dataesr/scientific-paragraphs-categorization huggingface dataset:
We used KaKs_Calculator  (Zhang et al. 2006)  to enumerate synonymous and non-synonymous sites, while transcriptome completeness was assessed using BUSCO v3. 0. 2b  (Simão et al. 2015)  and the arthropod odb9 lineage set from OrthoDB. We annotated the transcripts using queries against the Drosophila FlyBase database (dmel_r6. 38)  (Larkin et al. 2021)  with DIAMOND v9. 9. 0  (Buchfink et al. 2015).
SNP calling We measured genetic variation within nine species/lineages: M. norvegica, E. longirostris, E. similis and its variant form armata, E. spinifera, E. triacantha, E. vallentini, N. megalops and E. superba using Single Nucleotide Polymorphisms (SNPs) detected in the RNA-seq data. We mapped the trimmed RNA libraries from 113 specimens (3-20 specimens per species) to their respective reference transcriptomes using BWA-MEM v0. 7. 17-r1188  (Li 2013). Alignments were cleaned with samtools v1. 14  (Danecek et al. 2021)  to only retain reads mapping uniquely, concordantly and in pairs. Duplicates were removed using Picard v2. 23. 4 (http://broadinstitute. github. io/picard/). For each species, we called variants with GATK HaplotypeCaller v4. 2. 0. 0. Variants were called per-individual and combined into a multi-sample gVCF with CombineGVCFs.
Levels of intraspecific genetic variation The full biallelic SNP datasets (above) were annotated with SNPeff v4. 3T  (Cingolani et al. 2012) , i. e. to be synonymous, non-synonymous or UTR variants. We estimated variation using the population mutation rate Watterson's theta (θw)  (Watterson 1975)  separately at 5'-UTR, 3'-UTR, synonymous and non-synonymous sites, or jointly across full genes while accounting for accessible sites. We then used SplitsTree to calculate transcriptome-wide pairwise genetic distance matrices among individuals and deduce π (nucleotide diversity) and BioPerl to calculate Tajima's D (DT) as an indicator of demographic history  (Stajich et al. 2002). The effective population size (Ne) was inferred from Ne = θw/4μ, where μ is the mutation rate per base and generation.
Orthology and phylogenetic analyses We analyzed the 20 krill transcriptomes and seven outgroup datasets for gene orthology with ProteinOrtho v6. 0. 14  (Lechner et al. 2011) , using DIAMOND to detect similarities. For each set of orthologous genes (an "orthogroup" or "OG") with 10 or more krill species, we produced protein-level alignments. MAFFT was run using the G-INSI-I algorithm, using the "--allowshift --unalignlevel 0. 8 --leavegappyregion" variable scoring matrix settings to reduce the risk of over-aligning nonhomologous regions  (Katoh and Standley 2016). Single-copy OGs with at least 18 krill species and two outgroups were used to make a species tree. First, we trimmed unreliably aligned positions using Gblocks  (Castresana 2000)  (settings: "-t=p -b1=N -b2=N -b4=5 -b5=h -b6=y, where N was 50% + 1 of the number of sequences).
2020; Al-Saffar and Hahn 2022) , as well as the respective rates of adaptive or non-adaptive protein evolution (ωA or ωNA). For eight species with SNP data, we aimed to estimate the overall α, ωA or ωNA across thousands of genes. To estimate ω, we made 4-way datasets and pruned species trees that included the focal species and three additional species at different distances. We re-estimated branch lengths using the YN98+F1X4 model in Bio++ bppML v2. 3. 1  (Nielsen and Yang 1998; Guéguen et al. 2013) , selected an ancestral node at appropriate distance and used Bio++ bppancestor v2. 3. 1 to infer the gene sequence at the ancestral node. The node was selected from overall pairwise genetic distances between species, avoiding very short distances as shared ancestral polymorphism may distort substitutionestimates  (Mugal et al.
To have similar resolution of the SFSs between species and facilitate unbiased comparison, we downsampled the original population datasets to six or eight individuals. For each gene, individuals were selected to maximize the overall coverage and genotyped SNPs. We used the model-based program grapes  (Galtier 2016)  to estimate α, ωA and ωNA in each species. The program infers fitness-effects and fits either non-parametric methods or models from DoFE  (Eyre-Walker et al. 2006; Eyre-Walker and Keightley 2009)  to the SFS + divergence data under maximum likelihood criteria. The "Basic" method derives the statistics by comparing dN/dS to pN/pS without correction, while "FWW" removes non-synonymous polymorphisms segregating at low frequencies (<15%)  (Fay et al. 2001 ). In addition, grapes implements six model-based methods that estimate population demographics and fitness effects to correct for selectively neutral or fitnessaltering non-synonymous polymorphisms (see Galtier 2016 and Al-Saffar and Hahn 2022 for details).
Acknowledgements This research was supported by grants  2018-04444  from the  Swedish Research Council Vetenskapsrådet  and the  Inez Johanssons scholarship foundation  awarded to AW. The computational infrastructure was enabled by project  SNIC 2022/5-472  provided by the  Swedish National Infrastructure for Computing (SNIC)  at UPPMAX and the  PDC Center for High Performance Computing ,  KTH Royal Institute of Technology , partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. We thank  Dr N. Tremblay  ( Quebec university, Rimouski UQAR ) for kindly providing Pacific Ocean krill samples,  Camille Merland  ( Université Pierre et Marie Curie ) and  Marion Thellier  ( LOCEAN-IPSL, CNRS ) for help with species identifications, the helpful research vessel and marine station staff that supported field sampling,  Julien Yann Dutheil  ( Max Planck Institute for Evolutionary Biology ) for helpful discussions and  Mårten Larsson  ( Uppsala University ) for folding the Peropsin protein.
The REPCCOAI (Responses of the Pelagic Ecosystem to Climate Change in the Southern and South Indian Ocean) surveys were directed by  Philippe Koubbi  ( Roscoff Biological Station ) and J-Y. T. These surveys were supported by the  French oceanographic  fleet, the  CNRS Antarctic   Workshop Zone , the  CNES   KERTREND-SAT OSTST  project led by  Francesco d'Ovidio (LOCEAN) , the  European MESOPP H2020 programme  and the  TAAF National   Nature Reserve programmes. We thank  Philippe Koubbi  for the opportunity to take part in these surveys.
Contributions The main contributions of this paper are as follows: (1) Benefits of overplanted OWF with DTR are quantified for the case when revenue/cost from both DA energy sales and imbalances are considered. (2) The "business as usual" P50 commitment strategy is compared with other commitment strategies in the context of overplanting, and its combination with DTR. (3) Economic metrics such as NPV, LCOE and discounted payback periods are estimated for low and high market prices. (4) The economic benefits of DTR for export cables over the lifetime of an overplanted OWF are estimated. The paper is organized as follows: Section 2 details the models and data used in our paper, Section 3 presents the methodology and Section 4 describes the results and discussion. Finally, Section 5 concludes our research.
Energy prices: day-ahead prices and imbalances prices The electricity generated by the OWF is considered here to be sold on the DA market and does not include any subsidy. It is also assumed that mismatches between the commitment and the actual production are purchased/remunerated at the imbalance prices. The ENTSO-E datasets for the period of January 13, 2018 -January 12, 2019, in France were used for both energy prices (both DA and imbalances)  [102, 103]. It is important to note that the wind production and price data are selected from two different countries (Belgium and France respectively), as no offshore wind farm was being operated in France (which our study targeted) when this research work was carried out. Although a price difference may be observed between France and Belgium, it was assumed that offshore wind production is still sufficiently small not to influence the market prices.
Thermal modelling of the export cable and validation For the type of an export cable given in Section 2, the maximum allowed temperature is equal to 90 • C. We assumed that curtailment of wind power is applied as soon as the temperature reaches this value. However, temperature monitoring devices may present a coarser precision of a few degrees. This may require to curtail a wind power at a few degrees less than 90 • C (a sensitivity analysis was carried out in this perspective in this paper). The electro-thermal model used in this paper was developed in MATLAB based on IEC standard 60853-2  [104]. The IEC 60853-2 model is based on a two-cell RC equivalent circuit which is shown in Fig. 6. The soil temperature is assumed to be always equal to 18 ℃ and the cable burial depth is 1. 87 m. As the IEC standard presents a sophisticated procedure with some ambiguities, we decided to provide the details of our model in supplementary material to be published soon on the project webpage  [105].
In this paper, however, we show only the validation results for this thermal model. Specifically, the thermal model was developed in MATLAB and then validated against temperature profiles provided by RTE (see Fig. 7 ). As a result of these simulations, the difference in temperature estimation between the MATLAB and reference data converges to zero, and never exceeds 2 • C. Hence, it is assumed that our MATLAB model is sufficiently precise at this stage for the simulations presented in this paper. Note, however, that the MATLAB model provided in open access  [106]  is a little bit different from the MATLAB model used in the simulations shown here, as the latter was additionally calibrated thanks to confidential data. Nevertheless, the only difference for the open-access model consists in several parameters whose absence would still result in similar temperature calculations as for a calibrated model.
Acknowledgement This research was conducted as a part of the " CELT4Wind " project carried out within the framework of the  WEAMEC, West Atlantic Marine Energy Community , and with the funding from the  CARENE  and the  Pays de la Loire Region. All these organizations are gratefully acknowledged. We highly appreciated the help from specialists  Guillaume Rovai  and  Victor Le Maire  from the Belgian system operator Elia for their help with the Belgian OWFs forecast and measured data. We would like to thank a lot  Alexandre Godard  and  Laura Cordebart  from  French TSO  for their technical assistance, recommendations and especially comments. The authors are very grateful to  Ekaterina Daminova  for her help with proofreading of this article. Last but not least, we would like to thank our colleagues  Didier Trichet ,  Laurent Dupont & Guillaume Wasselynck  from the  IREENA lab, Nantes University , for their technical advice and recommendations during numerous meetings.
Brain serotonergic (5-HT) signaling is posited to modulate neural responses to emotional stimuli. Dysfunction in 5-HT signaling is implicated in major depressive disorder (MDD), a disorder associated with significant disturbances in emotion processing. In MDD, recent evidence points to altered 5-HT 4 receptor (5-HT 4 R) levels, a promising target for antidepressant treatment. However, how these alterations influence neural processing of emotions in MDD remains poorly understood. This is the first study to examine the association between 5-HT 4 R binding and neural responses to emotions in patients with MDD and healthy controls. The study included one hundred and thirty-eight participants, comprising 88 outpatients with MDD from the NeuroPharm clinical trial (ClinicalTrials. gov identifier: NCT02869035) and 50 healthy controls.
The NeuroPharm clinical trial (NCT02869035) was approved by the local scientific ethics committee (H-15017713), the Danish Data Protection Agency, and Danish Medicines Agency (NeuroPharm-NP1, EudraCT number: 2016-001626-34). The study was conducted in accordance with the Declaration of Helsinki II, and Good Clinical Practice (GCP) guidelines. Fifty-two healthy controls with no axis I psychiatric disorder were included for comparisons. They were either recruited from an online recruitment site, or drawn from the Cimbi database, a quality-controlled central repository of the Neurobiology Research Unit, Rigshospitalet, Copenhagen  [44]. The healthy controls underwent the same PET and MRI protocols (i. e. [ 11 C]SB207145 PET and emotional faces fMRI paradigm) on the same scanners as the patients with MDD.
Faces stimuli were presented for four seconds with a variable inter-stimulus interval (two, four or six seconds, mean duration= four seconds). Shapes were also presented for four seconds and had a fixed inter-stimulus interval of two seconds. The paradigm was presented and behavioral responses were recorded using E-prime (Psychological Software Tools, Pittsburgh, PA, USA). As done in previous studies  [48] , patients with MDD and healthy controls were removed from further analyses if they performed with less than 80% accuracy on the task. Two healthy controls did not meet the aforementioned inclusion criterion and were excluded for the final analyses.
MRI processing and analyses Functional MRI data were preprocessed and analyzed using the Statistical Parametric Mapping 12 software (SPM12, https://www. fil. ion. ucl. ac. uk/spm/ software/spm12/). Functional images were corrected for slice-timing, spatially realigned, corrected for spatial distortions and co-registered to the individual's T1-weighted MPRAGE image. The Automated Anatomical Labeling (AAL3) atlas  [49]  in Montreal Neurologic Institute (MNI) standard space was warped to each individual participants' native space using the inverse deformation field generated during segmentation of the T1weighted MPRAGE image. Functional images were smoothed with a 4 mm full-width half-maximum Gaussian filter. Artefact Detection Toolbox (ART) http://www. nitrc. org/projects/artifact_detect) was used to identify and censor individual volumes with excessive motion (>2 mm) or variation (>4 SDs).
PET processing and quantification Motion-correction of the PET images were performed using AIR (version 5. 2. 5). The 3D T1-weighted MPRAGE image was co-registered to PET images using SPM8. Delineation of region of interest was performed using PVE-lab  [52] , and performed on the individual's T1-weighted MPRAGE image. The mean tissue time activity curves for the set of gray matter voxels within a region of interest (ROI) was extracted for kinetic modeling. Kinetic modeling using the simplified reference tissue model (SRTM)  [53]  with cerebellum (excluding vermis) was performed with PMOD software version 3. 0 (PMOD, Zurich, Switzerland). The calculated non-displaceable binding potential (BP ND ) served as the outcome measure for quantifying 5-HT 4 R binding. Mean BP ND from bilateral caudate, putamen, amygdala, insula, vmPFC and dlPFC were extracted.
Statistical analyses Analyses of demographic, clinical, and radiotracer data. Analyses were conducted using R (version 4. 1. 2)  [54]. Group differences in continuous (age, HAM-D 17 ,MDI, BMI, weight-adjusted [ 11 C]SB207145 injected mass (µg/kg)) and categorical (sex, 5-HTTLPR polymorphism) measures were assessed using Mann-Whitney U-tests and Fisher's exact tests, respectively. All results were considered significant at p < 0. 05. Association between 5-HT 4 R binding and BOLD responses to emotional faces. We applied a linear latent variable model (LVM) using the lava package  [55]  implemented in R to first examine the association between 5-HT 4 R binding and BOLD responses to emotional faces. LVMs are flexible structural equation models, which allow us to model complex hierarchical structures and summarize multivariate data into a single latent variable, enabling ease of interpretation as well as reducing issues related to multiple testing of correlated variables.
Other parts of the Parseme data, which are not built on existing UD data, are accompanied by an automatic morpho-syntactic annotation, obtained with UDPipe  (Straka et al. , 2016) , thus also following the UD annotation framework. This means that all annotated data from the Parseme project can be considered as multi-layer annotated data, with morphosyntactic annotations encoded following UD and VMWE. In this article, we propose an encoding of the two annotation layers in a common structure, using a graph encoding of both UD and VMWE annotations. With this encoding, it is possible to use graph-based tools to work with the data. In this work, we use our GREW tool  (Guillaume, 2021)  to make queries on the two layers. The Parseme 1. 3 data will be released on http://hdl. handle. net/11372/LRT-5124.
Multi-layer queries The benefit of having the two annotation layers in the same structure is that it is possible to make queries which refer to both layers and then to make cross observations. We use the GREW tool which allows to write graph queries that can be executed on the Parseme corpora represented has above. The tool is available in an online web interface: GREW-MATCH 6 on a predefined set of treebanks. A Python library, named GREWPY, is also available to use queries in scripts. We give a few examples of GREW queries on the Parseme graph encoding.
VWMEs by sizes We keep the same basic request used in the previous subsection. In GREW, the clustering key MWE. __out__ splits the occurrences returned by a request depending on the number of outgoing edges on the node MWE. Following the encoding described in Section 2, this corresponds to the number of tokens implied in the VMWE. Table  2  in appendix reports the sizes of the clusters obtained with this clustering for all languages. According to the notion of Multi-Word Expression, we do not expect to have one-token annotation as VMWE. All languages have a few number of such VMWEs (above 50 occurrences) except for four languages: Hungarian, Chinese, Swedish and German. In Hungarian data, there are 5745 one-token VMWEs; this is probably linked to the fact that Hungarian is an agglutinative language; among the cases, the same noun with lemma bekezdés 'paragraph' appears 995 times, it is tagged as VPC.
Ratio of overlapping VMWEs With a graph request, we can distinguish VMWE annotations with or without overlapping. The request below corresponds to the "without overlapping" case: Lines 1-3 is a request for any VWME. Lines 4-7 use the without construction of GREW which filters the output of a query: each occurrence of the basic query (line 1-3) which satisfies the constraint expressed in the without part is filtered out. In our example, cases where some MWE2 exists, which shares a token X with the one previously found MWE1 are removed. The result of the full query is then only the non-overlapping VMWEs. For the "with overlapping" case, the request is the same where the keyword without is replaced by the keyword with (line 4). Table  3  shows the ratio of overlapping VMWEs for each language.
Positional Identification of Carbon Backbones from Mass Fragments of TMS Derivatives For Glutamate_3TMS and Malate_3TMS, the identity of carbons contained in TMS derivative mass fragments was based on previously published studies that used metabolites labeled in specific positions  [29, 31]. For Proline_2TMS, we separately injected a nonlabeled proline standard and a commercial U-13 C-Proline standard ( 13 C isotopic purity of 99%, Cambridge Isotope Laboratories (Eurisotop, Saint-Aubin, France)). For other TMS derivatives, we retrieved mass tables from GC-MS spectra of the Golm Metabolome Database corresponding to the injection of either 13 C-reference substances or 13 C-analytes produced with a determined 13 C isotopic signature  [37, 38]. For each TMS derivative and each fragment analyzed, the mass isotopic cluster was used to calculate the area distribution for each m/z expressed as a percentage of the total area for the mass isotopic cluster (relative intensity from 0 to 100%).
The second experiment consisted of the injection of 48 polar metabolite extracts from incorporation kinetics of U-13 C-pyruvate into Brassica napus (B. napus) leaf discs in either light or dark conditions for up to 6 h (4 biological replicates per condition and per timepoint)  [26]. The previous processing of the spectra for all samples comprised the adjustment of the retention time with a mixture of alkanes and the peak identification with respect to authentic standards derivatized and by reference to GC-EI-MS mass spectra of TMS derivatives in the US National Institute of Standards and Technology database and in the Golm metabolome database  [38]. Then, the spectra were processed again with a modified method based on the new fragments presented in Table  1. Raw areas were subjected to correction for the contribution of naturally occurring isotopes of all elements except the carbon arising from the metabolite backbone using the IsoCor v2.
1. 4 package of Python (https://github. com/MetaSys-LISBP/ IsoCor, accessed on 20 February 2023) in low-resolution mode. For samples arising from U-13 C-pyruvate incorporations, an additional correction was performed to account for the contribution of naturally occurring isotopes of the carbon arising from the metabolite backbone and the isotopic purity of the tracer U- 13 C-pyruvate (99%). Practically, this correction led to a value of 0% if labeling was at natural 13 C abundance (1. 1%) and a value of 100% if labeling was at the maximal possible 13 C enrichment. Samples of 13 C-PT were not concerned by this additional correction since they already held fully controlled 12 C/ 13 C patterns at all positions. A corrective method dedicated to IsoCor was specifically created for the TMS-derivative fragments described in this study (Table  1 ).
The IsoCor files and a GC-MS example file are available in the following INRAE dataverse, and all information will be updated indefinitely  [39]. The method is publicly available and can also be run directly from GC-MS source files using a Galaxy Workflow URL available at the previously mentioned INRAE dataverse. For this purpose, each raw GC-MS dataset should contain a column "Name" for the fragments considered and a column "Area". The name of each fragment must be written exactly as specified in the GC-MS example file from the previously mentioned INRAE dataverse. Fractional CIDs and fractional mean 13 C enrichments were directly obtained from IsoCor outputs (isotopologue_fraction and mean_enrichment). For 13 C-PT samples, CIDs were rescaled to Pascal's Triangle coefficients by multiplying with 2 n , where n is the number of carbon atoms  [36].
2. 3. Calculations of 13 C-Positional Enrichments 13 C-positional enrichments were calculated by combining mean 13 C enrichments of several mass fragments that shared part of the carbon backbone from the same TMS derivative. A workflow has been created to calculate these 13 C-positional enrichments directly from the outputs of our 13 C-processing method with a URL and explanations available at the dedicated INRAE dataverse  [39]. Briefly, by considering a metabolite X with 3 carbons, the mean 13 C enrichment (M 13 C) of the mass fragment containing the C1-C2-C3 carbons (M 13 C (X) C1C2C3) can be written as a function of positional mean 13 C enrichments (using fractional values here): M 13 C (X) C1C2C3 = M 13 C (X) C1 + M 13 C (X) C2 + M 13 C (X) C3 3 (1) Considering another fragment containing the C2-C3 carbons, the mean 13 C enrichment (M 13 C(X) C2-C3 ) is calculated by: M 13 C (X) C2C3 = M 13 C (X) C2 + M 13 C (X) C3 2 (2) Then, the mean 13 C enrichment for the C1 carbon (M 13 C(X) C1 ) can be calculated by: M 13 C (X) C1 = (3 × M 13 C (X) C1C2C3) -(2 × M 13 C (X) C2C3) (3) Following the same rationale, we calculated some 13 C-positional enrichments based on the mass fragments detailed in Table  1.