dataesr/scientific-paragraphs-categorization huggingface dataset:
This observation highlights the need for careful screening for data quality when using power curves from manufacturers and databases. In the examples presented so far the shape of the power coefficient function matches with that from the wind turbine database. However, turbine 404 in Figure  14  exhibits a different shape which does not conform to the majority of other wind turbines. In this case, it is difficult to identify the reason for the observed difference (modelling error, correction of the effect of turbulence intensity, shaded wind measurements. ) and further validation work would be needed to get a deeper insight in the performance of the model and possible sources uncertainty on the power curves. The different examples presented above summarise most situations encountered in the validation work.
In order to give an overview on the match between the model output and the database of power curves, all power curves have been represented by blue lines in Figure  15. Since the error of the model principally occurs in region II, the power curves have been scaled by normalising them by the rotor area so that all power curves are similar in that area. Indeed, it can be observed that many of them overlap for wind speed values between 3 and 10 m/s. The model outputs obtained with the standard parameters but with power to rotor area ratio of 0. 25, 0. 375 and 0. 5 and a turbulence intensity of 5% have also been represented in this plot. This final analysis shows that even in cases with the largest errors, such as those presented in Figure  13  and Figure  14 , the model is able to reliably reproduce the behaviour of the majority of power curves seen across the wind industry.
Conclusion We propose here an approach to estimate the power curve of a wind turbine from its main characteristics. The present work has aggregated existing knowledge on wind turbine operation to address a current need for energy modeling applications. The model, with 12 parameters, offers the possibility to adapt the turbulence intensity and air density to the actual conditions of a specific site. A sensitivity analysis has been conducted and established that nominal power, the rotor area and the maximal C p value as the most influencing parameters. Choosing the nominal power or the rotor area is straightforward since these two parameters are often used to characterise a turbine and are even frequently contained in the turbine model name. Then, a statistical analysis of the remaining parameters was conducted to suggest default values, relying on an extensive database of turbine characteristics.
A qualitative validation has been conducted where the model output has been compared to power curves taken from a wind turbine dataset. This validation revealed that the model yields realistic power curves when compared to power curve from the database for most of the wind turbine model. However, large and suspicious differences are observed for a limited number of wind turbines that would deserve further analysis, since power it is unclear whether they result from modelling issues or data quality issues within the database. This highlights the need for caution when using power curves found online. Another conclusion of this validation is that the different power curves contained in the database obviously correspond to different level of turbulence intensity. This information is generally not given and leads to an uncertainty that is avoided by the use of our model.
g. noise emission limitation or smooth disconnection at cut-off. In addition, further validation work would be needed to get a better insight in the performance and weaknesses of the proposed method. This model offers a lot of flexibility and can therefore be used in simulation of the wind power production in energy mix analysis. It maybe now be interesting to evaluate the impact of the different sizing parameter on the annual energy yield of a wind turbine but also to evaluate the expected yield of future wind turbines. Finally, to assist with the use of the parameterised model, we have developed implementations of the model able to generate power curves in MATLAB, R and Python, which can be found as supplementary material of this paper (https://github. com/YvesMSaintDrenan/WT_PowerCurveModel).
Acknowledgements The author would like to acknowledge the thewindpower. netteam for the compilation and regularly update of their wind turbine and power curve database. This work has been partly conducted in the framework of the Copernicus C3S energy and  ERANET CLIM2POWER  projects. Copernicus Climate Change Service (C3S) is a programme being implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission  (contract number:  2018/C3S 4 26 L ot1 W EM C ). The project  CLIM2POWER  is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  BMBF (DE) ,  BMWFW (AT) ,  FCT (PT) ,  EPA  ( IE ),  ANR (FR)  with co-funding by the  European Union  (Grant  690462 ). Malte Jansen  and  Iain Staffell  were funded by the  Engineering and Physical Sciences Research Council  through the  IDLES programme  ( EP/R045518/1 ).
4. 2. 2 Cut-in and cut-off wind speedsThe distributions of the cut-in and cut-off wind speeds of the wind turbine information contained in thewindpower. net (2018) dataset are displayed in Figure10. It can be observed that the cut-in wind speed are between 1 and 5 m/s. Most values are distributed around 3 m/s with 90 % of the values between 2 and.
INTRODUCTION It is accepted that the eukaryotic mature messenger ribonucleic acids (mRNAs) are monocistronic, leading to the translation of a single protein product. According to the rules described by  Kozak (1) , the coding DNA sequence (CDS) region is the longest nucleotide sequence flanked on each side by a START and a STOP codon. The CDS defines the reference open reading frame (RefORF) that is translated into a protein. Nevertheless, the proteome has been shown to be more complex than initially expected. Indeed, >10% of proteomic data remain unmatched by interrogation of reference protein (RefProt) databases; however, the quality of the tandem mass spectrometry (MS/MS) data is sufficient (number of characteristic fragment ions) to lead to protein identification. These unmatched data were used to demonstrate that proteins encoded by alternative ORFs (AltORFs) can be translated from the mRNA, in addition to the predicted protein encoded by the RefORF  (2).
Since protein databases used in large-scale proteomic approaches [e. g. UniProt  (7)  or NCBI  (8) ] are built on genomic annotations, AltProts have been missing and hence could not be identified through MS-based proteomics. Considering these accumulating evidences, the genome annotation and protein translation dogma had to be reconsidered, and the development of new databases including the AltProts was then a mandatory step to their identification in large-scale proteomic studies. Over the past years, several initiatives have led to predict and annotate AltProt sequences. Recently, the OpenProt database has been publicly released and can be used to identify AltProts in largescale proteomic datasets  (9). In silico studies using these Alt-Prot databases show that the average AltProt size, and thus molecular weight, is lower than their reference counterpart with ∼57 versus ∼344 amino acid length for the AltProts and the RefProts, respectively  (2, 10).
Before the advent of these new databases, a few AltProts have been nonetheless reported; however, they have been considered as an epiphenomenon despite showing central biological functions  (11). The implementation of AltProt database interrogation in large-scale proteomic experiments has opened the way to their massive identification moving forward with the characterization of their physiological and physiopathological role [e. g. ovarian cancer  (12) , brain physiome  (13)  and viral infection  (14) ]. Not only AltProts are believed to have a central role in signalling pathways, but it was also demonstrated that some of them (e. g. AltMRVI1) were showing higher expression than the RefProts though with shorter half-lives  (10). As of today, numerous AltProts have been identified but very few studies have been focusing on their functions, which hence remain largely unknown.
At last, data analysis is far more complex in XL-MS experiments than in conventional shotgun proteomics and requires dedicated software and platforms  (35) (36) (37) (38) (39) (40). So far, finding PPIs from all identified proteins versus the entire protein database is still not possible due to limited computational power in conventional labs. However, searching for candidates against the database is feasible. Another important aspect to be careful about the data analysis is the possible misassignments  (41). Some of the most advanced tools for XL-MS data analysis such as XlinkX computing node  (42) , which can be included within the usual proteomic identification pipeline of Proteome Discoverer 2. 2 (PD2. 2), are designed to avoid misassignments and include a score of false discovery rate (FDR) for the cross-link identification.
Shotgun proteomic data analysis Raw data obtained from the nLC-MS/MS run were treated using MaxQuant v1. 6. 1. 0 using the LFQ (label-free quantification) annotation of the protein identified. UniProtKB database for reviewed human proteins of April 2018 containing 20 303 protein sequences was used. Statistical analyses were carried out using Perseus software after filtering for 'reverse' and 'contaminant' proteins. For the comparison between control and Forskolin-treated groups, t-test was performed with a permutation-based FDR of 0. 05, and Pvalues <0. 05 were considered to be statistically significant. A heat map of differentially expressed proteins across the two different groups was generated. Gene Ontology (GO) analysis was performed using ClueGO (54) on Cytoscape v3. 7. 1  (55). For AltProt identification, a database (named HaltORF) corresponding to the annotation of long noncoding RNAs (ncRNAs), ncRNAs and mRNA uncoding regions was used.
The protein sequences are predicted using the Homo sapiens genome database (release hg38, assembly: GCF 000001405. 26). To ensure that unbiased Alt-Prot identification is obtained, the human AltProt database HaltORF (reference name 'HS GRCh38 altorf 20170421', which contains 182 709 entries) was combined with the UniProtKB  (7)  database that encompasses the RefProts (April 2018 release, 20 303 protein sequences) in a single database for a total of 203 012 entries. Since MaxQuant was described as not suitable for such a large database, the identification was performed using PD2. 2. Additional online databases such as 'Ensembl' (https://www. ensembl. org) and 'RefSeq' (https://www. ncbi. nlm. nih. gov/refseq) were also used to trace back the origin of the identified AltProts after HaltORF data interrogation.
Cross-link data analysis Data were analysed using PD2. 2 implemented with the XlinkX node  (48). Interrogation of data was performed according to the following workflow: first spectra were selected and DSSO was defined as cross-linker (characteristic mass 158. 003765 Da). Then, the workflow was divided into two paths: (i) The first was dedicated to the cross-link identifications using the XlinkX with the following search parameters: precursor mass tolerance: 10 ppm, FTMS fragment: 20 ppm, ITMS fragment: 0. 5 Da, and searching a compiled database comprising both HaltORF and UniProtKB. The validation was performed using percolator with an FDR set to 0. 01. (ii) The second path was dedicated to the shotgun protein identification using SequestHT and considering the following parameters: trypsin as an enzyme, two missed cleavages, methionine oxidation as variable modification, DSSO hydrolysed and carbamidomethylation of cysteines as static modification, precursor mass tolerance: 10 ppm and fragment mass tolerance: 0.
6 Da. The validation was performed using Percolator with an FDR set to 0. 01. A consensus workflow was then applied for the statistical arrangement. A de-isotope and a TopX filter were used to determine the m/z error with a selectivity of ∼10% FDR. The PPIs identified (Supplementary Table  S1 ) were manually checked (Supplementary Figure  S1 , Supplementary Table  S2 ) and then displayed in Cytoscape 3. 7. 1  (55). The accession number of each protein showing an interaction was extracted with its XlinkScore and transformed into a text file to get the visualization of the partners. The network was obtained according to the XlinkScore by plotting the distance between the nodes and the recurrence numbers of a given interaction.
Modelling and prediction of interactions Structure modelling of AltProts and RefProts was performed with the I-TASSER software  (56). For both Ref-Prots and AltProts, the most stable models (C-score between -5 and +2) were retained. The prediction of PPIs was performed with the ClusPro software  (57). The RefProt was identified as a receiver and the AltProt as a ligand. The interaction model was carried out by docking the ligand onto the receiver without cross-linker size restriction. ClusPro then generates multiple interaction models ranked in the order of stability. The selected models are still part of the top 5 'balanced' models taking into account the best compromise of stability. The selected interactions were then illustrated with Chimera  (58)  to measure the distance between the atoms observed during XL-MS analysis.
Each treatment dataset point was compared to untreated cells to identify specific changes in the protein regulation. Notably, specific clusters of upand downregulated proteins were identified after analysis (Figure  3A ), suggesting that significant changes occur at the nuclear level after treatment. Enrichment within each cluster of the time-course experiment reveals that specific molecular events undergo temporal changes while others are found to be time independent (Figure  3B ). In detail, the GO term analysis of the identified proteins shows significantly altered signalling pathways after 16 h of Forskolin treatment (Figure  3B ). In line with the role of PKA in transcriptional and translational regulation, proteins related to translation initiation and regulation, ribosome assembly and nucleic acid metabolism are identified.
Time-course identification of PPIs To validate our comparative enrichment analysis, we searched for PPI modulation following Forskolin treatment by large-scale XL-MS strategy. Nuclear fractions were submitted to XL-MS using the membrane-permeable DSSO cross-linker, and the samples were processed using the FASP method (Figure  4 ). In this time-course analysis, a total of 20 cross-link spectra matches were detected after XL-MS, representing 16 interactions between proteins. The interaction networks known from the literature for these enriched proteins and plotted using STRING  (63)  interrogation are presented in Figure  5. Cytoscape and ClueGo were applied to correlate the resulting identifications to known processes using Reactome and GO term databases. Most proteins identified at 16 h belong to the ATP synthesis pathway, including the electron transport signalling.
TPM network reveals hidden AltProt interactions In network 6 (Figure  6 ), TPMs are the major protein family for which PPIs were detected by XL-MS. Interestingly, TPM1, TPM2, TPM3 and TPM4 are in direct interaction regardless of the Forskolin treatment. However, some of the PPI partners are specific to one of the two conditions. In particular, in the control condition, TPM4 is interacting with AltProts such as AltTRAU1AP, AltMAP2 and AltEPHA5. The other members TPM3 and PLEC also interact with AltProts (Table  2 ). Two of these identified AltProts were previously included in transcript databases but were removed (XR 428143. 1 and XM 006723305. 1). Eight other identified AltProts were issued from an overlapping between CDS and 3 UTR (CDS-3 UTR) (2 of them), 3 UTR (1), 5 UTR (CDS-5 UTR) (1), +2 on the CDS (3 of them) and an ncRNA (1) (Figure  8A ).
For the three AltProts interacting with TPM4, AltTRNAU1AP is translated from the 3 UTR part of the transcript 201 of the TR-NAU1AP gene. AltEPHA5 is coming from an overlap between the CDS domain and the 3 UTR of the transcript EPHA5-204 also coding for an EPHA5 RefProt. Finally, the sequence coding for AltMAP2 can be issued from the transcripts 201, 202 and 205 of MAP2 as overlapping the CDS and the 3 UTR (Figure  8B ). To model the interaction observed by XL-MS between TPM4 and the three AltProts, a 3D model was created. First, the protein structures were predicted in I-TASSER and only the best conformations for each of them, according to the C-score, were kept (Supplementary Figure  3 ). Then, ClusPro was used to predict the possible interaction between these partners. To avoid any bias in the prediction, the cross-linker length was not considered.
Figure5. Network of proteins and their associated GO terms generated from the XL-MS data and enriched from STRING and known pathways for the NCH82 cell nucleus fraction upon time-course treatment by Forskolin for 16 h (purple), 24 h (red) and 48 h (green). The proteins are depicted in the colour according to the time point at which they were observed. Data were interrogated using a combined database between RefProts and AltProts. Circles correspond to the identified RefProts and squares to the AltProts. The networks were enriched through addition of STRING network to the identified RefProts using ClueGO application on Cytoscape.
ACKNOWLEDGEMENTS Author contribution: Conceptualization, I. F. , J. F. and M. S. ; methodology, I. F. , J. F. , E. C,  E. M. N. L, M. D. , T. C. and M. S. ; software, T. C. ; validation, I. F. , J. F. , T. C. and M. S. ; formal analysis, T. C. ; investigation, I. F. , J. F. , T. C. and M. S. ; resources, I. F. , M. M. and M. S. ; data curation, T. C. ; writing--original draft, T. C. , I. F. and M. S; writing--review and editing, E. C. , E. M. N. L, D. V. , I. F. and M. S. ; supervision, I. F. , J. F. and M. S. ; project administration, I. F. and M. S. ; funding acquisition, I. F. and M. S.
Following the electronic design automation (EDA) concept, which was an essential contribution that spurred the digital society we live in, there are many design automation tools for circuit and pathway, these are extensively reviewed in Appleton et al. 1  and Lin et al. 2  respectively. As an example, Cello 3 applies the EDA approach to genetic circuits. With Cello, a specific desired logic function is encoded into the Verilog language (a standard hardware description language used to model and design electronic circuits) which in turn is transformed into a linear DNA sequence that can be constructed and eventually run in living cells. The user enters the desired logic function and a "user constraints file" which contains the details on a logic gate library, the layout of the genetic system, the organism and strain, and the operating conditions for which the circuit design is valid.
Additionally, Cello encompasses a combinatorial algorithm allowing to design multiple constructs containing the same circuit while varying unconstrained design elements to build a library that can be screened. Cello eventually includes a simulator generating predicted cytometry distribution for all combinations of input states, which can be directly compared to flow cytometry experiments. Cello was applied to the design of 60 circuits for Escherichia coli, 45 (75%) of which performed correctly in every output state. Cello comprises several steps, which are connected and therefore need to use standardized input/output formats. Among those formats are Verilog to represent a logic function, JSON to describe the user constraints, and Eugene  4  to encode a set of parts and constraints between the parts.
SBOL can now encode complex genetic circuits, metabolic pathways, vectors, and plasmids. One of the biggest challenges and a barrier to the reuse of successful designs, is that biological data relevant to the design of novel systems are often not exchanged. Addressing this challenge, the SynBioHub repository  7  is an open-source software project that facilitates the sharing of information about engineered biological systems using the SBOL format. SynBioHub provides computational access for software and data integration, and a web-based graphical user interface that enables users to search for and share designs. As pointed in Appleton et al. 1  , most of the tools mentioned above feature dedicated support for designing genetic regulatory networks/circuits, but they do not feature the same level of support for designing biosynthetic/metabolic pathways.
One of the purposes of the Galaxy-SynBioCAD portal is addressing this shortcoming by providing a suite of interoperable and standardized tools to design pathways from the design specification (choice of the compound, strain) to the DNA parts to be assembled. As for metabolic circuit design, there are plenty of pathway design software tools 2. Briefly, from a given target compound and a given chassis strain, the first step consists of finding metabolic reactions that are heterologous to the chassis and link the target compound to the native metabolites of the host organism. This step is carried out by retrosynthesis software  [8] [9] [10] [11] [12] [13]  and requires the use of reaction rules  14  if one wishes to search for novel pathways or find pathways that produce unnatural target compounds.
The result of retrosynthesis software tools is a metabolic map and there is a need in a second step to enumerate the pathways linking the chassis metabolites to the target. There are many tools for pathway enumeration and search  15  , which are sometimes integrated into the retrosynthesis software itself. The third step is to find the most promising enzyme sequences catalyzing the metabolic reactions of the enumerated pathways. This can be achieved either through similarity search to enzyme annotated metabolic reactions  16- 18  , or machine learning trained on metabolic databases  19, 20. Once the pathways have been annotated with enzyme sequences, they can be ranked in a fourth step. The ranking criteria are diverse, they can be among others based on thermodynamics  21  , predicted yield of the target  22  , target rate of production through flux balance analysis  9, 11, 21  , chassis cytotoxicity of the target and intermediates  21  , along with simpler criteria like pathway length.
Tools implementation into Galaxy nodes To be implemented into Galaxy, software applications were selected among the computational tools mentioned in the Introduction section. Several criteria were used for this selection, (i) the tools needed to be relevant for pathway design, (ii) be published, (iii) open-source under MIT, GNU GPL, or related licenses, (iv) well documented and deposited in GitHub, (v) making use of standard input/output, and (vi) amenable to compartmentalization in Docker and implementation into a Galaxy node. The process used to integrate computational tools into Galaxy nodes is described in the Methods section (see IT Architecture subsection where an example is provided for the tool RetroPath2. 0). The list of Galaxy nodes provided below are currently installed on the Galaxy-SynBioCAD portal and enable one to design pathways from target and strain selection to DNA part calculation.