dataesr/scientific-paragraphs-categorization huggingface dataset:
This particular cohort of patients lends itself to this study because it represents a homogeneous group of cN0 patients who underwent a standardized surgical procedure: TT and systematic pCND, thus eliminating the variability in surgical techniques and approaches. Since all of our patients had a central neck dissection regardless of preoperative staging, this dataset is unique and can answer questions for the vast majority of surgeons who do not perform pCND. Patients with therapeutic LND, unilateral thyroid resection with pCND, and distant metastatic stage cancer M1 were excluded.
EUROCRINE® database EUROCRINE® is an online prospective database that has registered endocrine surgical procedures in centers across Europe since 2015. EUROCRINE® was started as a project within the Health Programme of the European Union in 2013 to reduce morbidity and mortality in patients undergoing surgical treatment for endocrine tumors. EUROCRINE® is managed by the EUROCRINE® Society, based in Vienna, and with a council of 13 participating national endocrine surgical societies and the European Society of Endocrine Surgeons. Personal registration is needed to enter the data in EUROCRINE®, which was supplied to the principal investigator at each participating center. Data collection was supervised by the principal surgeon at each site. The EUROCRINE ® database is currently used by 310 departments and clinics in Europe, including 12 in France (https://www.
These data were not available in the Eurocrine database for our study. The authors recognize that prophylactic pCND is not a widely accepted procedure, nor is it recommended by the ATA guidelines. However, this dataset is unique and can provide data that other studies cannot. We believe that the large size of this multicentric cohort, with standardized treatment and unchanged follow-up over time, overcomes the retrospective nature of the study and provides useful information for the management of cN0 PTC patients.
Acknowledgments AFCE-Eurocrine Prophylactic Group :  Marlène Parisot et Claire Angeline Goutard ,  Gregory Baud ,  Camille Marciniak ,  Mikael Chetboun ,  Vincent Vangelder , orence  Bihain ,  Claire Nomine ,  Cécile Caillard ,  Lola Cossé ,  Perrine Engel ,  Muriel Mathonnet ,  Christophe Tresallet ,  Pierre Goudet  Ethical Approval Not applicable Con ict of Interest and Source funding: None.
However, the new methodology of Taelman et al. [54]  moves one step further by allowing the quantitative assessment and integration of LCA and ESA methods through monetary valuation techniques to cover local and global effects of human activities, targeting both terrestrial and marine ESs. So far, there are no studies that comprehensively evaluate both positive and negative impacts of OWFs on terrestrial and aquatic ecosystems, particularly on marine ecosystems and its services, and at different geographical scales. Therefore, the main goal of this study is to measure these impacts by demonstrating and applying the (socio-) environmental impact assessment framework of Taelman et al. [54]  to a real OWF case study in the BCS. To this end, data was collected in an exhaustive manner, from technological data for the life cycle inventory (LCI) to biophysical data for the ESA and monetary values to aggregate the results of both methods.
However, when local ESs data is not available (or cannot be generated) for each of the processes in the value chain, it is still possible to account for impacts on ESs in a site-generic way. To do so, Taelman et al. [54]  developed new characterization factors and midpoint impact categories, adapting this way a classical life cycle impact assessment method (ReCiPe (H) v1. 05) to also account for the impact on provisioning, regulating and cultural ESs due to land use (i. e. transformation and occupation of land along the value chain), next to the more traditional impact categories such as global warming and eutrophication. These site-generic CFs are calculated based on the Ecosystem Services Valuation Database (ESVD) and expressed in monetary terms (more details in Section 1. 1 of Supplementary Material B and  [54].
The impacts assessed through an adapted LCA are expressed per FU (1 GWh of electricity delivered to the grid) and then monetized to have a common unit of € per GWh. The foreground system is visualized in Fig. 3. For most processes considered, primary data was collected including publicly-available data and confidential data from the OWF's concession holders  [58]. Secondary sources, such as peer-reviewed articles  [39, 41] , master theses  [59, 60]  and processes from Ecoinvent v3. 8 database were used and adjusted for this case study whenever primary data was lacking. A description of the foreground processes in this study can be found below and Table  1  shows an aggregated LCI for all these processes. More details on the description of the system and assumptions for each life cycle stage (Section 2.
e. data from the value chain obtained from the database Ecoinvent). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) assuming that all of them have the same dimensions, as well as the I-and J-tubes (see Fig. 1 ). Finally, to protect the monopiles installed in the seabed against erosion, a scour protection system is required. The offshore transmission system is comprised by an OHVS, infield cables and a subsea export cable. The OHVS collects the electricity produced by the wind turbines via 33 kV infield cables, and converts it for transport to shore through a 170 kV subsea export cable. The onshore transmission system consists mainly of a 150 kV land cable connected to the subsea export cable in a connection pit at the shore, which transports the electricity to an onshore high voltage station  [41, 58, 59].
Operation and maintenance The operation and maintenance (O&M) phase includes all activities (offshore & onshore) carried out to ensure the continuity of an OWF's efficient operation during its lifetime. For each component of the OWF, O&M activities were determined by personal communication with the concession holders and making some assumptions based on literature data to fill data gaps  [41, 59]. During the estimated time of operation of the OWF, there is on average a yearly electricity production of 532. 761 GWh. This value was used to estimate the total electricity produced during the lifetime of the OWF (i. e. 20 years), which is approximately 10,655 GWh  [58]. Losses were also included, amounting to about 2. 5% [58], with a net electricity production for a 20-year period of 10,389 GWh, or 519.
e. wind turbines, remaining part of monopiles, transition pieces, infield cables and submarine export cable) are taken to shore. For the dismantling stage, all the components are further dismantled into specific materials that are transported to their respective end-of-life treatments, i. e. recycling, incineration and landfill. To determine the treatment of each material from a particular component, the work of Vanderveken  [60]  was used as baseline. Avoided products, both materials and energy, were also considered in the model. The avoided materials correspond to recycled metals and plastics and the avoided energy to recovered heat and electricity from incineration. Processes from Ecoinvent database were used and adjusted to model the quantities of these avoided products.
To align with the system boundaries of the LCA +ES , it is important to value this ES correctly, i. e. we need to determine how much society is willing to pay for grid-supplied electricity, which is a basic need. Therefore, distribution costs, taxes and transmissions costs of offshore wind energy should be deducted from the Flemish market price. This data (i. e. the energy cost) was obtained from the Vlaamse Regulator van de Elektriciteits-en Gasmarkt (VREG) for a Flemish household with an average electricity consumption of 1600 kWh during the day and 1900 kWh at night time between 2019 and 2022 (Table  S7  in Supplementary Material B). This dataset was used to calculate an annual average market price of electricity delivered at the grid. The year 2022 was not included in the calculations because of exceptionally high energy prices due to the on-going war in Ukraine.
The next step is to determine the potential recruitment enhancement of fish biomass (i. e. transitioning of these six-month old fish to become an adult population and thus potential catch) given the initial effect of OWFs on the juvenile individuals  [68]. One of the first steps is to identify relevant fish species whose nursery grounds overlap with or are close to the OWF. These valuable nursery locations in the North Sea and BCS were identified using the graphs of Judd et al. [70]  and Maes et al. [71]. Other selection criteria included are the economic importance of the fish and how accessible and/or available the data is for certain species. Finally, the fish species selected, based on the criteria mentioned beforehand, were Common sole (Solea solea), European plaice (Pleuronectes platessa), Atlantic cod (Gadus morhua), Whiting (Merlangius merlangus), European sprat (Sprattus sprattus) and Lemon sole (Microstomus kitt).
Despite whiting not having a nursery ground but a spawning ground in the norther edge of the BCS, it was included in the analysis due to the potential edge effects of OWFs  [72]. A next step is to estimate the changes in juvenile fish density (ind/ km 2 /year) (i. e. for individuals less than one year old) in the area around the OWF both before and after its presence. A dataset of bottom trawl samples from Vandendriessche et al. [72]  was used for the years 2005 to 2013 and sampled in the Bligh Bank and Thornton Bank to study the effects of OWFs. For this analysis, samples taken in the surroundings of the Bligh Bank area were considered. From this data, the juveniles were filtered based on their length. The mean length of one-year-old individuals for each fish species was found in published graphs from the International Council for the Exploration of the Sea (ICES) fact sheets  [73].
To obtain the total annual production and express the enhancement in kg m -2 year -1 , the von Bertalanffy growth equation is used to estimate the average length of a fish for each age class up to the maximum age that a fish can reach. After obtaining the average length, this value is used to calculate the average weight of a fish for each age class using a length-weight relationship equation. All life history parameters required for these equations were obtained from FishBase  [74]  and the ICES fact sheets for each species  [73]. We chose life history parameters of studies conducted in the Southern North Sea, or if these were not available, we considered data from the North Sea in general. Finally, the total annual enhancement of a species (kg km -2 year -1 ) was calculated by summing the incremental increase in weight and then multiplying by the density (N i ) for each age class  [68].
Benchmark: LCA +ES Secondary data was used from a process in Ecoinvent v3. 8 (cut-off allocationunit) (i. e. electricity production, nuclear, pressure water reactor BE) as a proxy for the Doel plant. This process represents the production of electricity by a 1000 MW nuclear power reactor and includes the materials and auxiliaries, energy requirements, emissions and wastes generated (e. g. this process includes the transport, treatment and storage of spent nuclear fuel) during the construction of a power plant up to the production of 1 GWh of electricity delivered to the grid. We have assumed that the impacts per GWh of a 1000 MW plant are similar to those of a 2935 MW plant, as Doel has two 1000 MW reactors, but also two 450 MW reactors, which can be considered as one. Since data on the EoL of the nuclear power plant is limited, we have studied the value chain of nuclear energy from cradle-to-gate.
5. 2  [66]. The ECOPLAN tool has a geodataset that contains all the data needed for the quantification, e. g. land covers, land use, ESs specific maps, population densities  [86]. To quantify the changes on the ESs, we first had to determine land cover before and after the installation of the nuclear plant. Before the installation, i. e. 1975, the area was agricultural (i. e. polders) and in the current situation, using the year 2015 as reference, the area consists of paved surfaces, buildings and low vegetation (mostly grass). More details on the application of the tool can be found in Section 4. 1. 3 in Supplementary Material B. A similar approach as the OWF case was taken by selecting one ES per category (i. e. provisioning, regulating and cultural) for calculating the handprint and footprint in the benchmark assessment.
Data availability Despite the fact that the exhaustive LCI (cfr Table  1 ) is based on primary data sources, there were still data gaps and these were filled by adapting data from peer-reviewed articles or databases (i. e. Ecoinvent), which may lead to a loss of accuracy in the results and over-or underestimation of the impacts. For example, both wind turbines and OHVS have electrical equipment whose composition was not specified in the primary sources used, so we used the study of  [87]  as a proxy to determine the composition of electrical equipment containing gold and palladium. Based on the results, both metals appear to contribute significantly to the footprint of wind turbines despite their low quantities. In future research, it would be better to have a clearer picture of these exact quantities to obtain more robust results.
Modelling of impacts with LCA +ES Conducting the LCA required several model choices that may affect the accuracy of the results. For example, to model the avoided materials, a 1:1 substitution ratio was assumed for recycling In reality, however, this ratio is very likely to vary and ideally the substitution ratio should be calculated for secondary materials  [88]. Future research on the EoL of OWFs should take this into account to obtain more robust results on the benefits of avoided materials. Moreover, the processes used from the Ecoinvent database to model the manufacturing of materials for the OWF may be based on newer technology than was available at the time the OWF was actually manufactured, leading to an underestimation of the impacts of the OWF. Similarly, to model marine transport at different stages of life cycle, a sea ferry was used as a substitute for different type of marine vessels, which may lead to an underestimation or overestimation of transport impacts.
Data availability For the renewable offshore energy ES, the Flemish market price of electricity (without taxes, distribution and transmission costs) was available but only for the years 2019-2022. Similarly, for the nursery and habitat maintenance ES, the dataset used to quantify the fish juveniles before and after the OWF installation had a short temporal coverage (i. e. 2005-2013) and the sampling was not continuous. To increase the accuracy of the results for both ESs, a continuous dataset covering a longer period is preferable. As mentioned in Section 2. 5. 4, visual impacts on aesthetic value are usually quantified in the literature at the time OWFs are planned (i. e. they investigate individuals' preference on the attributes of a potential OWF), without studying the impacts of already installed OWFs (i.
By allowing the integration of these site-specific and site-generic impacts on ESs with other impacts quantified in LCA, this study is a first step towards understanding the beneficial and adverse impacts of offshore wind energy. Despite these positive aspects, the framework faces some challenges, which are outlined in Section 4. 3. 2. to this OWF case study was data accessibility and availability. This is mainly due to the high data requirements and also data gaps. As discussed by De Luca et al. [14]  and Taelman et al. [54] , this is one of the main challenges in attempts to integrate LCA and ESA.
Site-specific and site-generic impacts on ESs. There can also be impacts of seabed transformation and occupation on marine ESs, but these were not considered in the LCA +ES -ESA framework. Taelman et al. [54]  identified the lack of CFs for marine biomes as limitation of the ESVD database. This is particularly important because this study is set in marine context, so it would be relevant to understand the effects on ESs supply due to marine use. Moreover, there may be studies in other contexts where accounting few local ESAs for certain parts of the value chain may take a lot of time and effort, so it would be better to use the site-generic ESs calculations for the whole value chain. On the other hand, in studies, e. g. outside the European context, it would be better to rely on locals ESAs than sitegeneric impacts, since the CFs developed are based on the ESVD database, which is mainly built from European studies.
Data availability As mentioned earlier in Section 2. 7 no primary data was obtained for the nuclear power plant, but Ecoinvent data was used for modelling. As the Ecoinvent dataset does not contain information on the EoL of the nuclear power plant, the system boundaries of the assessment were changed to cradle-to-gate to have a fair comparison between the OWF and nuclear power plant. In September 2022, one of the four reactors in the Doel plant was permanently shut down. According to ENGIE  [84] , the dismantling phase, including the removal of infrastructure and radioactive materials, will begin in 2026, with an expected duration of about ten years. The footprint of dismantling a nuclear power plant is still very unclear; there are technological uncertainties and also the time-horizon of a nuclear plant's EoL is not precisely known.
Modelling with ecoinvent As explained in Section 2. 7, a process already established from the Ecoinvent database was chosen to quantify the impacts of a nuclear power plant. The results obtained are the aggregated results from cradleto-gate of the nuclear power plant, but it would have been ideal to have disaggregated results to understand the contribution of the different life cycle stages (i. e. manufacturing, transport, installation, O&M) to the burdens and to have a fairer comparison with the OWF, for which results are presented per life cycle stage. According to other LCAs for nuclear power plants, uranium mining and milling processes are the main contributors to all the impact categories assessed in those studies  [105] [106] [107]. Understanding whether these processes make a similar contribution to this nuclear plant would have been preferable.
e. water extracted for cooling). In this study, spent nuclear fuel (i. e. including treatment, storage, transport) is the largest contributor in the ionizing radiation impact category due to emissions of carbon-14. This goes in line with the results of EDF [105], but the results of Zhang and Bauer  [107]  show that uranium mining and milling had the largest contribution to this impact category for one of the plants assessed. In addition to this, the Ecoinvent process is for a nuclear power plant with a 1000 MW capacity, while the Doel plant has a 2935 MW capacity. While this is a good proxy because the reactors at Doel have a capacity of 1000 MW, it would be ideal to check whether there is an overestimation of impacts due to energy production efficiency and technological development. In future research, collecting primary data on the life cycle of nuclear power plants is crucial to improve the benchmark assessment.
Moreover, the process selected from Ecoinvent does not model the risk of a nuclear accident. Though the failure rate of reactor is low  [109] , if it ever happens, the consequences of an accident will be extremely severe on human health, and on ecosystems and its services  [110]. In addition, the impacts of nuclear waste need to be properly addressed in future research. For example, the use of "space", i. e. the volume occupied in a geological repository for nuclear waste is not quantified due to a lack of CFs. Also, the risks of nuclear waste still have uncertainties as a result of epidemiological studies with limited quality or a lack of data  [111]. This is something to consider when comparing the environmental performance of an OWF and a nuclear power plant, as the effects of nuclear power plants may be underestimated.
Ideally, the ESs chosen should have a similar functionality (e. g. nutrient cycling, erosion prevention, food provisioning), which was the case for the electricity production ES. However, due to lack of data and for technical reasons, this is not always possible and the ESs chosen were not the same. For example, the range of ESs that can be assessed with the ECOPLAN toolbox is large (i. e. 18), but not all ESs can be directly monetized, so other ways to do this need to be explored. For the cultural ES, the impact on aesthetic value was not quantifiable using this tool (see Section 4. 4. 4), so instead, the impact on the recreation ES was quantified in a site-generic way. Moreover, even if the marine and terrestrial ESs have a similar functionality (e. g. soil carbon sequestration and blue carbon sequestration), the biophysical structures (e.
Moreover, secondary data for the nuclear power plant was used, which may deviate from the actual (socio-) environmental impacts of the Doel plant in particular. Future research should improve this to achieve a more complete (socio-) environmental impact assessment. Despite the challenges when applying the LCA +ES -ESA framework, such as data accessibility and availability, model choices, the risk of double-counting, the feasibility of local ESAs and the drawbacks of monetization techniques, this study made a valuable contribution in the application and demonstration of the LCA +ES -ESA framework to a marine human activity. This framework has a very positive handprint from the OWF to be unveiled, which would not have been revealed by conducting a classical LCA that does not consider the handprint of the FU.
Acknowledgements This work was financially supported by the  Agentschap Innoveren & Ondernemen (Flanders Innovation and Entrepreneurship Agency)  and powered by The  Blue Clusterthe spearhead cluster for blue growth in Flanderswithin the framework of the Sustainable Marine Ecosystem Services (SUMES)  project [ VLAIO  grant number:  HBC. 2019. 2903 ]. We would like to thank  Parkwind , the  SUMES consortium (GhEnToxLab from Ghent University, VLIZ  and  University of Antwerp ),  Gwenny Thomassen  from the STEN research group,  Joris Vanbiervliet  and  Viktor Kouloumpis  for their valuable contribution and feedback in the development of this work. We also thank  Ines Vanderveken  and  Millan Willems  for their contribution related to the inventory/modelling of OWF decommissioning and ecosystem service nursery and habitant maintenance, respectively.
Finally, using SpliceAid, a database of human RNA target motifs  (38) , we identified a strong RNA binding motif for the splicing activator SRSF3 right at the exonic region targeted by the two gRNAs impacting splicing (Figure  4A, C ). SRSF3 was previously reported to impact PKM. Ex9 splicing  (39) (40) (41). As expected, and comparable to gRNA_1 and gRNA_CD-mediated splicing effect, SRSF3 knock-down in HEK293T cells confirmed SRSF3 splicing effect on PKM. Ex9 despite targeting PKM. Ex10 (Figure  4F  and Supplementary Figure  4X-Y ). Furthermore, this splicing effect was not only observed in HEK293T cells, but also in the colorectal cancer cell line HCT116 (Figure  4G ), where SRSF3 has also been described to regulate PKM splicing in the same manner  (41). These results validate the hypothesis that the position-dependent effect of dCasRx in alternative splicing can help elucidate key regulatory mechanisms that should be further addressed with appropriate analysis.
FUNDING: This work was supported by the French National Research Agency program Labex EpiGenMed (ANR-Labex) and the Institut National du Cancer (INCa PLBIO grant 2018-146). The "Institut de Génétique Humaine" is supported by the Centre National de la Recherche Scientifique (CNRS) and the University of Montpellier (UM). FIGURE LEGENDS.