US National Pipeline Mapping System (NPMS) dataset.
Other examples abound. Kim and Kim  [60]  considered the importance of adhering to government regulations on land use in relation to energy production when attempting to model a wind-based HSCN. Several papers reported the importance of modeling within real geographic regions and utilized geographic information system (GIS) database in modeling  [18, 45, 64]. For instance, the road network employed by Parker et al. [64]  is the "California base" network from the California Department of Transportation, which consists of all interstates, major highways, and urban arterial roads. The IEA  [1]  argued that, hydrogen transportation and distribution are costly and strongly dependent on transport distance. Accordingly, a more robust T&D cost estimation can only be realistically provided if knowledge on geographical parameters is available.
Mono-objective problems Solution approaches for the mono-objective SCND problems can be classified into two categories: problems solved with a general purpose software (either commercial or non-commercial) and those solved with a specially tailored algorithm  [111]. Within each class, two further cases are identified: exact and heuristic solutions. The descriptions of these classifications are summarized in Table  13. All the studies in the reference papers are within the General solver category, either with the exact solution or heuristic solution. In 2010, Klibi et al. [110]  noted, that most static and deterministic models of the SCND could be solved efficiently with different versions of commercial solvers, such as the CPLEX, available at that time. This explains why all the HSCND models run the CPLEX solver, which is accessed via the GAMS modeling tool  [131].
Data collection The data-intensive nature of optimization methods makes the modeling work challenging and time-consuming. This situation is worse in hydrogen energy-related problems because of the dearth of historical data. A review of the data-related work reported in the reference papers is conducted. The review includes the data source (publisher), and data type (statistics, report, book, dataset, or website); it also identifies the part of the HSCN where these are used (demand, feedstock, production, storage, transportation, environment, or safety). It should be noted that journal papers are not considered as data sources here, although they are commonly used for this purpose. The focus is set on the external (or original) sources to make a reference list of the data sources for future research.
As shown in Fig. 10 , reports and statistics constitute a substantial portion of the data source. Reports from government agencies and international organizations, such as the U. S. Department of Energy, European Commission, the IEA, and Intergovernmental Panel on Climate Change provide substantial information on hydrogen demand, production, and T&D cost based on a broad range of techno-economic analysis. Statistics is another vital part of the data resource. The statistics on energy prices, road traffic, greenhouse gas emissions, population data, land use, and neighborhood are indispensable to the HSCND database. Figure  10  illustrates that data are more taining to the data source adopted by the studies reported in the reference papers can be found in the supplementary material.
Three models were used to represent the Au 1 / CeO 2 (111) catalyst (see Supplementary Fig. 12  for details). Two reaction pathways were investigated to explore the catalytic cycle in the absence (pathway A) or the presence (pathway B) of OHgroup taking part in the CO oxidation reaction. Generally the CO oxidation on ceria-supported metal NP catalyst is governed by a Mars-van-Krevelen (MvK) mechanism  26, 28, 39. With model A (Supplementary Fig. 12a ) the energy for O vacancy (O v ) formation is as low as 0. 13 eV without any reaction barrier (Supplementary Table  2 )  39  , resulting in ultrahigh activity. However, the calculated result with such a model does not fit well with our experimental data and the reported results, indicating that the bare ceria surface with a number of dangling bonds is too simplistic  40, 41.
9/d where D represents the dispersion and d is the average diameter of the Au NPs (in nm). Theoretical and computational details. All of the theoretical calculations were performed using periodic density functional theory (DFT) as implemented in the Vienna ab initio simulation package (VASP)  49, 50. The electron exchange and correlation energy was treated within the generalized gradient approximation in the Perdew-Burke-Ernzerhof formalism (GGA-PBE)  51. The electron-ionic interaction was described by the projector augmented wave (PAW) method  52, 53. Spin-polarized DFT + U calculations with a Hubbard correction value of U eff = 5. 0 eV for the Ce 4 f state were applied to correct the strong electron-correlation properties of CeO 2  54,55. The valence orbitals of Ce (4 f, 5 s, 6 s, 5p, 5d), Au (5d, 6 s), C (2 s, 2p), O (2 s, 2p), and H (1 s) were described by plane-wave basis sets with cutoff energies of 400 eV, whereas the Brillouin zone was sampled at the Γpoint.
Acknowledgements This work was supported by  National Natural Science Foundation of China  ( 21776270 ,  91645203 ,  21606222 ,  21590792 ,  21433005 ,  21606189 );  National Key Projects for Fundamental Research and Development of China  ( 2016YFA0202801 ,  2017YFA0700104 );  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDB17020000 ). Financial Grant from the  China Postdoctoral Science Foundation  ( 2017M621170 ,  2016M601350 ),  DICP Outstanding Postdoctoral Foundation  ( 2015YB08 ,  2017YB02 ), and dedicated funds for methanol conversion from  DICP. B. Q. acknowledges funding by  Arizona State University  and  DNL Cooperation Fund ,  CAS  ( 180403 ). S. D. and  J. Liu  acknowledge funding by the  National Science Foundation  under  CHE-1465057. J. Liu  acknowledges the use of facilities in the  John M.
ZECMIP analysis will draw on carbon cycle feedbacks and process understanding from C4MIP (Coupled Climate Carbon Cycle Model Intercomparison Project;  Jones et al. , 2016)  and aims to complement analysis on reversibility and CO2 removal under CDRMIP (Carbon Dioxide Removal Model Intercomparison Project;  Keller et al. , 2018). Both C4MIP and CDRMIP encourage participation in the ZECMIP top priority simulation. For simplicity the data request is a replica of that for the CMIP6 emission-driven historical simulation (esm-hist). No new variables have been added. For EMICs the request is to output the same model variables as from the 1% run which forms the basis of ZECMIP, with the one addition of also providing atmospheric CO2 concentration. Data can be published via the Earth System Grid Federation (ESGF) (for ESMs contributing to CMIP6).