dataesr/scientific-paragraphs-categorization huggingface dataset:
Oxygen Reduction Reaction Initially, the ORR electrocatalytic performances of MWCNT_N8, GF_N8, GF_ND8, and GF_NS8 were assessed by CV in KOH saturated with nitrogen and oxygen. The CVs of the four doped-carbon nanomaterials are presented in Figure  S9 , where it can be clearly observed that in nitrogen-saturated electrolyte, no peak can be detected, whereas in oxygen-saturated electrolyte, all doped-carbon materials present an irreversible reduction peak, corresponding to oxygen reduction, at E pc = 0. 79, 0. 78, 0. 77, and 0. 70 V vs. RHE for MWCNT_N8, GF_N8, GF_ND8, and GF_NS8, respectively. Pt/C, pristine MWCNT, and GF were also evaluated in the same experimental conditions presenting the ORR peak at E pc = 0. 55, 0. 64, and 0. 86 V, respectively (data not shown). Further evaluation of the ORR electrocatalytic performances of the prepared doped-CM were conducted by LSV in KOH saturated in both N 2 and O 2.
Acknowledgments: DF acknowledge the  FCT/MCTES  by the work contracts (in the scope of the framework contract foreseen in the numbers 4, 5, and 6 of article 23, of the Decree-Law  57/2016 , of August 29, changed by Law  57/2017 , of July 19) supported by national funds (OE). NL also thanks SERP+ programme and  Erasmus Mundus  for her grant.
The maps show that Ti, N and O are present all along the tree-like structure. The measured elemental composition was Ti = 43. 4% at. , N = 23. 5% at. , and O = 33. 1% at. , with N/Ti = 0. 54 in agreement with EDX microanalysis. Further information on the composition of this film was gained by XPS. The spectra revealed a rather low nitrogen amount (~ 10% at. ), while the binding energies of the peaks in the Ti 2p and O 1s regions were comparable to reduced titanium dioxide (see details in Figure  S2  and Table  S1 ). Therefore, the XPS data overall suggested an extensive surface oxidation for the film deposited at 100 Pa. It is possible to conclude that, while the films deposited at P ≤ 20 Pa are consistent with titanium nitride materials with different stoichiometries, i. e. , TiNx, and limited (surface) oxidation, those deposited at P ≥ 50 Pa correspond to titanium oxynitride materials, i.
Acknowledgments L. M. and A. N. acknowledge support of the  Ministry of Education, Youth and Sports of the Czech Republic  through the  Operational Programme Research, Development and Education -European Regional Development Fund , project no. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000416 , and the  Czech Science Foundation (GACR)  through the project no. 20-17636S. The authors also thank  Ondřej Tomanec  for TEM and EDS measurements,  Seyyedmohammadhossein Hejazi  for XRD measurements,  Veronika Šedajová  for XPS measurements, and  Filip Münz  for his aid with FTIR measurements. The authors acknowledge CzechNanoLab Research Infrastructure supported by  MEYS CR  ( LM2018110 ).
Various studies show that these hydrocarbons resources are still very influential tools of foreign policy, despite two very disruptive trends occurring in the international energy market in recent years: the so-called 'shale revolution' and the global dynamics of energy transition towards decarbonized systems  [3, 4]. ☆ The authors did not receive any funding for this research and declare that there is no conflict of interest. After completion of the research, Open Access funding was provided by the Qatar National Library as the first author of this article is a faculty at the Doha Institute for Graduate Studies, a research and higher-education institution based in the State of Qatar. Over the past decade, there has been a major rise in the nonconventional (or "shale") gas produced in, and exported from, the United States.
Methodology In order to analyze Europe's new gas policy and geopolitics following the 2022 Russian invasion of Ukraine, as well as the real capacity of Europe to diversify its gas importations in the short and medium terms, the authors proceeded to an extensive desk research of energy databases and industry gray literature, particularly focusing on current industrial capacities and gas exporting countries' contractual engagements and growth strategies. In parallel, a set of fifteen semi-structured interviews of natural gas stakeholders, conducted between 20th April 2022 and 15th May 2022, provided much informal insight and analytical information about the gas industry and its ongoing dynamics. All interviewees had a strong expertise in the natural gas industry, with interviewees being based in the Asia Pacific (4 persons), the Middle East  (5) , and Europe  (6).
Electrochemical Properties The electrochemical behaviors of PbO2 anodes were characterized by electrochemical impedance spectroscopy (EIS). EIS measurements were carried out in a conventional three-electrode cell system containing 0. 1 M Na2SO4 using a VoltaLab PGZ301 potentiostat. PbO2 anodes were used as the working electrodes, platinum wire was used as the counter electrode, and a saturated calomel electrode was used as the reference electrode. Impedance spectra were recorded in a frequency range varying from 100 kHz to 100 mHz. The experimental data were fitted using ZsimpWin 3. 2 software.
Electrochemical Properties The electrochemical behaviors of PbO 2 anodes were characterized by electrochemical impedance spectroscopy (EIS). EIS measurements were carried out in a conventional threeelectrode cell system containing 0. 1 M Na 2 SO 4 using a VoltaLab PGZ301 potentiostat. PbO 2 anodes were used as the working electrodes, platinum wire was used as the counter electrode, and a saturated calomel electrode was used as the reference electrode. Impedance spectra were recorded in a frequency range varying from 100 kHz to 100 mHz. The experimental data were fitted using ZsimpWin 3. 2 software.
EIS Measurements and Fitting EIS measurements were performed to further investigate the effect of the TiO 2 interlayer and B dopant on the electrochemical properties of PbO 2 anodes. The EIS parameters listed in Table  2  were obtained from the collected experimental data fitted with the equivalent circuit (Figure  7 ) where R s corresponds to the solution resistance, and (R coating , Q coating ) and (R ct , Q dl ) characterize, respectively, the properties of the coating and the charge transfer process at the interface electrode/electrolyte. The Warburg impedance (W) is assigned to the diffusion of charged species  [12]. It can be seen from the Nyquist plots displayed in Figure  8  that SS/PbO2 (Figure  8a ) and SS/TiO2/PbO2-10%B (Figure  8c ) electrodes present straight lines while SS/TiO2/PbO2 impedance spectra (Figure  8b ) present a straight line with the appearance of a semi-circular shape at a high-frequency range attributed to the fast charge transfer at the anode interface  [52].
Phylogenetic analysis For the phylogenetic analysis, we chose couples of Pcc2 and Pcc1 paralogs from one representative of 27 phyla of Archaea covering the entire archaeal diversity. Protein alignment was done with MAFFT V7 with the amino acid matrix Blosum 30 and iterative refinement methods L-INS-I or G-INS-I  (42). The trimming was performed with BMGE with a BLOSUM30 matrix and the -b 1 parameter  (43). The Maximum likelihood trees were constructed using the IQ-TREE v1. 7 software (http://www. iqtree. org/)  (44)  with the best substitution model as suggested by the ModelFinder option -MFP. The branch robustness was estimated by the SH-aLRT approximate likelihood ratio test  (45) , the ultrafast bootstrap approximation  (46)  (10000 replicates for each), and with the transfer bootstrap expectation approaches  (47)  based on 500 boot trees.
Sequences of Gon7 orthologs from Metazoa (ENOG503BUEY, 60 sequences) and Fungi (ENOG503P560, 32 sequences) were retrieved from EggNOG database 5. 0. 0 (48) which harbours sequences selected for diversity and filtered by genome quality. Sequences were aligned with the T-coffee webserver  (49)  and the tree was inferred using IQ-TREE v1. 7 software using automatic selection of sequence evolution model.
Structure solution and refinement X-ray diffraction data collection was carried out at Synchrotron SOLEIL on beamline Proxima 2 (Saint Aubin, France) at 100K. Data were processed, integrated and scaled with the XDS software package  (50). The PaPcc2 crystals belong to space group C121 with unit cell a=76. 80Å b =81. 31Å c=66. 48Å and =90° =121. 59° =90°. The structure of PaPcc2 was solved by molecular replacement using PHASER  (51)  implemented in the CCP4 suite. A search model for PaPcc2 was obtained by MODELLER (52) using the known structure of PfPcc1 (PDB 3ENC). 3 copies of PaPcc1 were found in the asymmetric unit of the crystal. The initial structure was refined with BUSTER and interactively manually adjusted with COOT  (53). The PaPcc1-PaPcc2 crystals belong to space group P32 21 with parameters of unit cell of a=69.
SEC-SAXS analysis SAXS experiments were carried out on the SWING beamline at SOLEIL synchrotron (Saint-Aubin, France). The sample to detector (Eiger 4M Dectris) distance was set to 1500 mm, allowing reliable data collection over the momentum transfer range 0. 005 Å-1 < q < 0. 5 Å-1 with q = 4πsin θ/λ, where 2θ is the scattering angle and λ is the wavelength of the X-rays (λ = 1. 0 Å). To collect data on homogenous protein samples, SAXS data were collected at the exit of a size exclusion high-performance liquid chromatography (SEC HPLC Bio-SEC3 Agilent) column directly connected to the SAXS measuring cell. 65 µL of PaCgi121/PaBud32/PaKae1/PaPcc1/PaPcc2 (CBKP1P2), CBKP1, and CBKP2 samples concentrated at 2. 55, 0. 72 and 3 mg/ml, respectively, were loaded into the column equilibrated with 20 mM Tris pH 8.
5, 300 mM NaCl, and 5 mM 2-mercaptoethanol and 5 % glycerol. Flow rate was set at 300 μL/min, frame duration was 1. 0 s, and the dead time between frames was 0. 01 s. The protein concentration was estimated by UV absorption measurement at 280 and 295 nm using a spectrophotometer located immediately upstream of the SAXS measuring cell. A large number of frames were collected before the void volume and averaged to account for buffer scattering. SAXS data were normalized to the intensity of the incident beam and background (i. e. the running buffer) was subtracted using the program FoxTrot50, the Swing in-house software. The scattered intensities were displayed on an absolute scale using the scattering by water. Identical frames under the main elution peak were selected and averaged for further analysis.
Modelling of the complete KEOPS complex The PaKEOPS complexes were modelled using a combination of structures of archaeal KEOPS subcomplexes and of the present PaPcc1-PaPcc2 structure by the Modeller software  (55). The Dadimodo  (56)  and FoxsDock (57) programs were then used to adjust the models of CBKP1P2, CBKP1 and CBKP2 to optimally fit the experimental curve I(q). The final adjustment was performed using the program CRYSOL  (58).
Using HMM profile searches derived from representative alignment of Gon7 or C14 orthologs we only identified Gon7 sequences in two closely related plants Carpinus fangiana (hornbeam) and Quercus suber (cork oak) belonging to the Fagales order (Supplementary figure  10 ). The phylogenetic analysis placed these sequences within the fungal Gon7 orthologs suggesting that these are not genuine plant Gon7 orthologs but rather genes that were acquired by horizontal transfer from fungi. Overall, the data indicate that Pcc2 proteins are specific for Archaea and that Gon7/C14 are either highly divergent orthologs of Pcc2 or a result of convergent evolution whereby opisthokonta had recruited a distinct gene to fulfill a function in regulating the oligomeric state of KEOPS complex.
TK0642) both annotated as KEOPS subunit Pcc1 in the NCBI database. TK0642 encoded an 82 aa protein (UniProt Q5JF86) while TK1253 encoded an 85 aa protein (UniProt Q5JGMS) (Supplementary figure1A). The two proteins displayed rather low pairwise sequence identity (22 %) but high sequence similarity (55 %). The alignment of their sequences with the previously described Pcc1 from Pyrococcus furiosus (PfPcc1) (33)  showed that TK0642 is more similar to PfPcc1 (55 % sequence identity versus 29 % sequence identity for TK1253) (Supplementary figure1B). This suggested that TK0642 and TK1253 are paralogs derived from a gene duplication event whereby TK0642 encodes the canonical Pcc1 protein. We named the product of the gene TK1253 Pcc2 to underline the evolutionary link with Pcc1 protein family. Next, we set out to investigate the distribution of Pcc2 among archaea using HMM-based sequence similarity searches.
64 ( 64 complexes starting from high resolution structures of subcomplexes (Kae1-Pcc1, PDB 3ENC; Kae1-Bud32, PDB 3EN9, Bud32-Cgi121, PDB 4WW9) and our present PaPcc1-Pcc2 and PaPcc2-Pcc2 structures. The initial model of the octameric CBKP1 (39)  resulted in a few slight steric clashes which could be relieved using the FoxsDock software, a method for protein docking against SAXS profiles. The theoretical SAXS curve of the adjusted CBKP1 octamer model fitted very well to the experimental data with a χ 2 value of 1. 39 (Supplementary figure6A). The CBKP1 octamer adopts a V-like shape, in line with the experimental maximal extension value of about 188 Å (Supplementary figure 6A, middle panel). The Kae1-Pcc1 interface in the model octamer is slightly twisted compared to that in the Kae1-Pcc1 structure (PDB code 3ENO) but we modelled it as the interface of OSGEP-LAGE3 (PDB code 6GWJ).
Simulation and experimental methods Simulation work has been carried out using the TracePro, which is Fig. 1. A general view of an optical system. ray-tracing simulation software based on the Monte Carlo method. All the prototypes were simulated in the same conditions using the standard spectral correlation parameters AM1. 5D and solar radiation set to 1000W/m 2. The output of the simulation consists of optical power values as function of the incidence angle and spatial position in a plane perpendicular to the beam propagation that are then used to compute the different characteristics defined above. All the simulations have been performed several times by changing the total number of the incident rays (⩾10 5 rays), and an errors estimation using a statistical approach was performed. The simulated electrical efficiency η elec of a prototype is obtained by the multiplication of its optical efficiency η opt by the efficiency of the used solar cell.
Electrical performances of the designed photovoltaic concentrator After the optical characterization, the prototype are know coupled with the solar cell and the electrical, measurement are performed as both described in subSection 2. 3. Figs. 7(a)-(c ) show typical I-V curves recorded when the solar cell is coupled to the P 1 , P 2 , and P 3 prototypes, respectively and for different DNI. A large dependence of the I-V curves with the DNI is clearly seen. Fig. 7(d)  shows the P-V curves that can be derived from the I-V data recorded at DNI values of 523W/m 2 for P 1 and P 3 and 348W/m 2 for P 2. The P-V curve peaks at a voltage of 2. 5V and 2. 3V for P 2 and P 3 , and P 1 , respectively. Although measured for a much lower DNI, P 2 prototype exhibits the highest peak power. From this set of I-V data, electrical parameters such as short circuit current (I sc ), open circuit voltage (V oc ), maximum power (P m ), fill factor (FF), and electric efficiency (η elec ) have been computed and gathered in Table  3.
Total RNA extraction and purification of the eight samples (2 strains × 2 salinities × 2 replicates) was carried out using Nucleozol® following Macherey Nagel's protocol, and whole genomic DNA was isolated according to the phenol-chloroform purification and ethanol precipitation method of  Sambrook et al. (1989). Library construction (TruSeq RNA Library Preparation kit for RNA-seq and Swift Bioscience Accel-NGS Methyl-Seq DNA library Kit for WGB-seq) and high-throughput sequencing steps (Paired-End (PE) 2 × 150 bp, Illumina® HiSeq®) were performed by Genewiz (Leipzig, Germany). We then performed all the bioinformatic preprocessing analyses with publicly available software implemented in the European UseGalaxy server  (Afgan et al. 2018).
Gene expression analyses The RNA-seq raw reads were checked for quality using FastQC version 0. 72  (Andrews 2010)  and subjected to adapter trimming and quality filtering using Trim Galore! version 0. 4. 3. 1  (Krueger 2015). Additional 12 bp and 3 bp were also removed at the 5' and 3' extremity, respectively, to avoid bias not directly related to adapter sequences or basecall quality according to FastQC outputs, and only reads with a minimum length of 50 bp were retained. We aligned the trimmed reads on the D. salina CCAP 19/18 reference nuclear (Dunsal1 v. 2, GenBank accession: GCA_002284615. 2), chloroplastic (GenBank accession: GQ250046) and mitochondrial (GenBank accession: GQ250045) genomes, using HISAT2 version 2. 1. 0 (Kim et al. 2015a)  with default parameters for PE reads and spliced alignment option.
We used Stringtie version 2. 1. 1  (Pertea et al. 2015)  to predict transcript structures of each library based on the aligned reads, and performed de novo transcriptome assembly using the Stringtie merge tool, thus generating a unified and non-redundant set of transcripts across the different RNA-seq samples. We finally quantified the number of reads per transcript with FeatureCounts version 2. 0. 1  (Liao et al. 2014 ) using the alignment files from HISAT2 and the transcript annotation file from Stringtie.
To assess genetic diversity within and differentiation between strains, we calculated the gene diversity for each library (H E ), mean gene diversity for each strain (H S ), total gene diversity (H T ), and genetic differentiations (G ST ) among samples within each strain, and also between the two strains for all variants. We then represented the Euclidean genetic distances among samples with a dendrogram computed with ape R package  (Paradis & Schliep 2019). Finally, for each locus that diverged between the strains (i. e. , with alleles nearly fixed within strain but different between strains, H S ≤ 0. 1 and G ST ≥ 0. 9), we assessed the synonymous vs non-synonymous status of the substitution relative to the reference genome by using SnpEff version 4. 3  (Cingolani et al. 2012). Sites where one of the focal strains had the reference allele while the other strain exhibited a synonymous mutation relative to the reference genome represented synonymous substitutions between our focal strains.
We also investigated whether difference in growth rates could be explained by a release of density dependent competition. To do so, we tested the effect of initial density on population growth rate of one of the strains in hyper-osmotic condition, using populations starting at 20,000 or 5,000 cells. mL -  (Day 1 to Day 4 at [NaCl] = 4. 0 M: GLM #3 in Table  S2 ). Statistical analyses were conducted using the statistical environment R version 4. 0. 3 (R Core Team 2020) with the MASS package  (Venables & Ripley 2002)  for the GLMs.
Differential gene expression and DNA methylation analyses The differential expression analyses were performed with the Bioconductor's package DESeq2 version 1. 30. 1  (Love et al. 2014). We first normalized the count matrix using DESeq2 regularized log transformed (rlog). We applied a redundancy analyses (RDA  (Borcard et al. 1992 )), computed with the function rda() available in the vegan R package  (Oksanen et al. 2020) , to quantify the proportions of the total gene expression variation that are significantly explained by strain, salinity or the strain × salinity interaction, with population identity as covariates to account for paired samples between salinities. We also identified differentially expressed transcripts among the same three factors, by building a general linear model as implemented in DESeq2 and using Wald significance tests  (Love et al.
2014). Transcripts with FDR < 0. 05 (P-values after Benjamini-Hochberg (BH) adjustment) and |log 2 FC| > 1 were considered as differentially expressed. Significance of the strain × salinity interaction term was assessed using likelihood ratio tests (LRT) comparing models with and without the interaction term  (Love et al. 2014). Similarly, we performed a RDA to quantify the proportions of the total variation in DNA methylation level that are significantly explained by strain, salinity or strain × salinity interaction. We then used Bioconductor's methylKit package  (Akalin et al. 2012)  to identify differentially methylated regions (DMRs), corresponding to non-overlapping 100 bp windows between strains, or between salinities for each strain. The significance of calculated differences was determined using Fisher's exact tests.
Correlation between DNA methylation and gene expression Since gene expression is a crucial step in the mapping from genotypes to phenotypes, and is thought to be a key mechanism underlying phenotypic plasticity, we wished to quantify to what extent gene expression levels can be predicted by key covariates in our dataset. We first evaluated the different sources of variation in gene expression by achieving a global partitioning of variation, assessing the influence of i) the genotype, ii) the environment, and iii) epigenetic variation on the total gene expression. We performed a RDA using the rlog transformed transcript count table as the response variable, and strain identity, salinity and epigenetic variation as explanatory variables. To account for paired samples among salinities, partial RDA was performed by removing the effect of population identity prior to assessing the effect of the environment or epigenetic variation on gene expression variation.
4B ). Because the highest and most variable methylation level was found at CpG methylation in our dataset, and because of their suggested role in gene regulation, while non-CpG methylation haven been described mostly as silencers for transposable elements  (Law & Jacobsen 2010; Chen et al. 2018; Zhang et al. 2018; de Mendoza et al. 2020) , we investigated the genomic DNA methylation patterns in response to salinity across the two strains only for cytosines at the CpG context. Redundancy analyses revealed a significant effect of strain (R 2 = 58. 69%; P = 0. 005) on total DNA methylation at the CpG context. However, contrary to gene expression, we did not detect any significant marginal effect of salinity (R 2 = 6. 47%; P = 0. 602) on epigenetic state, as also observed on the PCA plot (Fig.
Data accessibility and Benefit-Sharing Data accessibility: Raw sequence data (RNA-seq and WGB-seq) used in this study are deposited in the NCBI's Sequence Read Archive (SRA) database under BioProject ID PRJNA736997. The specific samples used in this study are under the BioSample accessions SAMN19677492, SAMN19677495, SAMN19677496, SAMN19677497, SAMN19677507, SAMN19677510, SAMN19677511 and SAMN19677512. The de novo transcriptome assembly and growth rate data will be deposited in a public digital repository once a decision will be made on the manuscript. Benefits Generated: Benefits from this research accrue from the sharing of our data and results on public databases as described above. , A. , Alizada, G. , Kiraz, Y. , Baran, Y. & Nalbant, A. (2017). Flow cytometry: Basic principles and applications.