dataesr/scientific-paragraphs-categorization huggingface dataset:
The editing of the figure was based on  [62, 63]. We outlined a component architecture at the level of the technology/functioning enterprise in Figure  3. On the left-hand side, we list the necessary modules for an operating company, that is services that are specific to financial institutions. In the middle of the figure, we collected the services of information technology and infrastructure management that are essential for data processing and data analytics. The data storing architecture can be set up according to the data lake, but a wide spectrum of functions is required to realize efficient and effective data analytics. These functional services can be seen on the right side of the figure in the lower corner, and the techniques of the data analytics can be seen in Table  2. In the upper left corner of the figure, we describe an active symbiosis between the architecture of the data lake and data warehouse/mart that can store the data in a more disciplined and structured way so that the data are readily available for the tabulated structure as the input for the complex data analytics process.
The advantage of using data lakes lies in the rapid reaction with the right information out of the dataset. In the financial sector, banking, payments, and insurance, the concept of personal touch of services is becoming increasingly valuable and turns out to be the most relevant tool for marketing. Competition between competing companies forces businesses to use these personalized types of services. For instance, a personal recommendation can include how to save money by combining insurance policies or a specific investment construction, which, in combination with the investment, benefits the consumer, etc. (Figure  2 ). One of the limits of the application of data lakes will be precisely in this requirement of risk assessment. The issue is how to do it quickly, as the data have been transforming during the analysis.
Our study on the enterprise architecture for finance companies for that reason is a first and significant step in such a direction. The paper's contribution is that the disciplined, theoretically grounded architecture approach was applied for financial services technology companies and financial companies in transit during the digital transformation. We collected the techniques, methods, and algorithms from the field of artificial intelligence (data science, machine learning, and computational intelligence) and arranged them in an information architecture that is used in practice. This configuration yields a theoretically grounded pattern and a practical direction on how to apply it in real life. The theoretical results and the practical advice were grounded in empirical case studies and a qualitative literature research.
Conclusions and Future Directions Our paper was guided by the will to explore and understand the current state-of-theart in the domain of data science approaches employed by financial companies. First, we derived a literature review on the recent advances in the different categories of digital finance segments. Second, we analyzed the steps in the big data analysis process and the application scenarios as observed in the financial domain, with different examples of applications in market research and risk analysis as examples. We proposed an enterprise architecture for FinTech companies and proposed a process for the data collection, analysis, and exploitation of big data. Many researchers study technology issues related to only specific segments of digital finance-namely payments, cryptocurrencies, or consultants.
Acknowledgements The  MITI (Mission pour les Initiatives Transverses et Interdisciplinaires) program  of the CNRS is gratefully acknowledged for doctoral nancial support for R. D. This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) and  Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003. The  Nano-Bio ICMG (UAR 2607)  is acknowledged for providing facilities for analyses.
The two partial nucleotide sequences obtained were assembled to reconstruct the complete 18S rRNA gene sequence that was then compared to the NCBI database using BLASTn. In order to create a dataset of 18S rRNA nucleotide sequences of organisms belonging to the same clade than H. pluvialis (Chlamydomonadales), 37 sequences were imported from GeneBank and all aligned using Clustal 2. 1. The multiple alignment was subsequently used to perform a phylogenetic analysis. The best evolutionary model (GTR) for the dataset was determined using PhyML 3. 0 (http://www. atgc-montpellier. fr) and the phylogenetic analysis carried out using the maximum parsimony method. Nodal support of the tree was evaluated by a bootstrap analysis with 1000 replicates. The new 18S rRNA sequence has been deposited in GenBank under the accession number LT578169.
Before being treated by PEF, cells were collected after 8 days of growth and harvested by centrifugation (400 Â g, 5 min, 19 °C) (Fig. 1 ) (see  Scheme 1)  The cell diameter (Féret diameter) after PEF treatment was determined using ImageJ (https://imagej. nih. gov/ij/) using pictures of 15 bright field images per replicate representing around 5000 cells. Pictures were taken using a Zeiss LSM 800 microscope using the Zen2 software (Zeiss) calibrated with a scalebar. To avoid a temperature increase of the medium higher than 10 °C during the PEF treatment, the pellet was resuspended in distilled water in order to attain a medium conductivity of about 20 mS cm À1 (at the reference temperature of 25 °C). This conductivity is required to avoid possible damage to both the equipment and the microalgae  [18].
Five mL of each sample were loaded on a C18 pre-column (5 mm Â 300 mm; Dionex) at 20 mL/min in 2% acetonitrile, 0. 05% trifluoroacetic acid. After 5 min of desalting, the pre-column was switched on line with the analytical C18 column (15 cm Â 75 mm; in-house packed) equilibrated in 95% of mixture A (5% acetonitrile + 0. 2% formic acid in water) and 5% of mixture B (80% acetonitrile + 0. 2% formic acid in water). Peptides were eluted using a 5-50% gradient of B during 105 min at a 300 nL/min flow rate. The LTQ-Orbitrap was operated in data-dependent acquisition mode with the Xcalibur software. Survey scan MS spectra were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set to a value of 60000. The twenty most intense ions per survey scan were selected for CID fragmentation, and the resulting fragments were analysed in the linear trap quadrupole (LTQ).
Dynamic exclusion was used within 60 s to prevent repetitive selection of the same peptide. 2. 6. 3. 3. Bioinformatics analysis of nano-LC-MS/MS data. The MS and MS/MS data files were converted to the mzdb format and processed with the mzdb-access library (https://github. com/mzdb) to generate peaklists. Data were searched with Mascot (version 2. 6. 1, http://matrixscience. com) against a custom-made database containing all Chlamydomonas + Haematococcus pluvialis entries from the UniProtKB database (Swiss-Prot/TrEmbl release 20160321, 16 780 235 entries). The search included methionine oxidation as a variable modification, and carbamidomethylation of cysteine as a fixed modification. Specificity of digestion was set for cleavage after lysine or arginine for trypsin-digested samples, and one missed cleavage was allowed.
The mass tolerance was set to 6 ppm for the precursor ion. It was set to 0. 6 Da for fragment ions in CID mode (detection in the ion trap). Validation of identifications was performed through a false-discovery rate set to 1% at protein and peptide-sequence match level. The match level was determined by a target-decoy search using the in-housedeveloped Proline software version 1. 6 (http://proline. profiproteomics. fr/). The 1-DE gels were treated according to IPBS protocols for washing, trypsin digestion and drying. Dry peptide extract was taken in 14 lL of a 2% acetonitrile solution/0. 05% trifluoroacetic acid. For MS analysis, 5 lL of peptide extract was injected and the rest was stored at À20 °C until further use. The raw data were obtained after searching with Mascot Daemon search engine in the local data bank MicroMar (Chlamydomonas + H.
pluvialis on SP/ tremble; 03-18-2016). The created lists of proteins were validated using the Proline software. Briefly, the trypsin was used as a cleaving enzyme and one missed cleavage was allowed. The methionine oxidation has been defined as variable modification and carbamidomethylation of cysteines defined as fixed change. Data validation was performed with the Proline software. Peptides and proteins were confirmed by generating a random peptide Bank ''decoy", where the threshold of false positives (FDR) has been set at 1% for peptides and proteins. The proteins identified with exactly the same set of peptides (samesets) or part of the same set of peptides (subsets) have been grouped in the form of lists in Excel tables. Proteins of the same group were classified according to their protein score, their number of peptide sequences (specific or non-protein), and their number of tandem mass spectrometry (MS/MS) fragmentation spectra.
Therefore, the extraction yield could vary depending on the capacity of the cells to resist these forces (which likely depends on the properties of the cell wall) Alternatively, this difference could also be explained by a difference in axeny between the two strains. For the TCC932 strain, in addition to negative axenic tests (data not shown), no bacterial protein was detected in the supernatant by proteomic analysis (see Section 3. 6 and 3. 7). The yield of protein recovery by grinding after the post-treatment incubation period reached 46% of the water-soluble, extractable proteins. A similar yield was found by Coustets, Al-Karablieh, Thomsen and Teissié  [19]  with the microalga Nannochloropsis salina (Eustigmatophyceae) using 2 ms bipolar pulses with an intensity of 6 kV cm À1. However, these yields are much higher than those reported by authors working on other microalgae species.
The electro-extracted proteins are involved in many vital cell processes The predicted MW of the electro-extracted proteins ranged from 12. 4 to 164. 9 kDa. The range of extracted proteins is higher than the one reported by Postma, Pataro, Capitoli, Barbosa, Wijffels, Eppink, Olivieri and Ferrari  [26]. One could be surprised that high MW proteins were retrieved through PEF extraction. However, it is known that proteins as large as 98 kDa can be imported using a PEF treatment  [30]. Using the procedures developed in our study, nearly 50% of the electro-extracted proteins originated from chloroplasts. In addition, the diversity of proteins in P 1. 0 was higher than in P 0. 5 due to an increase of the permeation of the different cell compartments (Supplemental data 3). This result contrasts greatly with the results in  [31]  which displayed a destructive impact of PEF treatments, resulting in the extraction of mostly cytosolic proteins  [31].
Protein biosynthesis and trafficking We detected several electro-extracted proteins that are functionally involved in the cell's protein production pathway. In all organisms, ribosomes are responsible for the translation of mRNA into proteins. However, due to their endosymbiotic history, eukaryotic cells like H. pluvialis have ribosomes of prokaryotic origin in their chloroplasts and mitochondria. These ribosomal complexes consist of a large subunit (50S) and a small subunit (30S), both of which contain ribosomal RNAs and various ribosomal proteins. We found that the PEF treatment extracted several of these proteins from the chloroplasts and mitochondria (Supplemental data 3), for example two L7/L12-like proteins  [60]. The concentration of ribosomes in cells correlates with cell division rates because the cell's rate of protein production needs to increase when the cell division rate increases.
Among them, ClpC, the plant homolog of bacterial ClpA, has a MW of 100 kDa  [71]  which is very close to the MW of detected protein #48 (Supplemental data 3). Two S-adenosylmethionine synthases (EC 2. 5. 1. 6) were also electro-extracted (Supplemental data 3). They catalyze the formation of S-adenosylmethionine (SAM) from methionine and ATP  [72]. SAM serves as a methyl donor for transmethylation in many biosynthetic pathways, such chlorophyll biosynthesis and phytohormone biosynthesis. Because DNA methylation is an important factor controlling gene expression, these enzymes could also be important elements in the establishment of microbial stress responses -similar to their action in plants  [73]. 4. 3. 6. Stress response proteins 4. 3. 6. 1. Nucleic acids. RNA helicases are RNA degradosome enzymes that are involved in every step of RNA metabolism, including nuclear transcription, pre-mRNA splicing, ribosome biogenesis, nucleo-cytoplasmic transport, translation, RNA decay, and organellar gene expression, including the disruption of misfolded RNA structures  [74].
Acknowledgements The authors are grateful to  Dr F Denis  ( Muséum National d'Histoire Naturelle, UMR BOREA, Station de Biologie Marine de Concarneau, France ) for her help with the phylogenetic analysis and to  Dr J Gillard  ( Department of Biology, California State University, Bakersfield, USA ) for his critical reading of the text. The authors also thank the anonymous reviewers for their contructive comments. Funding HG thanks ' Les Collectivités locales de la Sarthe ' for her PhD grant. The authors thank the doctoral school VENAM, the  'College doctoral' of Le Mans University  and the  University of Le Mans  for their financial supports. This project was supported in part by the  Région Midi-Pyrénées, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ) and by the  Institut Universitaire ''Mer Littoral " (IUML--FR 3473 CNRS).
This adverse effect was less pronounced in the hlr1 mutant than in the WT, suggesting higher tolerance to strong illumination (Fig. 1c ). Prolonged HL induced a massive membrane rearrangement in WT cells with several plastoglobules also visible, while this effect was less evident in the hlr1 mutant, which maintained a relatively similar cell structure as under LL conditions (Fig. 1d  and Supplementary Fig. 2 ). The hlr1 mutant is defective in an HL-induced LHCR gene. A single band was observed by using the eHYG (hygromycin) probe in a DNA gel blot of the hlr1 genomic DNA, revealing a single transgene integration in the N. oceanica genome (Supplementary Fig. 3 ). The flanking genomic sequence retrieval (Supplementary Data 1) and genome-wide examination (transcriptomics datasets of the hlr1 mutant under darkness (DK) and HL conditions over three time points; see "Methods" for details; Supplementary Data 2) confirmed a single insertion site within the exon region of a gene (number 4928) encoding an LHC protein, which we designated as HLR1 (high-light resistance 1) (Fig.
oceanica strain IMET1 (IMET1-HLR1 i ) with depressed HLR1 protein levels (Supplementary Fig. 4  inset; see "Methods" for details of the generation and validation of these mutants). Like the original hlr1 mutant, the IMET1-HLR1 i (Supplementary Fig. 4 ) and 1779-HLR1 KO strains (Supplementary Fig. 5 ) were less sensitive to saturating light than their respective WT strains, firmly establishing the unambiguous relationship between highlight resistance and HLR1 deficiency. Topology prediction of the HLR1 supports a folding model with three transmembrane helices (TM1, TM2, and TM3 in Fig. 2d ), a stroma-exposed N-terminus, and a lumen-exposed C-terminus (Fig. 2d ). Hidden Markov model-based searches suggest structural similarity of HLR1 to the PSI-LHCI from a green alga Bryopsis corticulans (PDB entry 6IGZ in Pfam; http://pfam.
c Immunoblot analysis of HLR1 protein expression in WT and hlr1. Protein samples from the hlr1 mutant were taken from dark-acclimated cells or following high irradiance for the indicated time. Antibodies against glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were used as a loading control. PC, positive controls (recombinant HLR1 proteins expressed in E. coli). d The folding model of an HLR1 monomer with a view from a direction parallel to the membrane plane. Letters in red show the transmembrane helices. e Phylogenetic analysis of light-harvesting complex (LHC) proteins. Four subgroups were collapsed (LHCR7, LHCR4-LHCR6-LHCR8, LHCF1, and LHCX/LHCSR). HLR1 -LHCR5 subgroup was not collapsed. LHCA sequences were chosen as outgroup. Scale bar, 0. 3 estimated substitutions per site. See Supplementary Data 6 for the full protein list obtained from the Uniprot and PhycoCosm databases.
In addition, nested primers AP2 and 5B2 were used for a second-round PCR. To confirm the results of nested PCR, standard PCR was performed using primers amplifying regions across the ends of the insertion and the flanking sequences (101gi-F2 and 101gi-R3). HLR1 phylogeny and topology predictions. Phylogenetic analysis of the N. oceanica HLR1 was performed using amino acid sequences obtained from the Uniprot and PhycoCosm databases. Proteins from all genomes were blasted with the HLR1 protein queries using the standalone BLAST software package. The sequences that were ultimately selected are listed in the Supplementary Data 6. Gaps and ambiguously aligned sites were removed using CLUSTALW 2. 0 42 , gBlock  43  , and manual validation. ProtTest 3  44  and PhyML 3. 0  45  were used to select the available model of protein substitution and phylogenetic analyses with a maximum likelihood method.
More than three biological replicates were examined for each treatment. DNA gel blotting and gene expression analysis. The N. oceanica HLR1 gene structure was validated by PCR from cDNA and genomic DNA (primers gNoHLR1-F and gNoHLR1-R; Supplementary Data 5). Integration of the transforming cassettes within the genome was analyzed by DNA gel blotting using nonradioactive DIG-containing probes generated by PCR (PCR DIG probe synthesis kit; Roche Diagnostics). Genomic DNA was extracted from microalgal cells using the Plant Genomic DNA Extraction Kit (Omega Bio-tek, Norcross, GA, USA). Ten micrograms of DNA were digested by HindIII or PstI and separated on 1% agarose gels, followed by a transfer to membranes. DNA gel blots were then hybridized, washed, and developed. To produce cDNA templates used for qPCR, the total RNA was purified with the Eastep Super Total RNA Extraction Kit (Promega) and then subjected to genomic DNA digestion and reverse transcription by using PrimeScript RT reagent Kit with gDNA Eraser (Takara).
The total RNA of the algal cells was prepared using an RNA miniprep kit (CWBIO) and the concentrations were measured using the NanoDrop 2000 spectrophotometer (ThermoFisher). Libraries were constructed and then sequenced using the HiSeq 2500 (Illumina), which generated about 18 million read pairs per sample (ANNOROAD). Raw reads containing adapter, poly-N, and low-quality reads were filtered, and the effective data were mapped with the Nannochloropsis reference genome using TopHat (version 2. 0. 12). After excluding the ribosomal RNA or transfer RNA, we estimated the abundance of the transcripts using RPKM (Reads per Kilo bases per Million reads)  60. The P values were adjusted using the Benjamini and Hochberg method  61. Corrected P value < 0. 05 and fold change more than two were set as the threshold for a significant difference in expression.
Acknowledgements We thank  Thomas Roach  (The  French Alternative Energies and Atomic Energy Commission, CEA)  for assistance on hydrogen peroxide measurements,  Alessandra Bellan  ( Universita' di Padova ) on JTS-10 spectrophotometer analysis, and  Eva Farré  ( Michigan State University ) for providing the pNOC-CRISPR vector. We thank  Dr. Jian Xu  ( Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences ) and  Wenqiang Yang  ( Institute of Botany, Chinese Academy of Sciences ) for very helpful discussions. Y. L. and Q. G. gratefully acknowledge funding from the  National Natural Science Foundation of China  ( 32060061 ), the  Basic and Applied Basic Research Programs for the Talents of Hainan Province  (grant no. 2019RC033 ), the  Project of Innovation & Development of Marine Economy  (grant no.
Open Energy Modelling While historical and current mainstream approaches in energy modelling are proprietary solutions and lack transparency, open energy modelling is a promising emergence  [48]. A growing number of energy modellers put into practice open-source principles and gather in communities such as the open energy modelling initiative  [49, 50] , whose wiki identifies open models  [51]. Various levels of openness can be differentiated, from transparency with datasets and model documentation available, to open development with available code and data as well as open-source development principles  [52]. "Open" here refers to models, code, and data that can be freely accessed, used, modified, and shared by anyone for any purpose  [53]. While closed models prevent easy review  [54] , model comparison  [13, 15] , or third-party contributions  [27] , open energy modelling has many advantages.
4. OMEGAlpes is developed in the freely available scripting language Python with the Apache 2. 0 licence  [73] , a permissive open-source licence only requiring attribution to the source code authors. OMEGAlpes source code is available on a Gitlab repository  [74]  and its documentation on the readthedocs website  [75]. OMEGAlpes leaves the choice of solver to the user through the PuLP package  [76] : the use of open-source solvers does not limit the accessibility to professionals or academic users  [7] , while the possibility of using proprietary solvers can greatly improve the tool performance, as open-source solvers are often outperformed by commercial ones  [77]. A factsheet presenting OMEGAlpes is available in the Open Energy Platform factsheets library [78].
6. OMEGAlpes includes an actor package, modelling the energy stakeholders' objectives, constraints, and responsibility scope. The energy and actors' models can be quickly adapted to the stakeholders' needs. This, in addition to its openness and abstraction level, makes its models relevant for negotiation support. Currently, OMEGAlpes is indirectly used for policy support in the manner of Chang et al. [7] : models are used for negotiation and advice in a waste heat recovery project with a local laboratory involved in the modelling, as well as the local public authority and district heating network operator. The French Agency for ecological transition (ADEME) has also assessed the OMEGAlpes tool, its methods, and its use cases. Regarding its other general characteristics, OMEGAlpes includes unit and integration tests in order to ensure OMEGAlpes source code behaves as intended, which is particularly relevant for collaborative development.
This enables the user to specify an energy unit type and related input parameters depending on his needs. For example, FixedEnergyUnit type needs an energy profile as an input, and ShiftableEnergyUnit type needs a time profile that can be shifted on a given horizon. Constraints and objectives also inherit from dedicated classes of the general package. Constraint types will be detailed in the actor package sub-section (see Section 3. 3. 2). Once again, please refer to Appendix A for a comprehensive vision of OMEGAlpes structure. Energy units are linked together using energy nodes (EnergyNode). Each energy node must ensure energy balance between the units connected to it, as well as the connection of a single energy carrier. Thus, the energy optimisation problem at the district scale is built on two levels: at the first level, by modelling the energy system through a set of energy units representative of its constituent elements; and at the second one, by ascertaining the energy balance through intermediary energy nodes.