dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of NRep and NS, the average transcript levels across the corresponding DNRS were calculated for each gene for heatmaps creation (Figures  4 5 6 ). To strengthen the information given by the transcriptomic analysis, metabolic profiling analysis was performed through LC-MS on samples corresponding to 4, 5, 6, 8, 10, and 11 DNRS, which all allowed the detection of 4,267 compounds (p-value ≤ 0. 05) (Supplementary Dataset 2). Individual compound cell quota change kinetics showed that 2,867 compounds displayed a significant variation (|log2FC| ≥ 2) compared to controlled conditions (before DRNS) for at least one time point. A PCA analysis was performed taking into account these 2,867 compounds (Figure  3B ), thus, resulting in a similar pattern as observed on a transcriptomic level. A little shift was observed corresponding to the translation of the transcript changes at the metabolic level, and a great change observed was also observed on days 10 and 11.
Thiriet-Rupert et al. (2018)  reported that the PLAAOx encoding gene is coexpressed with the gene encoding the coccolith scale associated protein (CSAP) during NS in Tisochrisis lutea, which was also observed in P. tricornutum (R = 0. 9963, R 2 = 0. 9926). An increase in transcript levels was observed for genes encoding proteins involved in nitrogen recycling such as deaminases, acetamide, and formamidase  (Alipanah et al. , 2015). The genes encoding for transporters of different nitrogen-containing molecules, such as polyamines and small polypeptides, were also upregulated in NS (Supplementary Dataset 1).
Amino Acid Metabolism The total cell protein quota started to decrease after the RNS, reflecting both an increase in protein degradation and a decrease in protein synthesis. Upregulation of the genes encoding ubiquitin ligases, the enzymes that initiate the protein degradation and two 26S proteasome regulatory subunits (Subunit N3, EG02475, isoform 2 of the subunit 5, and EG0162) was observed in NS. Moreover, an increase in transcript levels encoding components of the ubiquitin-mediated protein degradation pathway was observed (Supplementary Dataset 1). Coherently, a decrease in transcript abundance for genes encoding aminoacyl-tRNA synthetases, ribosomal proteins, while translation and elongation factors were also observed. The overall transcript availability of genes involved in the biosynthesis of amino acids was reduced in NS conditions (Supplementary Dataset 1), with the sole exception of the serine and glycine, glutamine and glutamate biosynthetic pathway involved in photorespiration and central metabolism (Supplementary Figures  8, 9 ).
Pan et al. (2017)  reported MCC2 as a key enzyme for which the silencing of the encoding gene (J19329) caused a decrease of carbon flux toward the TCA cycle, resulting in a decrease in TAG accumulation and nitrogen utilization. A change was also observed for the 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) transcript abundance, which significantly increases in NS (Supplementary Figure  10 ). Aside from proteins, other molecules such as polyamines require N for their synthesis. Polyamines are playing an essential regulatory function and are synthesized in all organisms including diatoms  (Lin and Lin, 2019). The overall transcript abundance of the genes involved in the synthesis of the polyamines was decreased from the onset of the transition to the establishment of the NS phase (Supplementary Dataset 1).
position sn-2. Transcript abundance of genes encoding PLA1a and PLA2 significantly increased at the end of the transition phase (7 DRNS) and remained upregulated throughout the NS. In line with what was reported by  Abida et al. (2015) , a decrease in transcripts encoding MGDG synthase (MGD) and an increase in DGDG synthase (DGD) was observed, suggesting a reduction in the MGDG synthesis and its conversion to DGDG for further degradation. Decrease of membrane lipids, especially plastidial membrane lipids such as MGDG and DGDG, was also observed from a metabolomics point of view (Supplementary Dataset 2). Moreover, two putative DAG forms were shown to decrease in the NS phase and are in line with the hypothesis of the remodeling of lipids toward energy storage ones (Supplementary Dataset 2).
Lipid Storage Due to their non-polar nature, neutral lipid accumulation requires proper storage in hydrophobic subcellular structures called lipid droplets (LD), which are characterized by specific LD proteins (LDP)  (Goold et al. , 2015; Lupette et al. , 2019; Leyland et al. , 2020). Two different LDP are present in P. tricornutum, StLDP (Stramenopiles LDP) and in PtLDP1 (P. tricornutum LDP1)  (Wang et al. , 2017; Lupette et al. , 2019; Leyland et al. , 2020). While PtLDP1 transcript abundance gradually decreased from the onset of the transition phase with a minimum at 15 DRNS, the StLDP encoding gene is overexpressed, mirroring the PtLDP1 expression pattern (Supplementary Dataset 1). Wang et al. , (2017)  reported that PtLDP1 upregulation resulted in an upregulation of DGAT2, GPAT, and FAS encoding genes, while in this experiment, an overall downregulation is observed for these genes (Figure  5 ).
Moreover, a putative seipin homologous encoding gene (J47296) was mostly upregulated during the transition phase (Supplementary Dataset 1). The activity of seipins is important for LD biogenesis and maturation  (Lu et al. , 2017). FIGURE 6 | Transcriptional changes of the genes involved in glycolysis/gluconeogenesis, pentose phosphate pathways, the synthesis and degradation of chrysolaminarin. Heatmaps are reported when changes in gene expression were observed in at least one of the isoforms. Mitochondrial and plastidial transcriptional changes are reported as overall change, the detailed pathway and transcript abundance change are reported in Supplementary Figure  12. Full information on the gene expression change during the time-course of the experiment is reported in Supplementary Figure  13.
Other pathways parallel to glycolysis exist: one of these being the pentose phosphate pathway (PPP)  (Kroth et al. , 2008)  and the other is an alternative to PPP which involves the xylulose-5-phosphate/fructose-6-phosphate phosphoketolase (XPK)  (Fabris et al. , 2012). The gene encoding this enzyme that is involved in these two pathways was upregulated only during the transition phase (Supplementary Dataset 1). Pyruvate, the product of glycolysis and alternative pathways, can then enter the tricarboxylic acid cycle (TCA) via two ways (Supplementary Figure  13):  (1) through the conversion to acetyl-CoA by the activity of the pyruvate dehydrogenase complex (PDH), or (2) through the conversion to oxaloacetate by the action of the pyruvate carboxylase (PYC). The overall genes involved in the TCA were upregulated in NS, with an overall expression peak at 8 DRNS (Supplementary Figure  13 ).
Photosynthesis and Carbon Fixation Light Harvesting Light harvesting complex (LHC) proteins are functional components of photosystems having different functions including light harvesting, photosystem regulation, and photoprotection  (Bassi and Caffarri, 2001; Scarsini et al. , 2019). In NS, most of the LHC genes were downregulated with few exceptions including LHCX4 and LHCR10. These two transcripts, together with the high-light inducible protein 1 (HLIP1) transcript, were immediately overrepresented at the onset of the transition phase (Figure  1 ). Four other genes were upregulated in these conditions: LHCF6, LHCF9, and LHCF15 were transiently upregulated during NS, whereas HLIP1b was transiently upregulated at the onset of NS. An overall strong downregulation was observed for the nuclear-encoded PSII reaction center (psb) genes involved in photosynthesis and photosynthetic electron transport chain (Supplementary Dataset 1).
Photochemistry and Carbon Assimilation The majority of the transcripts involved in photosynthesis were downregulated, in particular, a strong reduction was observed for the gene encoding the chloroplastic ferredoxin-NADP reductase (J23717) responsible for the photosynthetic production of NADPH, confirming the observations reported by  Alipanah et al. (2015). In general, NS induced a strong reduction in transcript availability of most of the genes involved in the Calvin-Benson-Bassham cycle (Supplementary Dataset 1), while an overall increase in transcript abundance was observed for genes involved in the central PEP-pyruvate hub and for genes involved in the biochemical C4 cycle. High expression levels in NS were observed for the genes encoding the phosphoenolpyruvate carboxylase 2 (PEPCase2), as well as its antagonist, the phosphoenolpyruvate carboxykinase (PEPCK).
An increase in the transcript levels of genes encoding the malic enzyme (ME) and the 2-oxoglutarate: malate exchanger (EG02645) was observed in NS with two major expression peaks at 8 and 15 DRNS. While the malate exchanger encoded gene was upregulated, an overall downregulation was observed for the 4 plastidial triose phosphate transporters encoding genes (TPT1, TPT2, and TPT4a/b;  Moog et al. , 2015). Most of the carbonic anhydrase (CA) genes were downregulated especially in NS with the sole exception of the CA1, which gene expression doubled at the onset of the NS phase (Supplementary Dataset 1). Huang et al. (2015)  observed an increase in serine and glycine de novo biosynthesis via photorespiration, highlighting the potential importance of photorespiration in N redistribution in NS conditions.
Our transcriptomic analysis showed an increase in several gene encoding for components of the photorespiration pathway. In fact, an increase in the transcript levels of genes encoding for serine/alanine:glyoxylate aminotransferase (J49601 and J43874) and the gene encoding for the glycine decarboxylase (EG02292) was observed (Supplementary Dataset 1). These two enzymes respectively converted the toxic glyoxylate in glycine than in serine, releasing one molecule of CO 2 and one molecule of ammonia  (Kroth et al. , 2008; Kunze and Hartig, 2013)  (Supplementary Figure  8 ).
Photosynthetic and Photoprotective Pigment Synthesis As expected, in NS, the P. tricornutum culture color faded from a pronounced brown-green to a very light yellow recalling the chlorotic phenotype observed in other photosynthetic organisms  (Allen and Smith, 1969). The transcript abundance of most of the genes involved in the chlorophyll a biosynthetic pathway was decreased in this condition (Supplementary Dataset 1). The exception was observed for CHLC_2 and POR4, which were upregulated in NS. Simultaneously, genes encoding for the carotenoid biosynthesis were downregulated in NS. Moreover, lowering the N availability triggered a reduction of the transcripts levels corresponding to two main key enzymes of the xanthophyll cycle, violaxanthin de/epoxidase, and zeaxanthin epoxidase  (Scarsini et al.
, 2015). An equilibrium is reached when the actual N availability is enough to maintain a minimum level of cell division rate. Even if the parameter guiding the NRep to NS transition is N-int, cells are not unaware of the changing environmental conditions before the trigger point. Indeed, 69 genes were differentially regulated at 4 DRNS including PLAAOx and CSAP encoding genes and one transcription factor of the heat shock family (HSF) (Supplementary Dataset 1).
In NS conditions, ribulose cannot be transformed into 3phosphoglycerate but could enter the photorespiratory cycle  (Sharkey et al. , 1988; Bailleul et al. , 2015). This is demonstrated by the increase of DIC (also observed by  Valenzuela et al. , 2012) , the lack of increase in cell carbon quota (Figure  1C ), the changes in expression of genes encoding Calvin-Benson-Bassham, and photorespiration components (Supplementary Dataset 1). This interpretation is further supported by the upregulation of the genes coding the enzymes involved in the photorespiratory pathway such as the alanine:glyoxylate aminotransferase (J49601, Supplementary Dataset 1). A reduction of the plastidial activities follows a reduction of its energy content. Despite being exposed to low nitrogen availability for longer than 25 days, P.
ACKNOWLEDGMENTS The authors would like to thank  Ewa Lukomska  and  Gaël Bougaran  ( Physiology and Biotechnology of Algae Laboratory, IFREMER, Nantes, France ) for the C and N quantifications, as well as  Manoela Sibat  ( Phycotoxins Laboratory, IFREMER, Nantes, France ) for the technical help during metabolomic analyses. The authors are grateful to  Le Mans University  for the Ph. D. grant to MS and the post-doctorate position to ST-R. The Fondation BPO, the  Institut Universitaire Mer et Littoral  (IUML-FR 3473 CNRS),  Le Mans University  and the  University Bretagne-Loire (UBL)  are thanked for financial supports.
DATA AVAILABILITY STATEMENT The transcriptomic data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB47181 (https://www. ebi. ac. uk/ena/ browser/view/PRJEB47181). The metabolic profiles are publicly available on the online infrastructure Ifremer Sextant catalogue of referential data from marine environments (https://doi. org/10. 12770/38e00808-ffca-4266-943f-b691d3ca0bec). Data processing and statistical analysis of the metabolic profiles can be found at SANOE repository (https://doi. org/10. 17882/84209). The data supporting the findings of this study are available from the corresponding author, Benoît Schoefs, upon request.
Acknowledgments: This work was funded by the  Fuel Cells and Hydrogen 2 Joint Undertaking  under grant agreement no. 779366. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program ,  Hydrogen Europe  and  Hydrogen Europe  research. The work was supported by the  U. K. Engineering and Physical Sciences Research Council  under project  EP/P024807/1. interpretation of XANES and EXAFS results, M. P. and P. S. conducted CO chemisorption measurements and data analysis, A. M. B. and D. F. carried out large-size single cell tests, and G. D. performed EDSX-STEM and atomic-resolution STEM measurements and data analysis. A. M. , F. J. and A. K. wrote and edited the manuscript with feedback from all the contributing authors. A. K. acted as the project supervisor.
Power Profile In order to study the shedding coefficients, arbitrary power curves were selected (Figure  4 ). As can be seen, the maximum power provided by the WT was limited and fixed to 600 W, which was the maximal limit of the studied SSWD. The load power curve was fixed at 1000 W. The PV power curve was calculated according to the weather data recorded on the 29 June 2019 in Compiegne. In addition, the selected simulation time horizon was from 07:00 until 20:00. During this period, the DC MG operated only in on-grid mode. are two coefficients that take into account the costs of shedding PV and SSWD powers, respectively. = + = + +    _ _ _ _ max( , ) WD WD WD PV MPPT WD MPPT PV S WD S p p c c (31) +=  1 PV WD (32).
Power Profile In order to study the shedding coefficients, arbitrary power curves were selected (Figure  4 ). As can be seen, the maximum power provided by the WT was limited and fixed to 600 W, which was the maximal limit of the studied SSWD. The load power curve was fixed at 1000 W. The PV power curve was calculated according to the weather data recorded on the 29th of June 2019 in Compiegne. In addition, the selected simulation time horizon was from 07:00 until 20:00. During this period, the DC MG operated only in ongrid mode. The choice of such power profiles was to analyze the optimization of the DC MG in the case of production excess. In the opposite case, PV sources and the SSWD were considered as a single source of renewable energy since the amount of power that they produced was completely consumed.
Conclusions The integration of a SSWD into a DC MG connected to the grid requires a deep understanding of many parameters, including factors related to energy and power management. In this article, the supervision control design was described. In this context, a strategy for optimizing the MG that permits transactions with the main distribution network and the participation of the end-user customers has been suggested. The optimization and minimization of the total operating cost were investigated by dispatching power flow according to the power profiles and measurement data under the constraints of the modeling. Indeed, in the case of the power excess produced by PV sources and the SSWD, it is crucial to perform the power limitation to ensure power balancing, which is the main goal of the supervision.
As with all Horizon Scanning exercises, our dataset is thus highly contingent on the precise group of participants recruited. As noted elsewhere in the Horizon Scanning literature (e. g. ,  Orr et al. , 2022) , a different set of participants would have resulted in a different set of questions. It is worth highlighting though, that this is not necessarily a weakness of the methodology, for two reasons. First and more generally, all qualitative, deliberative methodologies share this feature of data being contingent on participants' unique perspectives, positions and experiences. Second, in attempting to systematically canvas as diverse a field as possible, we have made every reasonable attempt to gain as representative a sample of participants as possible within the practical confines of a single research study.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 826025  ( Energy-SHIFTS  project). We are grateful to  Quentin Genard  ( European Climate Foundation ),  Manon Dufour  ( E3G ),  Catherine Cooremans  ( Université de Lausanne ), and  Aled Jones  and  Emma Milroy  (both  ARU ) for their insights and support during the  Horizon Scanning exercise. We also thank our many energy-SSH colleagues for kindly submitting their research questions for consideration. Note that the first four co-authors acted as the Steering Committee for this Working Group, the fifth and sixth co-authors contributed to the conceptualisation and methodology of the Energy-SHIFTS Working Groups, and the latter 27 (alphabetised) co-authors kindly contributed in the capacity of Working Group members.
Genome Sequencing and Annotation We sequenced one diploid clone of D. magna from each of 36 locations in central and western Europe and the Middle East, and Eastern Asia (fig. 1 ) to high coverage (mean ¼ $44X, std. dev. ¼ 20. 1) using the Illumina platform. Raw data are available under accession PRJNA480405. We mapped reads to the D. magna reference genome (daphmag2. 4; GenBank GCA_001632505. 1 PRJNA298946). To provide outgroup sequences, we additionally sequenced the genomes of D. magna's close relatives, D. similis, and D. lumholtzi using Illumina short paired-end sequencing. Although not as contiguous as the D. magna genome, these outgroup sequences provided a comparable degree of biological completeness in coding sequences. To provide the primary outgroup sequence, we assembled a draft D.
similis genome using MaSuRCA  (Zimin et al. 2013 ) and identified the proteincoding sequences using both highly conserved BUSCO, related species, and species specific RNAseq data (BioProject accession number PRJNA533017). Using ORTHO-mcl  (Li et al. 2003) , we identified $12,000 putative 1:1 orthologs between D. magna and D. similis protein-coding genes. We aligned these as proteins, and excluded those with greater than 0. 6 synonymous substitutions per site, which probably represent unidentified paralogs. After filtering, $11,000 coding sequences with a total length of $20 Mb remained. This represents approximately 42% of the protein-coding sequences originally annotated in D. magna 2. 4 reference, suggesting either that a large proportion of D. magna proteins are recent in origin and so lack 1:1 orthologs with D.
melanogaster, Apis mellifera, and Anopheles gambiae are similarly strongly deleterious  (Palmer et al. 2018). However, D. magna displayed a larger estimated fraction of very weakly deleterious mutation with 19% having N e s < 1 as compared with only 3-6% in Drosophila, Apis, and Anopheles. This high proportion of very weakly deleterious mutations is consistent with the observation that p A /p S > K A /K S , as weakly deleterious mutations are likely to persist on average in the population for longer periods than more strongly deleterious mutations, which are more quickly removed by selection. We also explored potential alternative distributions for the DFE using the program multiDFE  (Kousathanas and Keightley 2013). Using MultiDFE, we identified two-step and three-step distributions to be a better fit, based on the Akaike Information Criterion (supplementary table 1, Supplementary Material online).
We did this in three ways. First, we used a maximum-likelihood implementation of the multigene MK test to estimate overall a and a for each class of gene function  (Welch 2006). Second, to mitigate the impact of weakly deleterious variants, but conditional on a shared demographic history for the different gene classes, we used the software DFE-alpha  (Keightley and Eyre-Walker 2007; Eyre-Walker and Keightley 2009)  to infer the DFE, a, and x a , for each gene class and tested among them using likelihood ratio tests and permutation tests. Note that, in light of our simulations (above), we chose to limit our analysis to the more widely used DFE-alpha rather than to explore a wider range of functional forms for the DFE. Third, we used a SnIPRe-like analysis, which recasts the MK test as a   1 ; see supplementary table 3, Supplementary Material online for observed values).
MBE Animals were moved out of antibiotics and into 1. 5-ml Eppendorf microcentrifuge tubes and excess fluids removed with a sterile pipette. Extraction buffer (Qiagen GenePure DNA Isolation Kit) was subsequently added to the tubes and tissue was disrupted using sterile and DNA-free plastic pestle. The resultant solution was incubated overnight with Proteinase K at 55 C. RNA was degraded using RNAse treatment for 1 h at 37 C. Protein removal and DNA precipitation, including the addition of glycogen (Qiagen) to aid DNA precipitation, were done using the Qiagen GenePure DNA Isolation Kit instructions. Resultant purified DNA was suspended in 40 ll of Qiagen DNA hydration solution and subsequently tested for purity and concentration using a Nanodrop and Qubit 2. 0, respectively. Libraries were either prepared using Kapa PCR-free kits and sequenced by the Quantitative Genomics Facility service platform at the Department of Biosystem Science and Engineering (D-BSSE, ETH), in Basel, Switzerland, on an Illumina HiSeq 2000, or were provided to Edinburgh Genomics (NCBI BioProject number PRJNA480405) for library preparation using TruSeq DNA Nano gel free kits and paired-end 125 nt sequencing using HiSeq v.
4. Read quality was assessed using FastQC v. 0. 10. 1 (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc; released on March 5, 2012). Paired-end sequences were adapter trimmed and quality filtered using Trimmomatic v. 0. 36  (Bolger et al. 2014). After trimming of adapter sequences, terminal bases with a quality score below three were removed from both ends of each read. Then, using the sliding window function and again moving in from both sides, further 4 bp fragments were removed as long as their average quality score was below 15. Read quality was rechecked with FastQC to confirm quality and adapter trimmed succeeded. These high-quality reads were mapped to the D. magna reference genome (NCBI database; Assembly name: daphmag2. 4; GenBank assembly accession: GCA_001632505. 1, Bioprojects accession: PRJNA298946), consisting of 28,801 scaffolds, 38,559 contigs and a total sequence length of 129,543,483 bp) using BWA MEM  (Li and Durbin 2009; Li 2013) , the resulting sam alignment file being subsequently converted to a bam, coordinate sorted, and filtered for mapping quality !20 using SAMtools  (Li et al.