dataesr/scientific-paragraphs-categorization huggingface dataset:
Merging existing databases of invasive species' distributions with the Global Biodiversity Information Facility could be further leveraged for this purpose  (Seebens & Kaplan 2022). For example, understanding social-ecological networks can identify opportunities for cost-effective management and the potential benefits of invasive species  (Hulme et al. 2017; McGeoch & Jetz 2019). Within and outside the context of invasive species impacts, there are numerous opportunities for combining this quantitative measure for social and ecological losses to produce more comprehensive databases alongside other biodiversity threats such as climate change and habitat loss/exploitation  (Seebens et al. 2018; Roxburgh et al. 2020; García-León et al. 2021).
Based on selected examples of species, it has also been argued that benefits of invasions might even outweigh costsfor example, the ecosystem services provided by exotic bivalves  (Burlakova et al. 2022). Currently, there is no comprehensive database that compiles the benefits of invasions in a way similar to what InvaCost does for costs. However, the argument that the presence of benefits lessens the importance or risks conveyed by invasive species does a disservice to natural resource managers and conservation practitioners, and conveys large risks to conservation programs and human wellbeing. This lack of knowledge about the magnitude, origin and distribution of benefits of invasive alien species has been inappropriately used to undermine or refute existing and synthesised estimates of costs  (Boltovskoy et al.
Promoting cost reporting InvaCost aims to increase the quantity and quality of cost reporting. Initially, this was done through the catalysing effect of high-profile studies synthesising estimates at different biological, spatial, and sectoral scales  (Zenni et al. 2021). New scientific, stakeholder, and public awareness, coupled with extensive media coverage, encouraged further primary research to uncover additional costs, and for researchers, stakeholders, and other entities to make existing and emerging costs available for syntheses. Templates to enter cost information into the database, alongside associated explanatory files, examples, and an e-mail account for data submissions (updates@invacost. fr) have all improved cost reporting  (Diagne et al. 2020). This infrastructure will continue to allow new and updated cost syntheses to be readily constructed, while raising project visibility and increasing international collaborations.
2022 ), New Zealand  (Bodey et al. 2022b) , and Nordic countries  (Kourantidou et al. 2022b). Other examples are taxon-focussed, including assessments of costs of invasive plankton  (Macêdo et al. 2022) , mammals  (Wang et al. 2023) , and herpetofauna  (Soto et al. 2022). Aside from leading the analyses and preparing scientific articles, such projects also targeted additional collections of cost data from relevant publications, stakeholders, and organisations. They were often permitted by a foundation of national knowledge and existing connections, as well as breaching language and access barriers. New research to address missing data has been promoted by InvaCost projects. Because many studies identify gaps in costs, there is now an impetus to collect these missing data. In the past, research has been hampered by a lack of syntheses, meaning that cost assessments have been partly fuelled by positive feedback loopscostly species are well-publicised and continue to accrue scientific attention (and thus costs)  (Cuthbert et al.
2021). This has contributed to poor coverage of cost assessments for most known invaders globally. Indeed, this is illustrated when comparing the total number of invasive alien species established per country (Fig. 3a ) with the total costs reported in the InvaCost database (Fig. 3b ), and the still low number of invasive alien species included therein (Fig. 3c ). However, given the absence of costs and the disparity in values among countries (e. g. , countries in Africa), the costs incurred by invasive alien species usually depend on the number of species with reported costs, with a few exceptions (Fig. 3d ). A major strength in cost syntheses is therefore not only putting an aggregate monetary value on the negative impacts of biological invasions, but also highlighting knowledge gaps and future research needs.
Conclusions The InvaCost project improves the quality and quantity of reported costs. This information can improve targeting and prioritising of resource investments and/or which species or pathways should be managed. To maximise their utility, we therefore recommend that future studies clearly specify the location(s), cost per species where possible, and the period when the costs occurred. Authors are welcome to adopt the nomenclature used in the InvaCost database  (Diagne et al. 2020; Supplementary Table 4) , and while not exhaustive, it is sufficient to encompass the main aspects of the potentially impacted sectors, and describe the type of cost and the management related to the time of introduction  (Diagne et al. 2020). Promoting improved granularity and quality of cost reporting has therefore been another positive outcome, helping to direct efficient allocation of resources, foster method alignment, and detect overlaps in cost assessments.
While InvaCost provides a solid foundation for estimating costs of invasive species, we acknowledge that the derived estimates are merely the tip of the cost iceberg, with the biodiversity and social impacts they entail in particular requiring much additional assessment. Table  S1  InvaCost publications List of 52 peer-reviewed articles that have used the InvaCost database, categorised according to themes: taxonomic (11), conceptual (7), habitat (3), sectoral (3) and geographical (23; regional, continental and national). The studies, trees  (Fernandez et al. 2022) , primary  (Turbelin et al. 2023b ) are available in preprint repositories, and hazards  (Turbelin et al. 2023a).
Acknowledgements The  InvaCost  project was funded by the  French National Research Agency  ( ANR-14-CE02-0021 ), the  BNP-Paribas Foundation Climate Initiative , the  AXA Research Fund Chair of Invasion Biology of University Paris Saclay  and by the  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios (AlienScenarios;  BMBF/PT DLR 01LC1807C ). M. K. received funding from the  European Union 's  Horizon 2020 research programme  under a  Marie Skłodowska-Curie  grant agreement  899546. C. J. A. B. acknowledges the  Australian Research Council  ( CE170100015 ) for support. R. N. C. is funded by the  Leverhulme Trust  ( ECF-2021-001 ). A. B. acknowledges  Azim Premji University 's grants programme ( UNIV-RC00326 ) for support. G. H. was supported by  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (Capes)   -(001 ) and the  Alexander von Humboldt Foundation.
1 1 Further development of the InvaCost database, version 5. 0 InvaCost is a dynamic database, and has therefore evolved over time as more cost entries are added or existing ones are corrected. The living figure provides the most up to date global value of the taxonomic and geographic costs of biological invasions based on the latest version 4. 1 (at the time of writing; last updated 15 February 2022) (see.
Cuthbert et al. (2021b) citation Diagne, C. , Ahmed, D. A. , et al. (2021). Global economic costs of aquatic invasive alien species. Sci. Total Environ. , 775, reference 3 version 145238 doi:10. 1016/j. scitotenv. 2021. 145238 Diagne, C. , Leroy, B. , Gozlan, R. E. , Vaissière, A. -C. , Assailly, islands database Australian Academy of Science (2022). Australia's data-enabled research future: Science. Bodey et al. (2022a) Diagne et al. (2020) Jun-22 science. org. au/supporting-science/science-policy-and-analysis/reports-Bodey, T. W. , Angulo, E. , Bang, A. , Bellard, C. , Fantle-Lepczyk, J. , Lenzner, B. , et al. (2022). Economic costs of protecting islands from invasive alien species. Conserv. Biol. , e14034 C. , Nuninger, L. , et al. (2020). InvaCost, a public database of the economic costs of biological invasions worldwide.
net, Newsbreak, The conversation -Phys. org -National Geographic NewsBeezer, MSN, National Geographic Catalunya Avanguardista Dicyt The Orkney News Phys. org DNA barcoding associated outlet(s) -Phys. org, Futures TradingCharts. com, MarketScreener Yahoo! News, The conversation, Newsbreak NewsBreak associated outlet(s) South Africa Today Australian Greens, Mirage News Phys. org, The Conversation TimminsToday. com COSMOS magazine Entomology today Environewsbits Outlook India, Devdiscourse, Mirage Phys. org news, Newsbreak EurekAlert! Winnipeg free press, Futures tradingCharts. com, Phys. org, Foregin Affairs New Zealand, Phys. org, Newsbreak, The conversation associated outlet(s) MSN, The Guardian news stories (outlets) ------6 (5) 70 (57) news stories -(outlets) news stories (outlets) 2 (3) 3 27 (19) news stories (outlets) tweets 22 (20) 2 25 (21) 19 (15) 37 (28) 57 (43) (users) 96 (86) tweets 18 (17) (users) tweets (users) 501 247 (209) 133 (65) 130 tweets (users) 2 crustaceans continental 7 mammals 1 Africa 3 fish 4 gastropods 8 terrestrial invertebrates protected areas database 2 Asia Brazil sectoral Ecuador Kouba et al.
If we focus on classification evidence, on the one hand, one of the most challenging criteria is PM1 "Located in a mutational hot spot and/or critical and well-established functional domain (e. g. , active site of an enzyme) without benign variation", which was used in about 10% of reported cases  (Amendola et al. , 2016). To extract this information, automated systems rely primarily on UniProt (The Uniprot Consortium. 2017) and "dbnsfp31a_interpro", which is a database of domain information from dbNSFP  (Liu et al. , 2011; Liu et al. , 2016)  and InterPro  (Mitchell et al. , 2019)  which incorporates information on protein families, domains, and functional sites. Other initiatives have been developed using the Conserved Domain Database (CDD)  (Marchler-Bauer et al. , 2015) , such as the subRVIS score  (Gussow et al.
, 2016) , which aims to assess the intolerance of gene subregions to variants. In general, the PM1 criterion is associated with a broad view of the functional regions where mutations cluster. However, it is difficult to use because this clustering is poorly defined and understood, as illustrated by its various interpretations in Vasome  (Kopanos et al. , 2019)  and InterVar  (Li and Wang, 2017). It can also be highly biased by the interest of genes in human diseases and thus to the number of variations classified. On the other hand, the most used evidence is the PM2/BA1/BS1 "Variant absent in population databases or with an allele frequency too high for the disease" reported in about 50% of cases  (Amendola et al. , 2016). The assumption of this criterion is very simple: if a variant has been reported with a high frequency in the general population, it cannot be a rare pathogenic variant, otherwise the frequency of this disease would be higher; if a variant has never been reported, or with a very low frequency, then it can be a rare pathogenic variant.
This information benefits greatly from the large-scale genome/exome sequencing projects and most people collect this information from gnomAD  (Koch, 2020)  or populationspecific databases, such as the ABraOM (Brazilian population)  (Naslavsky et al. , 2017) , TogoVar (Japanese population)  (Mitsuhashi et al. , 2022)  or Greater Middle East Variome (Middle East population)  (Scott et al. , 2016). Nevertheless, human evolution has not allowed for genome saturation of variants and some of them are very rare in the population due to genetic drift  (Bach, 2019). Indeed, the chance appearance of a neutral variant not subject to a selection force will most likely result in its disappearance after several generations if the population is large enough, and only a few will be fixed in the population.
Substitution scores for specific domains through positional score matrices All protein domain information was extracted from the Pfam database  (Finn et al. , 2016). We used the Pfam 33. 1 version of May 2020, containing 18,259 entries. Among these domains, we used the Pfam-A subset of 18,101 curated domains for further analysis  (Sonnhammer et al. , 1997). Each alignment was filtered to remove information from Archaea, bacteria, viruses, and other sequences to retain only data from eukaryotes. Overall, they contain information from 27,077,043 domains from 1,161 species. The human protein domains were extracted from the canonical Uniprot transcripts used in Pfam and represent 5,168,776 amino acids out of the 12,871,017 amino acids in human proteins (40. 2%). For each residue, we then calculated an amino acid value using the following steps: creation of a count matrix, a corrected frequency matrix, a corrected relative frequency matrix, and the position score matrix.
The counting matrix First, we created a script to count the occurrence of each amino acid at each position in the alignment and to transfer this data into a count matrix: C (p, l). Let A be a multiple alignment of length L and ψ a finite alphabet (single letter codes of amino acids). A count matrix denoted C representative of the alignment A is a matrix L × |ψ | such that for any letter l ∈ ψ and any position p ∈ {0. L-1}, the index element l and p of C denoted C (p, l) is defined by the number of occurrences of the letter l in position p of the multiple alignment A.
For each missense variation in a specific domain, we selected the most frequent mutational event leading to that substitution in all proteins containing that domain. For example, the NP_000129. 3:p. Thr2032Ser variant of the FBN1 gene corresponds to the 135th residue of the Pfam "Calcium-binding EGF protein" (PF07645) domain. The DOLPHIN frequency of this specific residue was set to 2. 41 × 10 -5 extracted from the NP_ 001989. 2:p. Thr784Ser variant of the FBLN2 gene that impacts the 135th residue of this domain. Data were stored in a substitution frequency table.
The DOLPHIN database and the website The DOLPHIN database has been developed using PostgreSQL version 11 (postgresql. org). The web interface was created with the Laravel framework version 6 ("http://www. laravel. com"). The alignment logos were obtained from the Skylign tool  (Wheeler et al. , 2014). End users can query the system for a gene, transcript, or protein and then select a specific variant. Intuitive graphical displays and tables make it easy to retrieve HMM logos, alignments, predictions, as well as frequencies for all variants in protein domains (Supplementary Figure  S1 ). To allow rapid access to DOLPHIN data, an API is also available. Il allows retrieval of the gene symbol, variation using the HGVS p. nomenclature, the Pfam entry names, position of the variation in a protein domain, reference and alternative amino acids, DOLPHIN "wt", "mutant" and Δ scores, PM1 and PM2/ BS1 predictions, as well as the domain symbol and name, from a single protein substitution localized in the protein domains (Supplementary Figure  S2 ).
2:p. Cys1061Tyr (NOTCH3). In other situations, they correspond to protein domains absent from Uniprot as illustrated by the pathogenic variant NP_ 000536. 6:p. Pro447Leu (HNF1A) that is localized in the Pfam domain (PF04812) of HNF-1B that has no match in UniProt (Table  2 , Supplementary Figures  S4A, B1 ). InterVar false positives correspond to variants localized in protein domains without benign variations when the variant itself does not involve a key domain residue. This is illustrated by the benign variants NP_ 000242. 1:p. Thr564Ala (MSH2) and NP_000359. 1:p. Phe285Val (TSC1) (Table  2 , Supplementary Figure  S4C, D ). DOLPHIN false negatives (e. g. , pathogenic variants NP_714915. 3:p. Leu349Ser (TMEM67) and NP_001186036. 1:p. Arg242Cys (TBC1D24)) and false positives (e. g. , benign variants NP_000240.
1:p. Glu600Gly (MLH1) and NP_009225. 1:p. Val1804Asp (BRCA1)) are all located in the C DOLPHIN zone, the area where pathogenic and benign variations overlap (Table  3 , Supplementary Figure  S5 ). Since the ClinVar classification takes into account all ACMG criteria, it is very likely that some missense variations classified as pathogenic may indeed impact on splicing and thus appear as false negatives when considering DOLPHIN scores. To evaluate this hypothesis, we used the HSF Pro system from GenOmnis (genomnis. com) (Desmet et al. , 2009 ) on all false-negative variants located in zones C and D. We found that among the 2,003 variants, 61 have an impact on splicing (out of a total of 100 in the entire dataset), 314 have a potential impact, 820 probably  100 species published by UCSC using phyloP  (Pollard et al.
, 2009)  and phastCons  (Siepel et al. , 2005)  based on the "multiz100way" multiple sequence alignment (MSA) generated by Multiz software  (Blanchette et al. , 2004). To assess whether amino acid conservation of a given protein in different species provides distinct information about the conservation of these amino acids in protein domains, we used the 9,121 ClinVar variants. As shown in Supplementary Figure  S7 , using the Spearman rank correlation coefficient, we obtained a moderate positive correlation (ρ = 0. 50). This difference is illustrated by the uneven distribution of conservation, with 54% of residues showing very high conservation between species (>85%) versus only 3. 6% showing very high conservation within domains. This difference can be illustrated by the example of the 37th (Supplementary Figure  S8A ) and 10th (Supplementary Figure  S8B ) repeated "calcium binding EGF-like domain" (PF07645) of the fibrillin-1 protein, which contains 37 such domains in Pfam.
, 2017)  that takes into account somatic variants from The Cancer Genome Atlas (TCGA). We believe that the selection pressure is different when considering somatic events in cancer cells versus germline events and their impact on the whole organism. DOLPHIN and OncoDomain are therefore complementary. Today, automatic classifiers, such as InterVar and VarSome, do not assign the PM1 criterion in the same way, even though they both use data from the same reference databases (e. g. , InterPro). Thus, InterVar excludes all protein domains containing variants annotated as benign or common (allele frequency >5%) and does not consider hotspots. VarSome considers protein domains that contain at least one annotated pathogenic variant if the ratio of pathogenic variants versus non-pathogenic and VUS variants is above 50%.
In addition, both approaches rely on manual annotation of variants, which introduces a bias because only a minority of the observed variants are currently classified. In comparison, DOLPHIN, using only residues conservation in eukaryotes, is free from annotation of variants and the presence/ absence of benign variants. To do so, it uses "wt" and "Δ" scores for each variant located in a protein domain. Based on the variants in the ClinVar database located in protein domains and a k-means clustering approach, we demonstrated that DOLPHIN could efficiently assign a PM1 label. This was extended to all potential variants in human transcripts, resulting in 15,841,959 variants (30. 0%) being assigned the PM1 label. We therefore propose to restrict the PM1 criterion to this subset of variants that was determined by a standardized approach provided by DOLPHIN, consistent with the harmonization goals of the ACMG-AMP recommendations.
In total, DOLPHIN will provide a PM1 or BP8 label to 63. 2% of variations localized in protein domains. Arguably, BP8 is a computational criterion that can overlap the BP4 criterion provided by in silico tools. We believe that BP8 should not be considered as computational evidence but rather as observational evidence. Concomitantly, as  Amendola et al. (2016)  report, the most widely used criteria for variant classification are PM2/BA1/BS1 "variant absent in population databases or with an allele frequency too high for the disease". As more and more genomes are sequenced and their data shared in central databases as gnomAD  (Koch, 2020) , their variant frequency content is directly linked to human evolution in relation to genetic drift and selection pressure. Thus, even if tens of millions of samples were available, these evolutionary forces could limit this information.
One way to circumvent this limitation is to access another layer of information that is provided by the protein domains. Because these domains are present in multiple proteins, they represent a much longer evolutionary period for a larger population and provide much more information. Comparing of the gnomAD and DOLPHIN data reveals that the former provides information for 3,990,680 (7. 6%) residues out of the 52,774,671 potential protein domains substitutions, while the latter provides frequencies for 16,764,498 amino acid substitutions (31. 8%). We are fully aware that the frequency information in DOLPHIN does not give the exact frequency of a given amino acid substitution at a given protein residue, unlike gnomAD, but rather the highest frequency of that specific substitution among all corresponding protein domains in any human protein.
3% which is more than threshold 50. 0% MLH1 NP_000240. 1:p. Glu600Gly Benign DOLPHIN UK Domain: Mlh1_C (PF16413) 5C C AA #502 to 756, wt = 1. 4, Δ = -2. 8 InterVar No - VarSome Yes UniProt protein MLH1_HUMAN region of interest 'Interaction with EXO1' has 66 non-VUS missense/ in-frame/non-synonymous variants (50 pathogenic and 16 benign), pathogenicity = 75. 8% which is more than threshold 50. 0% BRCA1 NP_009225. 1: Benign DOLPHIN UK Domain: BRCT (PF00533) 5D C p. Val1804Asp AA #1756 to 1842, wt = 1. 3, Δ = -4. 6 InterVar No - VarSome Yes UniProt protein BRCA1_HUMAN domain "BRCT 2"has 64 non-VUS missense/in-frame/non- synonymous variants (37 pathogenic and 27 benign), pathogenicity = 57. 8% which is more than threshold 50. 0%.
Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function.
Introduction SMLM has induced a major paradigm shift from conventional light microscopy modalities: SMLM datasets are composed of the precise coordinates, in 2D or 3D, of the molecules being acquired. As pixel-based analysis methods were not adapted, these point clouds have required the development of a whole new set of analysis tools based on geometry and spatial organization  (Khater et al. , 2020). Among those, Voronoi-based methods have emerged as one of the standard in the field, whether it be for clustering  (Andronov et al. , 2016) , segmentation  (Levet et al. , 2015; Peters et al. , 2017)  or colocalization  (Levet et al. , 2019; Ejdrup et al. , 2022)  and motion  (Beheiry. et al. , 2015)  analysis. Nevertheless, the rapid adoption of SMLM by life scientists has highlighted an important limitation of existing analysis methods.
As datasets composed of millions of localizations are now routinely acquired in biology labs and facilities, it has become obvious that very few methods were capable of scaling to this magnitude, and that especially in 3D. This results in the necessity to define small ROIs composed of 10 4 -10 5 localizations for analysis, as quantifying a whole dataset in one go is out of reach. For instance, and while 3D analysis with Voronoi diagrams is available for a few years now, it takes several hours to quantify datasets in the 1. 10 6 localization range  (Andronov et al. , 2018). This unfortunately lacks the interactivity required when analyzing an unknown biological model, as life scientists may need to fine-tune their analysis parameters through several attempts. In this paper, we propose to optimize two Voronoi-based quantification methods for segmentation and clustering, SR-Tesseler  (Levet et al.