dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement  Dr. Liudmila Novikova  would like to acknowledge the Coordinator of the  IMACS  project  Mrs Patricia Patrier  for affording a possibility to contribute to Erasmus Mundus IMACS program with a research project and a lecture course in Clay Science, and the  University of Poitiers, France , for granting a financial support for the research stay. Appendix A. Supplementary data.
Results The results are expressed in percent of the dry weight of the algae. As preliminary data, we performed a partial biochemical characterization of our dried samples of Sargassum muticum. Results have shown that the samples contained 33 ± 0. 2% ash, 2. 86 ± 0. 14% lipids and 7. 75 ± 0. 2% proteins (1. 44 ± 0. 036% nitrogen). The aminogram showed that half of the amino acids quantified were Glutamic acid (Glu), Alanine (Ala), Aspartic acid (Asp), Leucine (Leu) and Glycine (Gly). After alkaline extraction, the content of total polysaccharides was 6. 3 ± 0. 4%. The total extraction of phenolic compounds reached 2. 74 ± 0. 17%. These data have helped us comparing our samples of Sargassum muticum to other biochemical characterizations and to values obtained during the optimization of enzymatic hydrolysis.
Photoelectrochemical measurements. All PEC measurements were conducted in a 0. 5 M Na 2 SO 4 electrolyte using a three-electrode configuration with synthesized sample as the working electrodes, Pt and Ag/ AgCl electrodes being used as the counter and reference electrodes, respectively. The inter-electrode spacing was ~1 cm. Photocurrents were recorded using a PCI4/300 ™ potentiostat equipped with PHE200 ™ software (Gamry Instruments, Inc. ). The working electrodes were exposed to the AM 1. 5 light from a solar simulator equipped with a 300 W Xe-lamp (HAL-320, Asahi Spectra Co. , Ltd. ). The incident light intensity was 100 mW•cm -2 and the sample illumination area 0. 28 cm 2. Linear sweep voltammetry (LSV) was carried out under both dark and illumination conditions with a scan rate of 5 mV•s -1 with chopped light irradiation (frequency = 0.
). Potentiostatic mode was applied under white light illumination (AM1. 5, 100 mW•cm -2 ) at an applied voltage of -0. 2 V vs. Ag/AgCl. AC perturbations of amplitude 5 mV were superimposed with frequency in the range 0. 01-100 kHz. Equivalent circuit modeling and curve fitting were performed using the Echem Analyst ™ software (Gamry Instruments, Inc. ) Hydrogen production. The amount of hydrogen generated was measured using a well-sealed glass cell (100 ml) mounted with a quartz window. The Cu 2 O and Cu 2 O/NiFe-LDH working electrodes (sample illumination area = 1 cm 2 ) were exposed to the light of a solar simulator equipped with a 300 W Xe-lamp (HAL-320, Asahi Spectra Co. , Ltd. ), and the incident AM 1. 5 light intensity was 100 mW•cm -2. The working electrode, Pt counter-electrode and Ag/AgCl reference electrode were suspended in a solution (pH = 7) containing 30 ml of 0.
Modelling and Prediction Study We evaluated the potential of alternative solvents for the extraction of oil from Yarrowia lipolytica IFP29. This study was conducted with predictive software COSMO-RS (conductor-like screening model for realistic solvation) and Hansen solubility parameters. The theoretical predictions were compared to the practical results for fatty acid profiles and lipid classes. To conduct a predictive study, we have to know the most abundant molecules (Figure  5 ) in oil from Yarrowia Lipolytica IFP29. HPTLC results show that the main classes of lipids present in the oil were TAG, DAG and FFA. GC analysis showed that these lipids were composed of long carbon chains, such as C16, C18:1n9 and C18:2n6. The main components were modelled by the ChemSketch software and used for the predictive study.
To conduct a predictive study, we have to know the most abundant molecules (Figure  5 ) in oil from Yarrowia Lipolytica IFP29. HPTLC results show that the main classes of lipids present in the oil were TAG, DAG and FFA. GC analysis showed that these lipids were composed of long carbon chains, such as C16, C18:1n9 and C18:2n6. The main components were modelled by the ChemSketch software and used for the predictive study.
Additionally, the second eluent was a mixture of n-hexane/diethyl ether/glacial acetic acid in a ratio of 70:30:2 v/v/v to a height of 7 cm from the origin. After being dried, the plate was dipped for 6 seconds in a reagent (10 mg of primuline, 160 mL of acetone, 40 mL of water), then scanned using a TLC Scanner 3 with WinCATs software (CAMAG). Lipid classes were identified and quantified against those of corresponding lipid standards. After extraction and separation of the debris, the solvents having a lower boiling point above 100 ˝C, such as hexane, isopropanol, ethanol, ethyl acetate, MeTHF and CPME, were evaporated under reduced pressure. Other solvents, such as p-cymene, d-Limonene and α-pinene, possessing a boiling point around 150 ˝C, are evaporated another way. The conventional method of evaporation under pressure is not possible for these solvents with high boiling.
Additionally, the second eluent was a mixture of n-hexane/diethyl ether/glacial acetic acid in a ratio of 70:30:2 v/v/v to a height of 7 cm from the origin. After being dried, the plate was dipped for 6 seconds in a reagent (10 mg of primuline, 160 mL of acetone, 40 mL of water), then scanned using a TLC Scanner 3 with WinCATs software (CAMAG). Lipid classes were identified and quantified against those of corresponding lipid standards.
3 (GitHub Pages, San Francisco, CA, USA) to their simplified molecular input line entry syntax (SMILES) notations, which were then used to calculate the solubility parameters of various alternative solvents and constituents in extracted microbial oil. Using HSP software, the relative energy difference (RED) (Equation (  2 )) number can be calculated to determine whether the alternative solvent and the solute are miscible: RED " Ra{Rb ( 2 ) where Rb is the radius of a Hansen solubility sphere and Ra is the distance of a solvent located inside the Hansen solubility sphere. According to the classical "like to like" rule, the smaller Ra, the greater the affinity between solute and solvent  [11]. This means that a potentially good solvent has an RED number smaller than 1, while unsuitable solvents have an RED number greater than 1.
The (3D) polarization density distribution on the surface of each molecule is converted into a distribution function, the "σ-profile", which gives the relative amount of surface with polarity σ on the surface of the molecule. Blue is used to represent strongly positive polar regions, and red represents very negative polar surfaces. Green and yellow correspond to lower polarity (Figure  8 )  [15]. The chemical structures of the solvents and solutes discussed here were transformed by JChemPaint Version 3. 3 (GitHub Pages, San Francisco, CA, USA) to their simplified molecular input line entry syntax (SMILES) notations, which were then used to calculate the solubility parameters of various alternative solvents and constituents in extracted microbial oil. Using HSP software, the relative energy difference (RED) (Equation (  2 )) number can be calculated to determine whether the alternative solvent and the solute are miscible: RED = Ra/Rb ( 2 ) where Rb is the radius of a Hansen solubility sphere and Ra is the distance of a solvent located inside the Hansen solubility sphere.
Ethyl acetate, CPME and MeTHF could replace hexane for the solubilization of microbial oils. Economically and technically, these solvents have properties similar to those of hexane and more specifically for the energy required to evaporate 1 kg of solvent and carbon footprint. By contrast, theoretical and economic studies show that d-Limonene, p-cymene, α-pinene, ethyl lactate, IPA and ethanol are not suitable for replacing hexane, even for the solubility of oil, classes of lipids or for technical and economic reasons. Acknowledgments: This study was funded by  French Government Investissement d'Avenir   ANR PROBIO3 (2012-2020 ). Cassandra Breil thanks the  FIDOP/FASO funds (Fonds d'Action Stratégique des Oléagineux ) from the  French  vegetable oil and protein production industry for her doctoral grant.
Growth measurement The microalgae growth was determined by estimating cell concentration from the absorbance at a 680 nm (i. e. , A 680 ) measured with a spectrometer (T60-UV-Visible Spectrometer, UK). After cultivation, cell pellets were obtained by centrifugation at 4500 × g for 10 min at late exponential phase. Pellets were then dried at 105 °C until their weight kept constant. The dried microalgae were weighed to determine their dry biomass weight (DW). Data of growth in Box-Behnken experiments were presented as mean with standard deviation (±SD).
Experimental design and analysis of the response surface The level of the significant factors and the interaction effects between culture conditions which influence cells abundance and lipid production were analyzed and optimized by Box-Behnken methodology  [43]. In this study, the experimental design contained 15 trials for optimizing culture conditions and the independent variables were studied at three levels, which are low (-1), medium (0) and high (+1) (Table  1 )  [44]. The growth (Y 1 ) and lipid content (Y 2 ) were taken as the response values of the design. The different factor levels and response values are shown in Table  1  and Table 2 , respectively. The optimal values were derived from the result analysis by using the NemrowdW Software (LPRAI, Marseille)  [45]. The experimental data obtained from the Box-Behnken model experiments can be represented by the following quadratic polynomial Eq.
Flow cytometry Counting and characterization of the selected strain were analyzed by FCM using a LSR flow cytometer (Becton Dickinson Biosciences) equipped with a 488 nm argon laser. The sample cells were entrained in the core of a sheath fluid so that they were individually intercepted by the laser beam, generating scatter and fluorescence signals. The forward scatter signal is related to the cell size and the sideward scatter signal to the cell structure and granularity. In addition to the scatter signals, two fluorescence signals were recorded, namely that of NR orange fluorescence (λExcitation/λEmission; 526/575 nm) after cell-lipid staining, and red fluorescence of chlorophyll a (λExcitation/Emission 613/682 nm). The LSR flow cytometer was under the control of the Cell Quest Pro software (Becton Dickinson, UK) that was also used for data recording.
Culture conditions optimization of cell abundance and lipid production by V 2 strain Table  2  shows the results of different combinations of three factors (salinity, light intensity and pH) chosen for optimizing V 2 strain cell abundance and lipid production. Optimal culture conditions and statistical analysis of Box-Behnken Design results were further explored using NemrowdW Software (Table  2 ). After identification of factors affecting cell growth and lipid production, the experimental data were fitted by the following Eqs. (  2 ) and (3), second order polynomials established by multiple regression analysis: Y 1 ¼ 0:697þ0:042X 2 -0:02X 3 -0:087X 1 2 -0:092X 2 2 -0:067X 3 2 -0:037X 1 X 2 þ 0:028X 2 X 3 ð2Þ Y 2 ¼ 2329:760 þ 469:564X 1 þ 286:552X 2 -642:012X 1 2 þ 295:337X 1 X 2 ð3Þ Where Y 1 is the predicted cell abundance; Y 2 is the predicted lipid content; X 1 is salinity; X 2 is light intensity and X 3 is pH.
Acknowledgements Funding for this work was provided by  NSERC of Canada  ( Discovery Grant ) the  Canada Research Chair  secretariat (Tier I  CRC 2013-2020 ) to W. E. P. , the  National Science Foundation  ( CHE-1454370 ) to M. L. N. and by an  Alfred P. Sloan Research Fellowship  to M. L. N. The  Alexander von Humboldt Foundation  (W. E. P. and L. M. ) and the  Chinese academy of Science  (L. M. ) are acknowledged for nancial support. L. M. is member of the  Institut Universitaire de France. L. M. and W. P. also acknowledge  CNRS  for a bilateral grant through the  PICS program. CalMip is acknowledeged for a generous grant of computing time. D. M. S. thanks the  University of Calgary  for an  Eyes High Postdoctoral Fellowship. Prof. Hans-Jörg Himmel  of the  Ruprecht-Karls-Universität Heidelberg  is acknowledged for helpful discussions.
Briefly, a phylogenomic reconstruction of the ST09 samples was computed by using both the phylogenetic information contained in the SEED nr database and the similarities to the ribosomal RNA database  (Meyer et al. , 2008). The PHF metagenomes (P27 and P28) were screened for hydA genes encoding the large subunit of the [FeFe]-hydrogenase. The amino acid sequences of HydA proteins (pfam02906) were retrieved from the NCBI protein database (in May 2014) and used locally as a specific database for similarity searches using PHF metagenomes as query with BLASTX tool (version 2. 2. 25+) with default algorithm parameters and an E-value cutoff of 10 -5. The merged paired-end reads related to hydA genes with significant hits were then extracted from MG-RAST, aligned and then clustered into OTUs using an 80% identity threshold with UCLUST algorithm.
Nonchimeric 16S rRNA gene sequences (checked with the online Bellerophon program) and translated hydA sequences were aligned using Muscle implemented in the MEGA6 software  (Tamura et al. , 2013). The program mothur was used to group sequences into operational taxonomic units (OTUs) based on 97% identity for 16S rRNA genes and 80% identity for HydA sequences  (Baba et al. , 2014). Mothur was also used to estimate richness  (Chao, 1984)  and to compute diversity indices  (Shannon and Weaver, 1949; Simpson, 1949)  for each hydA clone library. The Good's coverage C of each clone library was calculated according to the equation: C = 1-(n/N) where n is the number of OTU and N is the total number of clones in the library  (Good, 1953). At least one HydA sequence deriving from translated hydA sequence and representing each OTU, designed as phylotype was further aligned using Muscle with related sequences retrieved from NCBI databases using BLAST tools.
The MEGA6 software was also used for phylogenetic tree construction by the Maximum Likelihood method using bootstrap analysis on 1000 replicates  (Felsenstein, 1985). The hydA gene and HydA sequences from environmental samples were deposited in the Genbank database under the accession numbers KT357617-KT357637. The 16S rRNA genes from bacterial cultures were deposited in the Genbank database under the accession numbers KR349722 (3b), KJ626326 (PROH2), and KR349723 (BJ2).
Pyrosequencing Analyses of Bacterial 16S rRNA Gene Fragments The mixtures of 16S rRNA gene amplicons were generated from a 341F/815R bacterial primer set, as previously described by  Dowd et al. (2008) , and were sequenced on a 454 GS-FLX Titanium sequencer (Roche Life Sciences, USA) by the Molecular Research Laboratory (Texas, USA). Raw sequence data generated by pyrosequencing were uploaded into QIIME 1. 8. 0 and processed as described by  Caporaso et al. (2010). Briefly, sequences were qualitatively trimmed, aligned using Pynast and clustered into operational taxonomic units (OTUs) using a 97% identity threshold with UCLUST  (Edgar, 2010). Taxonomic assignment was carried out with the RDP Classifier  (Wang et al. , 2007)  with a minimum bootstrap confidence of 80%. BLAST searches against a non-redundant nucleotide database were performed for a representative sequence of each OTU.
The [FeFe]-hydrogenase sequences from PHF samples are marked in bold. Each OTU is represented by one representative sequence (at ≥80% similarity level). Percentage of OTU with respect to the total number of retrieved sequences in each PHF site is indicated by different levels of red from 0% (white) to 100% (red). Genbank accession numbers (in brackets) were obtained from the protein sequence database. Bootstrap values <70% are not shown. The scale bar indicates 0. 1% sequence divergence. UnBact and α_Prot stand for unclassified Bacteria and Alphaproteobacteria, respectively. b Environmental sequences obtained in previous studies of the Prony Hydrothermal field (New Caledonia;  Quéméneur et al. , 2014; Postec et al. , 2015). Different colors indicate different bacterial taxa (classes or phyla).
and RKC subcultures (after 7 days of incubation). All H 2producing subcultures (BdJC, RKC, and ST07) displayed before the isolation procedure, a weak bacterial richness with the detection of only one dominant OTU belonging to Firmicutes after 16S rRNA-based cloning/sequencing analyses (data not shown). These dominant OTUs corresponded to the bacterial strains finally isolated by the roll-tube method from each H 2producing subcultures. As shown in Figure  6B , the alkaliphilic, highly efficient H 2 -producing ST07 culture was dominated by the strain PROH2 (sharing 99. 9 and 96. 8% 16S rRNA identity with Acetoanaerobium pronyense and Clostridium sticklandii, respectively;  Mei et al. , 2014; Bes et al. , 2015). The RKC cultures were dominated by the alkaliphilic strain 3b having Alkaliphilus hydrothermalis as its closest phylogenetic relative (92.
, 2015) , despite their abundance in both 16S rRNA and dsrB gene databases (e. g. , Desulfotomaculum spp. for LCHF and Desulfonatronum spp. for PHF). However, it is well-known that the use of H 2 as electron donor is generally considered as more thermodynamically favorable than use of organic acids or sugars as electron donor  (Thauer et al. , 1977; Amend et al. , 2011). This probably means that such sulfate-reducing bacteria have a particular metabolism (and/or a synthrophic life style), and consequently may possess a novel type of hydrogenases. Altogether, these results indicate that Desulfotomaculum-related [Fe-Fe]-hydrogenases certainly play a crucial role in the biological H 2 cycle in serpentinite-hosted environments but at this stage of knowledge it is difficult to stand if these [Fe-Fe]-hydrogenases were involved mostly in H 2 production or consumption or both depending on environmental conditions.
ACKNOWLEDGMENTS This project was financially supported by the  French Research Institute for Development (IRD) , the  EC2CO-Biohefect/Ecodyn/Dril/MicrobiEn (MicroProny) CNRS program , the deepOASES  ANR  project ( ANR-14-CE01-0008-06 ), the  Deep Carbon Observatory (Census for Deep Life : Comparative metagenomics of archaeal biofilms in carbonate chimneys associated with geographically isolated sites of serpentinization; PI W. J. Brazelton ) and the  China Scholarship Council. We acknowledge the support given by the  Deep Life Community  from the  Alfred P. Sloan Foundation  to  M. L. Sogin  and  K. Hinrichs. We sincerely thank  M. O. Schrenk  and  W. J. Brazelton  for their helpful reading and comments on the manuscript. We are grateful to the scuba divers team of  IRD Nouméa  ( Eric Folcher ,  John Butcher , and  Bertrand Bourgeois ) for collecting samples.
A previously described 3D FEM model implemented for homogeneously distributed MIEC cubes on CGO electrolyte system  (16, 17) , is adopted. To extend the approach to a composite system, a fraction of the cubes is assigned to CGO in the cathodic compartment. The model includes the following processes as illustrated in Figure  3:  1. Surface exchange process at the surface of LSCF in defined boundaries, 2. Bulk diffusion of reduced oxygen ions within the LSCF lattice, 3. Charge transfer at the LSCF(electrode)/CGO(electrolyte) and LSCF(electrode)/CGO(electrode) interfaces, 4. Ionic conduction in CGO (electrode and electrolyte). COMSOL Multiphysics FEM software was used to solve the two coupled partial differential equations governing bulk O 2-diffusion within LSCF and CGO ionic conduction. A number of assumptions were made in the model.
Limitations of the current reserve and resource estimates [28] 39, 44, [47] [48] [49] [50] [51]  The lack of updated, transparent and robust estimates at the global level is particularly problematic in the case of coal. In fact, only two original datasets exist at the global level: the Federal Institute for Geosciences and Natural Resources (BGR)  52  and the World Energy Council (WEC) 53 assessments. The BGR assessment reports the total coal in-place rather than an estimate of the resources that can be recovered, and the WEC estimates of resources and reserves are mainly data collected from its member countries, and hence do not address the limitations outlined above. Actually, recent studies have pointed out that coal reserve/resource estimates suffer from a combination of weaknesses such as outdated data and methods and a considerable heterogeneity in methods applied across different regions hampering robust global aggregations,  [23] [24] [25] [47] [48] [49]  suggesting that common coal reserve/resource estimates might be substantial overestimates, especially in some key regions.
Thus, we used Dale's (2012)  40  dataset, which is the result from a meta-analysis covering estimates made up to 2012. This dataset collates estimates of recoverable resources from diverse sources such as national and international agencies (e. g. USGS, WEC, BGR, IEA, and IIASA), independent associations (e. g. ASPO and EWG), industry (e. g. ESSO, Shell, and International Gas Union) and scientific peer-reviewed studies. The meta-analysis was performed as follows: ‡ ‡ compilation of original estimates (avoiding double-counting) through a literature review; updates counted as different estimates; if an estimate was provided by a high-low span, the mean was taken; if an estimate was characterised using a high-middle-low range, the middle value was taken; where estimates for subtypes were provided (e.
g. sub-bituminous, bituminous and lignite for coal), these were aggregated based on heating values. The objective of the metaanalysis was to determine the distribution in estimates, not to provide an in-depth analysis of the differences in assumptions leading to the discrepancy in estimates. Thus, no estimates were discarded for the construction of the original dataset. For our analysis we eliminated URR estimates with values less than the current cumulative production. The number of estimates that needed to be removed was less than 5% of the estimates from the original dataset for each of the fuels. The final dataset contains a total of 39 estimates for coal, 191 for conventional oil and 68 for conventional gas. Hence, since we have a large set of studies with no preference of any particular one, we consider the estimates to be equally probable.
Therefore, in the Monte Carlo analysis we randomly sample an entry from the dataset, which is equivalent to random sampling from the corresponding empirical cumulative distribution function (ECDF). The resulting ECDF for coal, conventional oil and conventional gas is in agreement with a recent review of the RURR of fossil fuels performed by Mohr et al. , (2015)  17  (see Table  S2 , ESI †). The situation is very different for unconventional oil and gas, since a fraction of these fuels has only recently become economically profitable (e. g. shale oil and gas) and extraction techniques for some resources are still under research & development (R&D) (e. g. methane hydrates). As a result, few published estimates exist with large associated uncertainties. 12, 17, 19, 20, 39, 40 This implies that the existing sample of RURR estimates is too small and cannot be properly used for an uncertainty analysis.
Depending on the shape, the future technology competition will evolve differently. With a greater steepness (e. g. logistic shape), substitution processes by other fuels and/or technologies will be boosted. ] [73]  Specifically, three main patterns were identified and integrated   7. 2 from the IPCC-AR5 3 (see also Table  S2 , ESI †). For conventional oil, conventional gas and coal RURRs, the ranges of the interval represent the minimum and the maximum estimate of the ECDF derived from Dale (2012)'s dataset. 40 For unconventional gas and unconventional oil RURRs the interval is bounded by the ''low'' and ''high'' estimate from Mohr et al. (2015),  17  while the median is represented by their ''best guess'' estimate. For comparison, the total cumulative carbon emissions from fossil fuel combustion to date are around 350 GtC.