dataesr/scientific-paragraphs-categorization huggingface dataset:
Preparation of cell extracts and western blot Total protein extracts from human cell lines were obtained by harvesting cells in Laemmli buffer (0. 06 M Tris-HCl, pH 6. 8, 10% glycerol, 2% SDS, protease inhibitors (Calbiochem)). Protein concentrations were determined using the BCA method. Equal amounts of proteins (50 μg) were separated on SDS-PAGE and transferred on 0. 45 μm PVDF membranes. The primary antibodies used were: anti-β-actin (Sigma-Aldrich #A5441), -CAIX (carbonic anhydrase 9, NovusBio #NB100-417SS), -CIAPIN1 (Sigma-Aldrich #HPA042182), -cleaved caspase-3 (Cell Signaling #9661), -caspase-9 (5B4) (Abcam #Ab28131), -CISD2 (Proteintech #13318-1-AP), -FXN (frataxin, a gift from Dr. H. Puccio IGBMC, Illkirch, France), -HIF-1α (NovusBio #NB100-449), -HSC20 (Sigma-Aldrich #HPA018447), -ISCU (Proteintech #14812-1-AP), -mitoNEET (designed by Eurogentec), -NARFL (Sigma-Aldrich #HPA040851), -NFS1 (Proteintech #15370-1-AP), -NUBP1 (Sigma-Aldrich #HPA041656), -PARP1 (Calbiochem #AM30), -VDAC1 Nter (Interchim #AP17825PU-N), -VDACs poly (recognizing VDAC1, 2 and 3 isoforms, a gift from Dr.
C. Brenner, INSERM U769, University of Paris-Sud, France), -HIF-2α (Novus #NB100-122) and-VINCULIN (Sigma-Aldrich #V9131). Secondary antibodies were anti-mouse, anti-rabbit and anti-chicken fluorescent IRDye 800CW (LI-COR) and membranes were scanned with an Odyssey 1 Imaging System (LI-COR). Quantitation was performed using Li-Cor Odyssey software. In some blot images, unnecessary lanes were cut off and clearly demarcated using black lines in the corresponding figures.
Mitochondrial membrane potential Cells were analyzed for hallmarks of mitochondrial depolarization by using the membranepermeable JC-1 (5,5',6,6'-tetrachloro-1,1',3,3' tetraethylbenzimidazolylcarbocyanine iodide) dye according to the manufacturer's protocol (Biotium). Flow cytometry analysis was performed on an FC500 Beckman Coulter instrument. Statistical analysis-All results are presented as the mean +/-standard deviation of at least three independent experiments. Data were analyzed using one-way ANOVA analysis of variance with SigmaPlot. The Dunnett's test was performed for all multiple comparisons versus control group. The Student-Newman-Keuls test was used for all pairwise comparisons of mean responses among the different treatment groups. Differences between groups were considered significant if the p value was less than 0.
Acknowledgments We thank  Hela Allani  and  Sylvie Riquier  for technical support,  Dr Laurent Combettes  and  Isabelle Garcin  ( INSERM UMR-S 1174, Orsay, France ) for technical support with  Confocal Microscopy ,  Dr Jerome Bignon  ( CIBI platform, ICSN ) for flow cytometry analysis and  Dr Catherine Brenner  ( INSERM U769 ,  University of Paris-Sud ) for scientific discussion. We also acknowledge the I2BC cloning platform ( CNRS, Gif-sur-Yvette, France ). The authors also warmly acknowledge the networking support from the  EU COST Action FeSBioNet  ( CA15133 ).
Emission datasets We first consider a dataset of emissions for reference years used in the NDCs. For consistency with work from the Intergovernmental Panel on Climate Change (IPCC), we rely on published emission inventories  [16, Table   S2 ]. Historical emissions data come from three distinct databases: the Emissions Database for Global Atmospheric Research (EDGAR) for emissions of CO2 except from LULUCF, CH4, N2O, HFC, PFC, and SF6  [17] , and the BLUE (bookkeeping of land use emissions) model  [18 and personal communication]  for CO2 emissions from LULUCF. For the United States, Canada and Russia, whose NDCs are expressed in "net-net emissions", we also consider their CO2 LULUCF balance (emissions + sinks) reported to the UNFCCC. Since the UNFCCC accounting for LULUCF balance differs from that used in Earth system models  [19] , we only consider half of these supplemental carbon sinks as anthropogenic (Table  S4 ).
Uncertainties in historical emissions, including in Chinese emissions  [20, 21] , are not accounted for. Rather, in the light of the recent study by  Liu et   A c c e p t e d M a n u s c r i p t about 5% smaller than EDGAR over the 1990-2010 period (Table  S3 ). We discuss the impact of this correction on our results in the Supplementary Material (Section 10 and Fig. S6 ). Combining these datasets, we estimate 2010 GHG emissions to be 50. 6 Gt CO2eq yr-1 accounting for an anthropogenic carbon sink due to LULUCF. We are not able to derive a strict uncertainty range for this quantity as neither EDGAR nor BLUE provides an uncertainty analysis for their data. Our estimate is within the range of other estimates: 50. 9 GtCO2eq yr-1 for EDGAR  [23] ; 47. 6 Gt CO2eq yr-1 for the Climate Action Tracker  [24]  and 49.
GDP datasets We consider 20 GDP scenarios, illustrating the five Shared Socioeconomic Pathways (SSP)  [10]  for four quantifications (the marker quantification by OECD  [13] , and three alternative quantifications by CEPII  [12] , IIASA [14], and PIK  [15] ). SSP scenarios aim at facilitating the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation  [10]  and represent a framework to examine possible future states of the world. Because the GDP scenarios diverge after 2005, we correct them on the basis of the historical 2005-2015 data from the World Bank World Development Indicators database (release from September 15, 2017). We propagate the difference between the SSP and actual GDP growth rates until 2030 with a transition from a purely historical GDP driven trajectory to a purely SSP driven trajectory (see Supplementary Material section 4).
and Rogelj et al. [9] have inferred the 2030 global emissions from INDCs or NDCs. The UNFCCC based its analysis on the content of INDCs, but considered additional information from the IPCC AR5 database and other national data for emissions not covered by INDCs. Their calculations result in global emission Page 2 of 20 AUTHOR SUBMITTED MANUSCRIPT -ERL-103963. R2 A c c e p t e d M a n u s c r i p t levels of 56. 2 (52. 0 to 59. 3) Gt CO2eq yr-1 in 2030; their uncertainty ranges indicate 20%-80% percentiles. The four studies by Fawcett et al. , den Elzen et al. , Vandyck et al. and Rogelj et al. [9] share similar methodologies relying on scenarios from Integrated Assessment Model(s) (IAMs) in which the targets expressed in the INDCs/NDCs are implemented. These studies assess 2030 emission levels accounting for current policies and additional targets expressed in the INDCs/NDCs.
For that purpose, cells were cultured, treated, or not with our extracts in 96-well plates. Cell were washed with PBS, trypsinized, centrifuged, and re-suspended in the preserved supernatant of the first wash in order to keep all non-adherent apoptotic cells present. A minimum of 2000 cells was acquired per sample and analyzed on the InCyte software (Guava, Millipore Merck, Burlington, MA, USA). To discriminate between negative and positive events in the analysis, a non-stained control sample from each culture condition always accompanied acquisition of the stained cells to define the cut off. A negative control i. e. , sample with cells without compounds but with the same percentage of DMSO (v/v) as for diluted compounds was included in each experiment. Celastrol was used as a positive control for apoptotic assays.
The outcome of this study provides additional scientific data for the selection of several microalgae strains for further investigation and offers support for the continued research for the development of new potential drugs for human therapeutics from cultured microalgae. Supplementary Materials: Supplementary materials are available online at http://www. mdpi. com/1660-3397/ 16/9/317/s1. Table  S1 : Strain codes and isolation zone for bioprospecting microalgae, Table  S2 : Anti-microbial, antiviral, anti-proliferative and apoptotic results for 65 extracts of marine microalgae. Results are expressed as a percentage effect compared to the untreated control. Extracts were tested at a final concentration of 100 g/mL (antimicrobial, antiviral, and apoptotic) and 50 g/mL (anti-proliferative). For clarity, color scale has been applied to highlight the differences in values.
Acknowledgments: This work was supported by the grants  EU FP7-KBBE-3-245137  (MAREX),  CTQ2014-55888-C03-01  (the  Spanish MINECO)  and by the  Academy of Finland  (PT, grant no. 277001 ). The authors thank  Dr. Santiago Fraga  ( Oceanographic Centre of Vigo, IEO, Spain ) for the generous gift of microalgae strains and  Pirjo Käpylä  for the valuable technical assistance in the high throughput anticancer assays carried out at  VTT.
Potentials of BESS in distribution systems Grid-scale storage facilities through the world have been gathered in a large database from the US DOE  [10]. A full description is given for most of them such as the date of creation, the location, the technology, the rated capacity, the rated power, the use cases, a picture of the project, etc. It appears that the global storage resource is small (the operational maximum power storage is around 170-180 GW, corresponding to less than 1% of our energy production). The main storage technology (in terms of rated power) is by far pumped hydro (~96%), but electrochemical projects are the most numerous (nearly 1000) and represent nearly 2% of the total rated power. As listed in [10-12], a BESS can provide numerous benefits such as: • Environmental: integration of renewable sources (the variability of these sources threatens the grid stability), replacement of diesel generators (in off-grid sites), pollution reduction (by reducing peak demand often met with harmful and costly plants), etc.
Optimization techniques Several optimization techniques are available for the sizing and the planning of renewable energy-based systems  [17]. Some popular software tools such as Hybrid Optimization Model for Electric Renewables (HOMER) and Hybrid Power System Simulation Model (HYBRID2) both developed by the National Renewable Energy Laboratory (NREL), United States, and Hybrid Optimization using Genetic Algorithm (HOGA) developed in the University of Zaragoza, Spain, are presented in Ref. [17]  to simulate and optimize any microgrid configuration. Nevertheless, in order to have the highest flexibility in terms of modeling and optimization, other classical tools are commonly used such as MATLAB and General Algebraic Modeling System (GAMS). In optimization problem, the objective function can be mono-objective (e.
Second, computation burden of each local controller is mitigated which in turn reduces the required communication bandwidth. Finally, a distributed P/EMS could improve the redundancy and modularity of the system where it is needed. However, there is still a problem if a communication link fails in the system. This failure would not end to a total system collapse, but the performance of the system would not be optimal any longer. Also, a distributed P/EMS suffers from degradation of performance on small/medium networks, increased use of database space, and complex use and administration. Multi-agent system (MAS) is one of the best illustrations for a distributed scheme  [44].
Steady-state kinetic parameters were determined by varying L-cysteine concentration for Nfs1 1-53 activity (after Nfs1 1-53 /Isd11/IscU/frataxin complex formation) showing a catalytic efficiency of 149 M -1 s -1 , k cat 2. 6 min -1 , and a K M of 0. 290 mM. Taken together, these data indicate that Isd11, IscU, and frataxin are required for the activation of Nfs1 in Drosophila, providing experimental evidence that the initial complex of the mitochondrial Fe-S cluster biosynthetic machinery is conserved in Drosophila similar to what is known in other eukaryotes, including species with nonrespiring mitochondria  (Tovar et al. , 2003; Richards and van der Giezen, 2006).
ACKNOWLEDGMENTS The authors thank  Kuanyu Li  from  Nanjing University  who provided the purified human frataxin (Figure 3),  Konstantinos Mandilaras  for the circadian behavior tests performed at  Queen Mary University of London  ( Figure 8 ) and the two reviewers for their helpful comments. Ralf Stanewsky , who had worked with two of the 2017 Nobel Prize in Physiology and Medicine laureates on light entrainment of the circadian clock (Stanewsky et al. , 1996(Stanewsky et al. , , 1997(Stanewsky et al. , , 1998;;Emery et al. , 2000) and now focuses on the question how the clock is temperature compensated (Sehadova et al. , 2009;Gentile et al. , 2013;Simoni et al. , 2014;Chen C. et al. , 2015;Buhl et al. , 2016;Klemz et al. , 2017;Tang et al. , 2017) introduced FM to circadian clocks;  Tracey Rouault  to iron biology (Missirlis et al.
Electrochemistry Electrochemical data were acquired with a Biologic VSP 300 or a Princeton Applied Research Ametek PARSTAT 4000 potentiostat for measurements in the glovebox. Electrochemical measurements were conducted in 3-electrode cells made of two compartments separated by a porous glass frit or a Naon® N-117 (180 mm, 0. 90 meq g À1 exchanging capacity) membrane in the case of aqueous electrolytes. The working electrode was a decorated or a pristine NiO electrode (Dyenamo). In one case, the NiO electrode was covered by a galvanoplastic scotch tape bearing a hole of 8 mm diameter to determine the electroactive area (S ¼ 0. 5 cm 2 ) and clamped against a 9 mm diameter hole in the electrochemical cell, with an intercalary O-ring seal to ensure tightness at the junction. In the other case, the electroactive area was determined by applying a galvanoplastic tape at the top of the mesoporous NiO part, which was dipped in the electrolyte.
14 and 568. 12, respectively (Fig. 5b ). These contributions either imply deprotonation or an in-source reduction of Co III to Co II during MS data collection. Interestingly, a peak at m/z ¼ 627. 14 (Fig. 5a ) was also observed and was ascribed to the molecule without any Br À ligand but containing Ni and corresponding to the exact mass of 627. 129 (Fig. S7 † ). This signature emphasizes a graing on the NiO surface via the phosphonate binding, which is also conrmed by the observation of fragments containing Ni, O and P in the negative mode spectra ([NiPO 4 H] À and [NiPO 5 H 2 ] À with exact mass 153. 897, resp. 170. 899, detected at m/z ¼ 153. 88, resp. 170. 90; Fig. S8 † ). 48 n the other hand, the set of peaks around m/z ¼ 650 (Fig. 5a )  clearly exhibits intensity ratios still different from the ones expected for the contribution proposed above (Co C11P minus phosphonate).
Acknowledgements This work was supported by the  French National Research Agency  ( PhotoCAT  project  -ANR-14-JTIC-0004-01  and the  Labex  program  ARCANE -ANR-11-LABX-0003-01 ). We thank  E. Martinez  from the  Nanocharacterization platform at MINATEC campus  ( Univ Grenoble Alpes, CEA-Leti ) for XPS measurements and  Colette Lebrun  from  SyMMES  ( Univ Grenoble Alpes ,  CNRS ,  CEA-INAC)  for  ESI-MS  measurements.
Thus, the aim of our study was to analyze clinical and biological data reported in literature to date. We also present 10 new cases referred to our center (Table  1 ). To improve patient follow up after reversal and idarucizumab use in our daily practice, we analyzed the impact of initial dabigatran plasma concentrations on the incidence of rebound. The study included 33 cases of patients found in literature between December 1 th 2015 and February 1 st 2017, and 10 cases referred to our institution (European Hospital Georges Pompidou, Paris, France), of which 7 received idarucizumab for serious bleeding and 3 for required surgery or other emergent invasive procedure (Table  1  and Online Supplementary Figure  S1 ). We collected clinical and biological data from all patients. In our study, dabigatran plasma concentrations were analyzed when determined using the specific diluted thrombin time (TTd) assay, as reported in RE-VERSE AD.
RNAseq library construction and data analysis Our transcriptomic study is based on six biological samples from this experiment (Pt-27, Pt-83, Pt-90, Pt-117, Pt-125 and Pt-129) for each strain (Table  S1 )  [24]. Pt-27 samples correspond to the steady state condition preceding the first nitrogen spike and Pt-117 to the third steady state. Pt-83 and Pt-125 samples correspond to the second and third nitrogen repletions, and samples Pt-90 and Pt-129 correspond to the second and third nitrogen depletions, respectively. These twelve samples were used for transcriptomic sequencing using Illumina technology. Total RNA was extracted and sequencing was carried out as in  [22]. For each sample, raw reads data were filtered using Cutadapt (version 1. 0)  [28]  to remove known Illumina adapter sequences.
Reads quality filter (version 1. 0. 0) was then used to exclude low quality reads, with a quality score threshold of 30 and a minimal length of 75 bases. Read quality was assessed using FastQC software  [29]. The 352,199,068 cleaned paired reads were mapped on the reference genome of T. lutea (raw reads at SRA, RUN: SRR3156597) using Tophat2 (version 0. 5)  [30]  and aligned reads for each gene were counted using htseq-count  [31]  (version 0. 3. 1) in union mode. Gene expression level was then calculated in Reads Per Kilobase per Million mapped reads (RPKM). A gene with an RPKM value > 1 in at least one of the twelve samples was considered as expressed. Among the 20,582 identified genes in the T. lutea genome, 15,333 were expressed in this study. Genes were annotated using BLAST against the Swissprot database and functional domains were identified using InterProScan as implemented in BLAST2GO  [32].
Gene co-expression networks (GCNs) underlying the response of each strain to nitrogen deprivation Seven TFs (MYB-2R_14, GATA_2, MYB-2R_20, MYB-rel_11, NF-YB_2, Fungal-TRF_8 and HB-other_9_PAS) were differentially expressed in the 2Xc1 strain. They are used in a strain-specific manner to cope with the variation of nitrogen availability, which induced the mutant phenotype. This suggests an important role of these seven TFs in the establishment of the mutant phenotype under nitrogen deprivation. To identify the link between these TFs and the mutant phenotype, a GCN was constructed for each strain using eLSA software  [35, 36]  that uses co-expression to link TFs to the others DEGs (Fig. 2 ). The GCNs were based on the RPKM values of the DEGs between the strains. The WTc1 GCN is a directed graph with 298 nodes for five TFs and 293 genes linked by 412 edges (density: 0.
For the genes co-expressed with the Fungal-TRF_8 TF, the TFBS contained in the JASPAR fungi database  [38]  were used because this TF family is well characterized in fungi. Three motifs bound by TF from this family were found (RSC30 (uniprot: P38781), RSC3 (uniprot: Q06639) and CHA4 (uniprot: P43634) TFs from Saccharomyces cerevisiae). An alignment of the amino acid sequence of the DNA binding domains (DBDs) of these four TFs revealed that Fungal-TRF_8 is closer to CHA4 (48. 15% identity) than to RSC3 (32. 14% identity) or RSC30 (30% identity). CHA4 was shown to be involved in serine catabolism allowing the use of serine and threonine as nitrogen source in S. cerevisiae  [39, 40]. For the other TFs, the Arabidopsis thaliana dataset from O'Malley et al. [41]  was used to screen the promoting sequences for TFBS enrichment.
For this purpose, a weighted gene co-expression network was built based on the twelve RNAseq samples using the WGCNA R package  [33]. Based on the WGCNA data, the genes for which the expression profile was correlated to the variation of lipid or carbohydrate content were identified. The genes were then prioritized (or ranked) following this correlation coefficient. These genes are likely to be related to lipid and carbohydrate content because their expression follows the variation of these physiological parameters. Moreover, in co-expression modules such as those identified by WGCNA, the more highly connected genes (called "hub genes") were shown to play a critical role in the cellular function of the whole module  [44] [45] [46]  making of these genes very interesting candidates. Consequently, the hub genes of the modules found to be significantly correlated to lipid and carbohydrate content were thought to be functionally related to these two parameters.
Therefore, these hub genes were added to the gene prioritization. Following the WGCNA standard procedure, the 15,333 expressed genes were clustered into 20 modules of co-expressed genes (Fig. 3a ). The first principal component of each identified module, called the eigengene, describes the eigen expression profile of a given module. This signal is then correlated with physiological traits of interest (i. e. , cell carbohydrate and storage lipid content dynamics) to decipher three modules of physiological interest (Fig. 3b ). A first module, called the Lipid Related (LR) module, correlates with cell storage lipid content (r = 0. 72, p-value = 8 e -3 ), Two other modules, called Carbohydrate Related (CR1 and CR2), correlate with cell carbohydrate content (r = 0. 78, p-value = 3 e -3 and r = 0.
To identify the potential role of each TF in the establishment of this phenotype, the functional annotation of the co-expressed genes was completed by gene prioritization, based on WGCNA data. The functions enriched in these three modules were diverse. Such a functional diversity is explained by the overall reorganization of the metabolism driven by changes in nitrogen availability. These conditions are already known to induce various changes in gene expression and metabolic re-modelling, which are not necessarily directly related to lipid or carbohydrate biosynthesis  [56, 63, 64].
Acknowledgments The authors are grateful to the anonymous reviewers for their critical comments, which have greatly improved the manuscript. We would like to thank  Ms Helen McCombie  for revising the English of the manuscript. We thank  JJ. Maoret  and  F. Martins  from the  PlateformGeT of the Genotoul (Toulouse, France)  (http://get. genotoul. fr/)for the q-RT-PCR experiments using Fluidigm Biomark technology. We also thank the  Biogenouest genomic platform  for transcriptome sequencing. This work was supported by the  French region of Pays de la Loire  and the  French Research Institute for Exploitation of the Sea (IFREMER). There are no non-financial competing interests. The funders had no role in study design, data collection and analysis, the decision to publish or preparation of the manuscript.
FTIR spectroscopy and FTIR spectroelectrochemistry Transmission FTIR spectra were obtained using a Vertex 80v FTIR spectrometer from Bruker Optics with an N 2 cooled mercury cadmium telluride (MCT) detector. All sample preparations were performed under strict anaerobic conditions. Samples were immobilized between CaF 2 windows and measured in a continuously purged sample chamber. Spectra were recorded with 20 kHz velocity in double-sided forward backward mode with phase resolution of 16, zero filling factor of 2 and Blackman-Harris threeterm apodization. Final data processing was performed using home-written scripts in the Matlab ® programming environment. FTIR spectroelectrochemistry was carried out as previously described, but without use of redox mediators  [15]. Spectra were recorded on a Bruker IFS 66v/s spectrometer with N 2 cooled MCT detector with an aperture of 2.
Acknowledgements The  Max Planck Society  is gratefully acknowledged for financial support. The  French State Program 'Investissements d'Avenir (Grants "LABEX DYNAMO ",  ANR-11-LABX-0011 , and " LABEX ARCANE ",  ANR-11-LABX-0003-01 ) supported this work. Open access funding provided by  Max Planck Society. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.