dataesr/scientific-paragraphs-categorization huggingface dataset:
For selfconsistent explanation, we summarize the operating mode of this load. When push button T is closed, the capacitor C is charged through the resistor R1. When T is open, C is discharged through the resistance Req (potentiometer R and the resistance R2) inducing a variation of the gate voltage Vg of MOSFET transistors and their drain current switching from maximum (short circuit) to minimum (open circuit) values. In order to improve the response time of the test bench, the push button is replaced with a relay controlled by the LabVIEW program (see next paragraph) via the Agilent 34972 unit.
LabVIEW Graphical User Interface The test bench installed is fully controlled by a dedicated program developed under LabVIEW software environment. A LabVIEW program possesses two windows. A Front Panel also called the Graphical User Interface (GUI), and a diagram that contains the program governing the GUI  [5]. The two pages of the developed GUI are shown in Figure  6  and 7. The developed program controls the electronic load and the various sensors via the Data Acquisition and Switch Unit (Agilent 34972 A). Once the current-voltage (I-V) characteristic is measured, it is displayed with the power-voltage (P-V) characteristic and all corresponding parameters on the first page of the GUI. Translation to STC is then performed to display on a second page the parameters and I-V and P-V characteristics at STC.
All technical data of the PV module are displayed at the top of both pages with the dropdown list where the name of the PV module to be tested is selected. Each measured I-V characteristic is automatically saved with the irradiance and cell temperature under which it is measured in Excel file with a name consisting of the date and time of measurement. Within this developed GUI, simulation is also performed to validate the model with the extracted parameters, and calculate the performance at STC. A test report can be printed from the GUI. It is similar to a data sheet that contains all the information on the GUI after testing a given PV module. The block diagram of Figure  8  describes the overall operation of the program governing the developed GUI and some stages of the diagram are shown in Figures  9  and 10.
CONCLUSION The Grafical User Interface (GUI) presented in this paper is associated to a fairly complete test and characterization tools allowing its use by researchers and professionals working in the application and development fields related to photovoltaic. The two main objectives of this original developement are the control of an outdoor test bench for PV module and the extraction of all the electrical parameters of PV module based on the one diode model parameters. These so obtained working parameters are of prior importance. They are necessary for the implementation of the model in a simulation program and are generally not given by the manufacturer in the data sheets of PV modules. Additionally, we have implemented the function related to the simulation of the PV panel within this GUI.
It is performed under measurement conditions to validate the model and at standard test conditions, STC, to calculate the values of the electrical parameters of the PV module. This allows the comparison between the so-obtained experimental values with those provided by the manufacturer in the data sheet of the PV module. In this work, a special care was brought in the user interface by the association of a LabVIEW GUI offering much interactivity in the test bench allowing to easily performing complete electrical tests of various commercial PV modules or panels before their use in experimental works or their implantation in new renewable electrical generators. Finally, an experimental application, done with a commercial PV module for different pairs of irradiance and temperature illustrates this study and validates the concept and the development of the test bench and its associated GUI.
Energy security improvement is often presented as a co-benefit of climate policies. This paper evaluates this claim. It presents a methodology to investigate whether climate policy would improve energy security, while accounting for the difficulties entailed by the many-faceted nature of the concept and the large uncertainties on the determinants of future energy systems. A fourdimension analysis grid is used to capture the energy security concept, and a database of scenarios allows us to explore the uncertainty space. The results, focusing on Europe, reveal there is no unequivocal effect of climate policy on all the dimensions of energy security. Moreover, time matters: the effect of climate policy depends on the time horizon considered. Lastly, these results are robust to uncertainties on drivers of economic growth and the potentials and costs of end-uses technologies, but are sensitive to fossil fuels availability, low carbon technologies in the energy sector and improvements in energy efficiency.
A second difficulty arises when studying the effect of climate policy on energy security due to pervasive uncertainties on the determinants of future changes in energy systems -for instance population and economic growth, costs and the potential of technologies such as electric vehicles, renewable energies or synthetic fuels and consumer behavior with respect to energy services. This difficulty calls for an investigation of the uncertainty space to test the robustness or sensitivity of the results. In Section 3 we describe a methodology to build a large database of scenarios exploring the uncertainty space using an energy-economy-environment model, Imaclim-R, to account for this second difficulty. In Section 4, we use the set of indicators we developed to analyze the database of scenarios, focusing on Europe.
Method Our method is a multi-criteria analysis, using the indicators presented above, of a database of scenarios built with the Imaclim-R model. We extend the approach used in some recent publications  (Jewell et al. , 2014a; Jewell et al. , 2014b; McCollum et al. , 2014; Cherp et al. , 2013)  by broadening the scope of energy security dimensions analysed, adding indicators measuring the affordability and the sustainability dimensions, and developing an original treatment of the uncertainty. Unlike most of the recent publications, we consider only one scenario with a constraint on the global CO 2 emissions. Consequently, the uncertainty is not on the stringency of CO 2 -constraint, or on the design of an international climate agreement, but on different determinants of the future energy systems, both on the supply side and on the demand side.
The scenarios database Investigating the impact of climate policies on energy security requires building long-term scenarios for the world economy. There are many highly uncertain potential determinants of this impact including future population growth, the growth and structure of the economy, energy markets, low-carbon technologies, and energy efficiency. To account for these uncertainties, we constructed a database of scenarios combining hypotheses on a large number of model parameters, following the methodology proposed by  Rozenberg et al. (2014). This methodology allows us to consider many different types of uncertainties simultaneously. Compared to a methodology based on a few scenarios only, this approach has the advantages (i) to enable the evaluation of the robustness or sensitivity of a given result, and (ii) to allow the analysis of the main determining factors of a given result.
But electricity production can either increase or decrease: energy efficiency in end uses reduces demand but substitutions between energy types tend to increase electrification of end uses, hence increasing demand. The overall effect is ambiguous and depends on the relative weights of these conflicting forces. The implementation of climate policies has thus mixed impacts on the indicators of energy security chosen, but rather tends to improve most indicators from the medium term on. 13 evertheless, we note that the differences between the 10 th and 90 th percentiles tend to increase with time horizon, revealing more contrasted evolution depending on the future world considered. We explore this point in the following part by seeking if some indicators are more sensitive to some specific assumptions on the energy systems explored in the database of scenarios.
Uncertainty matters Our scenarios database made it possible to explore the role played by the different determinants of the energy security indicators. In this part we focus on the results in the medium term, because at this time horizon the effect of climate policy is ambiguous on four out of the eight indicators chosen (the oil market concentration, the share of imports in TPES, the households' energy budget and the installed nuclear capacity). 14 We compare the effects of the climate policy on the indicators depending on whether the assumption concerning the determinant is "low" or "high" (Table  2 ). For each energy security indicator and each determinant, Figure  3  shows the average effect of climate policies on the value of the indicator in the two subsets of the scenarios database: those where the determinant is low (henceforth, the "blue" value), and those where the determinant is high (henceforth, the "red" value).
However, some of these uncertainties determine the magnitude of the effect of climate policy on energy security indicators. Among the few assumptions playing a significant role, some of them can really be impacted by public choices, in particular in terms of innovation policies. The importance of these different points confirms the relevancy of the methodology applied. It accounts for the difficulties entailed by the many-faceted nature of the energy security concept, the temporal dimension of the issue and the large uncertainties on the determinants of future changes in energy systems. The method uses a set of indicators in a four-dimension analysis grid of the energy security concept, three time horizons and a database of scenarios to explore the uncertainty space. Nevertheless  The modeling structure of oil supply in Imaclim-R is based on three general principles.
that presents changes in the macro-economic and technological world in a consistent framework. It is disaggregated into 12 regions and 12 sectors, including five energy sectors: coal, oil, gas, refined products and electricity. Europe is one of the 12 regions. Details on the regional and sectoral disaggregation can be found in Appendix B. The model is calibrated on the 2001 base year by modifying the input-output tables provided by the GTAP-6 dataset (Dimaranan and McDougall, 2006)  to make them fully compatible with 2001 IEA energy balances (in Mtoe) and data on passengers' mobility (in passenger-km) from Schafer and Victor (2000). The model covers the period 2001-2100 in yearly steps through the recursive succession of static equilibria and dynamic modules. The static equilibrium represents short-run macroeconomic interactions at each date t under technology and capacity constraints.
The chemical formulas and atomic compositions of TNWs and HT products were determined by crystallinity, content of water, surface atomic content and bulk atomic content, which were examined by X-ray diffractometry (XRD), thermogravimetry (TG; ULVAC-RIKO TGD-9700), X-ray photoelectron spectroscopy (XPS; JEOL JPC-9010MC) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/ EDS, JEOL JSM-6360LA), respectively. The content of water was determined by TG based on the assumption that no volatile components other than water were included in the samples and thereby loss of weight was solely caused by removal of water. The SEM/EDS measurements were performed with an accelerating voltage higher than 15. 0 kV and a working distance of 10 mm. Five different areas were analyzed for each sample and average data were used for determination of composition.
Time-resolved microwave conductivity measurements Charge-carrier dynamics was studied by measuring timeresolved microwave conductivity (TRMC). The incident microwave of 36. 8 GHz was generated by a Gunn diode of the K a band, and UV laser pulses were created by the third harmonic of a 1064 nm Nd:YAG laser (10 Hz) with full width at half maximum of ca. 10 ns. Details of measurement and data processing have been reported elsewhere. 34, 35 Results and discussion.
Acknowledgements This study was partly supported by  CONCERT-Japan Program  ( Japan Science and Technology Agency)  and a  Grant-in-Aid (KAKENHI)  from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan  (grant no. 2510750303 ). One of the authors (Z. W. ) appreciates  China Scholarship Council  ( CSC ) for support. We also thank  Kunlei Wang  for his kind help on drawing cover picture.
The question has been difficult to address because the molybdenum cofactor is oxygen sensitive, very labile and never found in its free state. Moreover, threedimensional (3D) structures of molybdoenzymes demonstrate that it is systematically found buried in the enzyme. For instance, in E. coli FdhF, the cofactor is 25 Å away from the enzyme surface, thus rendering it inaccessible to EcFdhD  12. This suggests that the cofactor is likely sulphurated prior to its insertion in the apoenzyme. Interestingly, the structure of an FdhD homologue in the d-proteobacteria Desulfotalea psychrophila has been solved (DpFdhD; pdb code 2PW9, unpublished) and revealed that it has structural homologies with the cytidine deaminase family of nucleotide-interacting proteins  13. As nucleotides are chemical moieties of the Mo-bisPGD structure  14  , it has been hypothesized that EcFdhD could bind and sulphurate the cofactor before its insertion into FDHs  11.
Briefly, protein interactions were estimated by measurement of b-galactosidase activity in cells grown to mid-log phase in anaerobiosis at 30 °C in L-broth medium supplemented with 0. 5 mM isopropyl-1-thio-b-D-galactopyranoside (IPTG), HCOONa (12. 5 mM), Na 2 MoO 4 (2 mM) and Na 2 SeO 3 (2 mM). Statistical analysis. All data are expressed as means ± s. d. Data for each experiment were acquired from at least three independent experiments. Protein purification. EcFdhD purification and its variants were performed by nickel-affinity chromatography as described in ref. 11. For the gel filtration, the Superdex 200 column (1 Â 30 cm) was equilibrated with buffer containing Tris-HCl 20 mM pH 7. 2, NaCl 150 mM. 50 ml of purified EcFdhD at 240 mM was applied to the column. The enzyme was then eluted in 0.
For cryoprotection crystals were gradually transferred in a mother liquor supplemented with 30% glycerol. Data were collected at beamline ID29 (ESRF) and processed using Mosflm and Scala from the CCP4 suite  28. The structure of E. coli FdhD was solved by molecular replacement using the structure given in the PDB entry 2PW9 as a searched model and the programme Balbes  29. PDB entry 2PW9 depict the 3D structure of a formate dehydrogenase accessory protein from D. psychrophila (30% identity with E. coli FdhD over 246 residues). One molecule per asymmetric unit was found and the dimer of EcFdhD is generated through a crystallographic symmetry axis. The structure was then refined with the programme phenix. refine with manual building using the programme COOT  30. From the first refinement cycles, additional electron density corresponding to the GDP became visible.
Acknowledgements We thank  Pierre Mandin  and  Sandrine Ollagnier de Choudens  for their critics and comments. We also thank  Florence Chaspoul  for ICP-MS analysis,  Deborah Byrne-Kodjabachian  from the protein expression facility for DLS analysis,  Anne Galinier ,  Jean-Pierre Duneau  and  Pierre Hubert  for help and support concerning the fluorescence analysis. This work was supported by the  Centre National de la Recherche Scientifique , the  Commissariat al'Energie Atomique et aux Energies Alternatives , the  Agence Nationale de la Recherche  ( ANR MC2 ), and  Aix Marseille Universite.
Species and Genes Phylogeny The species tree was based on a rooted maximum-likelihood phylogeny of 454 phylum level phylogenetic markers for e-proteobacteria  (Wang and Wu 2013). The tree was processed in the R software environment using functions of the Ape library  (Popescu et al. 2012). Tips were purged to include the genomes selected for the analysis with the "drop. tip" function, and the resulting tree was rendered ultrametric with the "chronos" function. Traits of gene presence/absence were plotted alongside the tips (see fig. 3 ) using the "table. phylo4d" function of the Adephylo package  (Jombart and Dray 2010). The phylogenetic tree of SelA proteins was obtained using the maximum-likelihood method implemented in the RaxML program ver. 7. 7. 8 (Stamatakis 2006 ) using the PROTCATGTR amino acid substitution model.
To discriminate between equivalent and nonequivalent homologs, known paralogous proteins (e. g. , EF-Tu) were included in the search. The BLAST output was parsed with a custom Perl script to obtain the results reported in supplementary table S1, Supplementary Material online. Proteins containing an Sec UGA codon were identified by homology with a set of 1,022 annotated selenoproteins downloaded from the database of Trace Element Utilization (dbTEU; http://gladyshevlab. org/trace_element/). Annotated selenoproteins were used to query the genomes set with tBLASTn (E < 10 À6 ). The top-scoring alignment of each DNA locus was parsed to identify the codon (either TGA or TGY) corresponding to an Sec in the query sequence, as reported in supplementary table  S3 , Supplementary Material online. Coding sequences of selenoproteins identified by homology were predicted using the gmhmmp_heuristic.
pl program of the Genemark suite  (Borodovsky et al. 2003) , by considering the best gene model (option -b) obtained after the replacement of the TGA codon with NNN. The Genemark prediction of the C. jejuni SelD gene was corrected based on the evidence of a rare CUG start codon  (Shaw et al. 2012). The search of proteins without homology to known selenoproteins was based on previously described methods  (Kryukov and Gladyshev 2004). Briefly, open reading frames (ORFs) greater than 90 nt containing potential UGA codons were translated from complete e-proteobacteria genomes with the Getorf program of the EMBOSS package using a custom genetic code assuming UGA!U decoding. The ORFs were searched for homology using tBLASTn in the same genome set, the results were parsed to count the occurrences of U:C, U:U, and U:X in sequence alignments.
Sequences encompassing 100 nt upstream and downstream candidate SECIS elements identified by the covariance model were translated in full (including stop codons) using the EMBOSS transeq program and assuming UGA!U decoding. This procedure allowed us to observe in tBLASTn alignments U residues immediately followed by a stop codon, as in the case of DUF466 proteins; the same result could be obtained with ORF translations using the tfastx36 program with the "-m BB" option to simulate the BLAST output.
RNA Secondary Structure and Covariance Analysis The covariance model for bacterial tRNAs (TRNAinf-bact-c. cm) of the Infernal package  (Nawrocki and Eddy 2013)  was used to align and fold non-Sec tRNAs in the standard cloverleaf structure. The alignment of e-proteobacterial Sec tRNAs was based on the prokaryotic covariance model of Sec tRNAs (PSELCinf-c. cm). Different secondary structures proposed for Sec tRNAs (see supplementary fig. S1 , Supplementary Material online) were evaluated by covariance analysis and by comparing the structure components of the scores obtained by the e-proteobacteria Sec tRNAs with covariance models assuming 7/5, 8/5, or 9/4 bp in the aminoacyl-and T-stems. The RNA secondary structure of sequences encoding selenoproteins was analyzed with the Centroid_alifold program  (Hamada et al.
2009 ) using sequence alignments of the region around the TGA codon (À10; +60) of the formate dehydrogenase (Fdh), SelD, and SelW protein families. The prediction of the SECIS structures in individual sequences was based on the bSECISearch program  (Zhang and Gladyshev 2005)  accessed through the web interface (http://genomics. unl. edu/bSECISearch/). The identification of SECIS elements in the e-proteobacteria genome set was based on the Cmsearch program  (Nawrocki and Eddy 2013)  with the covariance model derived from the alignment of figure 3A using a cut-off score of 16. The matches of the SECIS elements with the covariance model were assessed by bit scores (T) and significance (P) as provided by Cmsearch. Covariance analysis and the drawing of RNA structures were based on the R2R program  (Weinberg and Breaker 2011).
Synonymous-Nonsynonymous Substitutions The ratio of nonsynonymous substitutions per nonsynonymous site (dN) to synonymous substitutions per synonymous site (dS) in pairwise comparisons was calculated on the multiple alignment of SelA coding sequences with the maximumlikelihood method implemented in the Codeml program of the PAML package  (Yang 2007). Variations of dN/dS ratios in SelA coding sequences along the e-proteobacteria tree were analyzed with Codeml by allowing the dN/dS ratio to vary among particular branches of the tree. Parameters were estimated under models of a single or multiple dN/dS ratios (up to four) and under models of persistent and episodic rate variations. The likelihoods of the different models were compared using the likelihood ratio test.
Sequence Alignment and Structure Analysis Multiple sequence alignments were built with the ClustalW program  (Larkin et al. 2007 ). The presence or absence of the tRNA Sec binding domain in SelA proteins was assessed through comparison with a Hidden Markov Model  (Eddy 1996)  of the domain. The alignment was analyzed with Genedoc, and the online resource Espript  (Robert and Gouet 2014)  was used to generate alignment figures. Models of the SelA structure were downloaded from PDB and analyzed with PyMol (http://www. pymol. org). The list of residues involved in protein-protein interactions was downloaded from PDBsum (http://www. ebi. ac. uk/pdbsum). Analysis of residue conservation mapped onto the AaSelA structure was conducted with the Consurf web server (http://consurf. tau. ac. il/) using multiple alignments of SelA sequences from Helicobacteriaceae species possessing or not the Sec-decoding trait.
Results Sec tRNA Genes (selC) in e-Proteobacteria tRNA genes are usually identified with high accuracy (>99%) in genomic sequences by dedicated programs. An exception is caused by Sec tRNAs, which differ from the tRNA model for the longer D and variable arms and nucleotide positions deviating from the consensus (fig. 1 ). The general tRNA-finding program tRNAscan-SE  (Lowe and Eddy 1997 ) can optionally identify Sec tRNAs using special covariance models, whereas the eukaryotic tRNA-finding program Pol3scan  (Percudani et al. 1997)  runs by default a dedicated procedure for their identification; the application of this procedure to the first yeast genome provided evidence for the absence of tRNA Sec in Saccharomyces cerevisiae. Seven tRNA Sec genes were annotated in our e-proteobacterial genome set (supplementary table S1, Supplementary Material online), whereas a search using tRNAscan-SE with the default parameters identified tRNA Sec genes only in C.
Identification of Annotated and Unannotated Selenoproteins A search in the complete proteomes of the e-proteobacteria selected for the analysis revealed ten proteins containing an Sec residue (U): 6 Fdh, 2 selenophosphate synthase (SelD), 1 selenoprotein W (SelW), and 1 thioredoxin (Tr). By searching nucleotide sequences for tBLASTn homology with the specialized trace element database (dbTEU) (Zhang and Gladyshev 2010), we found 40 additional genes potentially encoding Sec residues (supplementary table  S2 , Supplementary Material online). Most of these sequences corresponded to 5 0 -or 3 0 -truncated sequences in GenBank. However, some of the identified genes, particularly those encoding SelW proteins, were completely missed in the genome annotation (supplementary table  S2 , Supplementary Material online).