dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement number  758826 ). Z. Z. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Actions grant  (No. 842271  - TRITON  project). J. S. would like to acknowledge support from the  DGIST Start-up Fund Program of the Ministry of Science and ICT ( 2022010005 ). D. T. W. T. , M. P. W. , A. J. R. and R. A. L. J. acknowledge support from the  Engineering and Physical Sciences Research Council (UK)  via grant  EP/P027814/1. We thank  Dr. David Paleček  and  Dr. Christoph Schnedermann  ( University of Cambridge ) for the assistance with the TA spectroscopy measurements and for the useful discussion on transport dynamics.
Data collection The field measurements campaign was conducted during two periods, from 12 to 25 November 2021 and from 07 December 2021 to 10 January 2022. Both measurement periods were combined to form a 47-day dataset. For the purpose of this paper, the focus is on 97-m altitude, which matches the position of the sonic anemometer. TI derived from the sonic anemometer are the reference for the comparison with TI derived from the measurements of the commercial and prototype WindCube lidars. The sonic anemometer was set to record continuously the horizontal wind speed and wind direction at a sampling rate of 4 Hz. Both horizontal wind speed and direction were used to derive the two horizontal wind velocity components thus allowing for the calculation of the reference TI. The comparison of TI derived from the commercial and prototype configuration imposed to resample the sonic-derived wind dataset to a rate that matches the sampling rate of the LOS velocities measured by both configurations.
This allows to ensure that similar turbulence time scales are captured when calculating and comparing the The 47-day dataset was divided into 6762 subsets of 10-min data records giving subsets containing 150 and 600 measurement points for wind data acquisition at a sampling rate of 0. 25 Hz and 1 Hz respectively. The 10-min duration is long enough to retain the longest time scales of coherent turbulent structures in the turbulent fluctuations. This duration is also appropriate to support the hypothesis that the turbulent processes cannot be regarded as quasi-stationary (i. e. , stable mean and variance), which is essential for calculating turbulence metrics and turbulence velocity spectra. Note that, instead of 10-min, previous works divided their dataset into 30-min of data records  (Sathe et al.
de la Recherche under the Investissements d'Avenir program, with the reference ANR-10-IEED-0006-34. This work was carried out in the framework of the POWSEIDOM projects. 20 https://doi. org/10. 5194/wes-2022-53 Preprint. Discussion started: 5 July 2022 c Author(s) 2022. CC BY 4. 0 License.
| Molecular analysis Genomic DNA was extracted using the QIAxtractor and QIAcube robots (Qiagen) following the manufacturer's instructions. Samples were screened at 30 microsatellite markers from Peyran et al. (2020)  and  González-Wangüemert et al. (2014)  following the same genotyping procedure as described in  Peyran, Boissin, et al. (2021). Allele sizes were scored and checked manually using GeneMapper v. 5 software (Applied Biosystems). Samples presenting ambiguous peak profiles were re-amplified, genotyped, and re-scored, and all peaks that remained unclear were considered as missing data.
| Data analysis As mollusks are known to often present amplification troubles leading to high frequencies of null alleles  (Hare et al. , 1996) , the quality of the microsatellite markers was checked using MICROCHECKER v2. 2. 3. (Van Oosterhout et al. , 2004 ). On the original set of 30 markers, 8 presented high frequencies of null alleles and were removed  from the database to avoid any bias in further analyses. The final set thus contained 22 polymorphic loci. F I G U R E Since the sampling areas overlapped between three fieldwork campaigns, some individuals were sampled multiple times, leading to duplicate genotypes in the database. GenAlex 6. 503 (Peakall & Smouse, 2012) was used to identify identical genotypes that corresponded to the same individuals. Both genotypes from the same individual were kept for analyses when comparing genetic composition between years while a unique genotype was kept for parentage analysis.
Genetic diversity was evaluated through allele number (N a ), expected (H e ), unbiased expected (Hnb), and observed (H o ) heterozygosities using GenAlex 6. 503  (Peakall & Smouse, 2012). As sample size is not homogeneous among years, standardized allelic richness (Ar) and standardized private allelic richness (Apr) were estimated using ADZE software  (Szpiech et al. , 2008) , based on a standardized sample size of 150, large enough to be representative of the total sampling. Genetix  (Belkhir et al. , 2004)  was used to compute inbreeding coefficients (F IS ) for each year, using the method of  Weir and Cockerham (1984)  and the significance of values was estimated by permutations (1 000 permutations per population). Genetix was also used to calculate differentiation index (F ST ) between years using the Robertson and Hill unbiased estimator for F ST  (Robertson & Hill, 1984)  corrected by  Raufaste and Bonhomme (2000) , RH′.
Individuals from different clusters are unrelated. Colony provides a likelihood for the entire cluster configuration as well as for each dyad. In the present study, all individuals were considered as potential offspring, as they could all potentially have a parent present in the bay of Peyrefite, providing thus a dataset of 596 individuals. The potential parent pool included all individuals except for the juveniles sampled in 2018, providing a dataset of 578 potential parents. P. nobilis is a successive hermaphrodite for which a succession of alternate spawning and fast gametogenesis occurs during the spawning period  (De Gaulejac et al. , 1995). All parental genotypes were thus considered as candidate fathers and mothers within the analysis since the sex of individuals could not be determined.
Colony analysis was performed with the following parameters for the three long-length runs: both sexes are polygamous, the organism is dioecious, and, as it is an external spawner, inbreeding was considered as possible but clonal reproduction was not. The full-likelihood method was used with a high likelihood precision and no sib-ships prior. The genotype error rate was set to 1% for all loci. Only family clusters with a probability greater than 95% and inferred relations with a probability also greater than 95% were considered in the results. The resulting relations between individuals were visualized using R package ggraph  (Pedersen et al. , 2017).
2. 2) and at the ocean surface (3-hourly surface winds, hourly currents) by the CNRM-CM6-1-HR climate model and the regional ocean model IBI-CCS (Sect. 2. 2. 1). In the regional wave model, the bathymetry differs from the global model with the use of a smoothed ETOPO1 ocean bathymetry (https://sos. noaa. gov/datasets/etopo1-topographyand-bathymetry/). The spectral discretization is the same as for the global model CNRM-HR-WAV. The time step of IBI-CCS-WAV is set to 240 s. Classical integrated wave parameters are generated hourly for IBI-CCS-WAV.
Data availability Information on CNRM-CM6-1-HR simulations can be found at https://doi. org/10. 22033/ESGF/CMIP6. 4067 (CNRM-CM6-1-HR, historical; Voldoire, 2019a), https://doi. org/10. 22033/ESGF/CMIP6. 4164 (CNRM-CM6-1-HR, piControl; Voldoire, 2019b), https://doi. org/10. 22033/ESGF/CMIP6. 4185 (CNRM-CM6-1-HR, ssp126; Voldoire, 2020a), https://doi. org/10. 22033/ESGF/CMIP6. 4225 (CNRM-CM6-1-HR, ssp585; Voldoire, 2019c). The CNRM-CM6-1-HR forcing fields are available on the ESGF website (ESGF, 2022a: historical data, http://esgf-data. dkrz. de/search/cmip6dkrz/?mip_era=CMIP6&activity_id=CMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=historical; ESGF, 2022b: piControl data, http://esgf-data. dkrz. de/search/cmip6dkrz/?mip_era=CMIP6&activity_id=CMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=piControl; ESGF, 2022c: ssp126 data, http://esgf-data.
dkrz. de/search/cmip6-dkrz/?mip_era=CMIP6& activity_id=ScenarioMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_id=ssp126; ESGF, 2022d: ssp585 data, http://esgf-data. dkrz. de/search/cmip6-dkrz/ ?mip_era=CMIP6&activity_id=ScenarioMIP&institution_id=CNRM-CERFACS&source_id=CNRM-CM6-1-HR&experiment_ id=ssp585). The reanalysis data and wave buoy observations were obtained from the Copernicus Marine Services (Copernicus, 2022a: reanalysis data, https://doi. org/10. 48670/moi-00030; Copernicus, 2022b: observational data, https://doi. org/10. 13155/53381). https://doi. org/10. 5194/egusphere-2022-508 Preprint. Discussion started: 5 July 2022 c Author(s) 2022. CC BY 4. 0 License.
, C-terminus; -, intermediate amino acid residue between two domains. c Variants classified as VUS: no genotype could be established from the literature or database data. d Variants observed in an affected individual with an OPN1MW/OPN1LW-associated genotype indicative for and compatible with a clinical diagnosis of blue cone monochromatism, X-linked. e Variant designation of variants located in the alternate exon 3b (formerly named exon 2b  (Wissinger et al. , 2001) , now renamed exon 3b) and its flanking intronic sequences is based on XM_011510554. 2 (GRCh38/hg38). f Variants only observed in a heterozygous state in the absence of a second likely disease-causing allele.
Funding information Deutsche Forschungsgemeinschaft, Grant/Award Numbers: KO 2176/3-1, 398539671, STI 727/1-1, 399487883; Foundation Fighting Blindness, Grant/Award Numbers: BR-GE-0120-0775-LUMC, BR-GE-0619-0761-INSERM; Instituto de Salud Carlos III (ISCIII) of the Spanish Ministry of Health, Grant/Award Number: FIS PI19/00321; Ministry of Health of the Czech Republic, Grant/Award Number: AZV NU20-07-00182 nucleotide-gated ion channel in cone photoreceptors and is one of the major disease-associated genes for ACHM. Herein, we provide a comprehensive overview of the CNGA3 variant spectrum in a cohort of 1060 genetically confirmed ACHM patients, 385 (36. 3%) of these carrying "likely disease-causing" variants in CNGA3. Compiling our own genetic data with those reported in the literature and in public databases, we further extend the CNGA3 variant spectrum to a total of 316 variants, 244 of which we interpreted as "likely disease-causing" according to ACMG/AMP criteria.
We report 48 novel "likely disease-causing" variants, 24 of which are missense substitutions underlining the predominant role of this mutation class in the CNGA3 variant spectrum. In addition, we provide extensive in silico analyses and summarize reported functional data of previously analyzed missense, nonsense and splicing variants to further advance the pathogenicity assessment of the identified variants. K E Y W O R D S achromatopsia, CNGA3, cyclic nucleotide-gated ion channel, in silico analysis, variant classification, variant spectrum 1 | BACKGROUND Achromatopsia (ACHM; rod monochromacy) is a rare autosomal recessively inherited retinal disorder characterized by impaired cone photoreceptor function. The disease is estimated to affect one in 30,000-50,000 live births worldwide  (François, 1961).
, 2016; Liu & Varnum, 2005; Matveev et al. , 2010; Meighan et al. , 2015; Michalakis et al. , 2017; Muraki-Oda et al. , 2007; Patel et al. , 2005; Reuter et al. , 2008; Shaikh et al. , 2015; Täger et al. , 2018; Tränkner et al. , 2004). ACHM caused by pathogenic variants in CNGA3 is of strong current interest as a first gene supplementation therapy trial has been completed for CNGA3-ACHM (CNGA3: NCT02610582;  Fischer et al. , 2020; Reichel et al. , 2021) , and a number of such therapy trials are ongoing at several sites all over the world for both CNGA3-and CNGB3-ACHM (NCT03758404, NCT02935517, and NCT02599922). Although the primary endpoint of such a trial is safety-which was confirmed, the functional benefits were limited but persistent for the adult treated patients for a period of 3 years  (Fischer et al.
, 2020; Reichel et al. , 2021). These included slight improvement in color contrast sensitivity, improvements in the ability to fixate, to recognize letters and numbers as well as in photoaversion. It has been discussed whether treatment of younger patients may result in greater functional benefit by avoiding amblyopia as a potential limiting factor  (Reichel et al. , 2021). The emergence of therapeutic studies highlights the necessity to classify the pathogenicity of disease-associated variants as unambiguously as possible to enable the right selection of patients for these trials. In the present study, we aim to provide a complete and comprehensive spectrum overview of variants in the CNGA3 gene. The CNGA3 variant database combines the variants of our Tübingen in-house investigated ACHM cohort, variants from literature, and mutation databases and is complemented by extensive in silico analysis of the identified variants for assessing their pathogenic effect.
| Variant nomenclature and establishment of the CNGA3 variant database Variant nomenclature of the identified CNGA3 sequence alterations was based on the NCBI reference sequence NM_001298. 2 and the Ensembl transcript reference sequence ENST00000272602. 7 (GRCh38/hg38 genome assembly) comprising seven coding exons. The first nucleotide of the translation initiation codon ATG is denoted as nucleotide 1. Moreover, all variants located in the alternatively spliced exon 3b (formerly named exon 2b;  Wissinger et al. , 2001;  now renamed exon 3b) and its flanking intronic sequences were denoted based on NCBI reference sequence XM_011510554. 2 (GRCh38/hg38). Validation of the variant nomenclature was performed using the web-based Mutalyzer name checker tool (https:// mutalyzer. nl/name-checker). To establish a comprehensive CNGA3 variant database, a literature search was conducted on the free search engine PubMed ® (https://pubmed.
ncbi. nlm. nih. gov/) (last queried on January 11, 2021). In addition, web-based databases including the Human Gene Mutation Database (HGMD ® ) (http://www. hgmd. cf. ac. uk/ac/index. php)  (Stenson et al. , 2017) , the Leiden Open Variation Database (LOVD) for CNGA3 (https://www. lovd. nl/CNGA3), and the public archive of human genetic variants ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/)  (Landrum et al. , 2014)  were searched for entries regarding CNGA3 disease-associated variants (searches conducted on  January 11, 2021 and March 18, 2021). Variants retrieved from public databases (i. e. , LOVD and ClinVar) that have not been associated with a retinal phenotype (i. e. , ACHM or allied disease) were not included and not validated. Finally, all CNGA3 variants identified in our ACHM cohort were entered into our in-house CNGA3 variant database.
Variants identified in the ACHM cohort genetically characterized in Tübingen that were not yet published in a journal listed on PubMed ® are referred to as "novel. " All novel variants were submitted to the ClinVar database with accession numbers from SCV001571262 to SCV001571322. Additionally, these variants will be submitted to the Global Variome shared LOVD database and can be accessed using the following URL: databases. lovd. nl/shared.
| In silico variant assessment Sequence alterations listed in the CNGA3 variant database were subjected to in silico analysis to evaluate their pathogenic impact. The Combined Annotation Dependent Depletion (CADD) tool was used to score the deleteriousness of both coding and noncoding singlenucleotide variants in the CNGA3 gene (https://cadd. gs. washington. edu/). CADD provides PHRED-scaled scores ranging from 1 to 99. A scaled CADD score of 10 thereby indicates a variant being amongst the top 10% of deleterious variants in the human genome whereas a score of 20 is used to describe variants that are amongst the top 1% of deleterious variants and so on  (Kircher et al. , 2014). To score the identified variants, we used the CADD model GRCh38-v1. 6 and applied a PHRED-scaled score of 20 as a cutoff for deleteriousness as suggested by the authors  (Rentzsch et al.
ebi. ac. uk/Tools/msa/clustalo/)  (Sievers et al. , 2011)  and the alignment visualization tool BoxShade version 3. 21 (https://embnet. vital-it. ch/software/BOX_form. html) (Figure  S2 ). Minor allele frequencies were retrieved from the Genome Aggregation Database (gnomAD) (v2. 0. 2/v2. 1) (https://gnomad.
| Comprehensive CNGA3 variant database To create a comprehensive CNGA3 variant database, we combined all CNGA3 variants identified in our ACHM cohort with all CNGA3 sequence alterations obtained after an extensive literature search on PubMed ® , and database search in HGMD ® , LOVD and ClinVar. The so established CNGA3 variant database currently comprises 316 variants (Tables  2  and S3 ). Of these, four variants are located in the alternate exon 3b and its flanking intronic sequences. As the functional relevance of this sequence remains unclear and no association with disease has ever been reported, these variants were not further analyzed and were classified as "likely benign. " Following the standards and guidelines of ACMG/AMP, 67 out of the remaining 312 variants were classified as pathogenic, 152 as likely pathogenic, and 71 were categorized as variants of uncertain significance (VUS).
Furthermore, 12 out of 312 sequence alterations were classified as likely benign and 10 as benign (Tables  2  and S3 ). The 316 CNGA3 variants listed in our CNGA3 variant database were revised manually and expert-categorized into "likely benign," "VUS," and "likely disease-causing" applying the following criteria. Variants categorized as "likely benign" (n = 44 variants; Tables  2  and S3 ) included: (1) variants classified as likely benign or benign according to ACMG/AMP guidelines, (2) synonymous variants that are not predicted to affect splicing, (3) missense variants with a minor allele frequency (MAF) higher than 0. 1% and predicted to be tolerated by at least two in silico algorithms, (4) missense variants predicted to be tolerated by at least two in silico tools and only observed in a heterozygous state in the absence of a second "likely disease-causing" allele, (5) noncanonical splice variants not predicted to affect splicing and (6) variants located in the alternate exon 3b and its flanking intronic sequences.
Variants classified as "VUS" (n = 28; Tables  2  and S3 ) comprised missense variants that were classified as VUS following the ACMG/ AMP guidelines and were predicted to be tolerated by at least two in silico tools. In addition, 15 variants were grouped in this category since no genotype could be established from the literature or database data. If the following criteria for a "VUS" were fulfilled, we considered patients carrying such variants with respect to the phenotype as solved cases: (1) observation of the variant in a heterozygous state with a (known) "likely disease-causing" variant and/or (2) segregation analysis demonstrated independent segregation of two variants and/or co-segregation of the variant with the disease. All variants in the CNGA3 variant database not meeting the above-mentioned criteria were categorized as "likely disease-causing" (n = 244; Tables  2  and S3 ).
Thus, we included the following variants in this "likely disease-causing" category: (  1  Among 316 CNGA3 variants listed in the CNGA3 variant database, 251 variants were already published in the literature and in public databases. From these, we classified 196 variants as "likely disease-causing", 23 as "VUS" and 32 as "likely benign" following manual and expert-based revision (Table  S3 ). Only a small number of previously published missense variants were categorized as (2) All abovementioned missense variants have a MAF higher than 0. 1% and were predicted to be tolerated by in silico tools underlining their presumably nonpathogenic impact. " Within our CNGA3 variant database, we report 48 novel "likely disease-causing" variants detected in our ACHM cohort with six variants classified as pathogenic, 36 as likely pathogenic, and six as VUS following ACMG/AMP classification (Table  S3 ).
These previously unpublished variants comprise 24 missense substitutions, 14 small indel variants, and 6 nonsense substitutions creating a premature stop codon. Overall, out of the 244 "likely disease-causing" variants in the CNGA3 variant database, missense substitutions were the most frequently observed variant type (66. 8%) followed by small indel variants (16. 8%) and nonsense alterations (12. 3%) creating a premature stop codon (Figure  2 ). An overview of the mutation types and the respective frequencies of these "likely diseasecausing" CNGA3 variants within our database is provided in and one copy number variation leading to the complete deletion of exon 8 (NC_000002. 12:g. 98393909_98399093del (GRCh38/hg38)). Actually, this deletion is the first large structural variation/copy number variation observed and reported for the CNGA3 gene and   Copy number variations (CNVs) are an important mutation type associated with ACHM and are commonly seen in the CNGB3 gene as well as in GNAT2 and ATF6.
All variants affecting the canonical splice sites are predicted to lead to the loss of the respective donor or acceptor site based on five in silico algorithms. Additionally, the previously published variant c. 396-11C>G/p. (?) and the novel sequence alteration c. 450-15T>G/p. (?), both located in the vicinity of a highly conserved splice acceptor site, are predicted to activate an intronic cryptic acceptor site potentially affecting splicing (SpliceAI Δ scores: acceptor loss 0. 87 and 0. 65, respectively; acceptor gain 0. 70 and 0. 53, respectively; Table  S3 ). Furthermore, one small duplication is localized at the exon/intron border of exon 5 (c. 396-2_398dup/p. (?)). Although only one of the in silico tools predicts a minor effect on transcript splicing, the variant is still considered to be "likely diseasecausing" (SpliceAI Δ score: acceptor loss 0.
00; acceptor gain 0. 22). The novel missense variant c. 566G>A/p. (Arg189Lys) affecting the last nucleotide of exon 6 was also predicted to have an effectthough minor-on transcript splicing (SpliceAI Δ score: donor loss 0. 08; donor gain 0. 32). Consequently, it remains unclear whether this variant needs to be considered a splicing or missense variant, as it may also act both ways. The majority of the exonic ACHM-associated CNGA3 variants classified as "likely disease-causing" within the CNGA3 variant database are located in exon 8 including 140 missense substitutions, 46 nonsense/small indel variants, and the single copy number variation (deletion of complete exon 8) (Table  2  and   S3  and Figure  S2 ).
| Prevalence of CNGA3 variants in ACHM From our in-house genetic investigations, we report diseaseassociated CNGA3 variants in 385 affected individuals descending from 304 independent families representing 36. 3% of our ACHM cohort which predominantly comprises Western European and North American patients (Figure  S1 ). This observation is in line with our previous and other studies reporting a prevalence of 5%-41% for CNGA3 variants in cohorts of Dutch and British ACHM patients making CNGA3 the second most frequently mutated gene in European ACHM patients or patients of European descent  (Johnson et al. , 2004; Thiadens et al. , 2009; Wissinger et al. , 2001). The vast majority of all "likely disease-causing" CNGA3 variants listed in our CNGA3 database represent amino acid substitutions (66.