dataesr/scientific-paragraphs-categorization huggingface dataset:
No data Incomplete spatial coverage by features with 'no data' regions affects the implementation of forest-based classification. Any training points located in a 'no data' region are ignored limiting an already sparse training set. In addition, no data regions do not produce predictions for economic mineralisation, regardless of the spatial coverage of other features. This presents an important consideration especially given most geoscience datasets do not provide full coverage. Interpolation is thus required in areas of no data, adding a layer of subjectivity in the form of assumptions associated with algorithm and parameter selection. Another case is that the feature does not extend very far spatially, such as isopach and stratigraphic thickness (Fig. 5 ), because the geological object does not.
The Marra Mamba Iron Formation, for example, is relatively thin and presents limited outcrop, and any feature based on the Marra Mamba Iron Formation alone is subsequently limited. The model is only trained on deposits located in the Marra Mamba Iron Formation, and predictions are only made to where the Marra Mamba Iron Formation extent is mapped. This issue has been addressed by adding a background value close to zero in locations of 'no data' where a near-zero value is appropriate, such as thickness. This practice needs to be taken with care, as near-zero values may not be appropriate in some circumstances, such as using a digital elevation model or other datasets with signed values (e. g. magnetics and gravity), or any normalised feature. Features generated from potentially incomplete sources also impact model predictions.
The structurally flat-lying northern region provides another illustrative example with a lack of mapped or interpreted faults and folds as compared to the rest of the map (Figs. 1  and 5a ). Higher feature importance rankings for both folds and faults is relatively consistent across all models (Fig. 8 ). If locations proximal to fault and fault traces modelled to be prospective, locations underrepresented by fold and fault data are subsequently under-predicted. Whether these structural datasets are incomplete and other structures are present but not recorded, or simply because the region is largely undeformed and the structural datasets are complete presents a scenario that is a constant dilemma facing MPM practitioners. The best approach one can take is to ensure all observations and interpretations have been considered and align with confidentiality concerns (e.
Geochemistry We note that  Angerer et al. (2015) ,  Duuring et al. (2016)  and  Perring et al. (2020)  suggest bulk rock geochemistry as very useful to elucidate vectors to mineralisation. Indeed, geochemistry has long been recognised as a useful Fe ore exploration tool (Ewers and  Morris, 1981; Webb et al. , 2003). For example, Si content is not reliably represented by the ASTER Si content feature in the absence of geochemistry. While Si content of some outcrop is likely captured by this feature, the relative unimportance suggests that a dataset like geochemistry may provide a more useful source for Si distribution. Thus, logic would dictate the inclusion of geochemistry to this study, however no publicly available data is available from the area.
Plausibility tests Plausibility criteria would be more meaningful if defined using a dataset not included in the original model as a feature. While the stratigraphic units shown in the geological map are useful as a test for plausibility, some issues remain. Firstly, an independent feature for plausibility is needed, however difficult to find. If the geological map was withheld purely for plausibility testing, a key feature is then unavailable for prediction and unnecessarily hinders modelling. Another dataset could be chosen, such as geochemistry, or geophysics, but this suffers from the same issues. If we have data available to make good predictions, it will necessarily then be a good test for plausibility. Realistically, given the sparsity of geoscientific data, we must make best use of everything when building models  (Lindsay et al.
Acknowledgements The authors gratefully acknowledge the financial support of the  ARC ITTC DARE  Centre  IC190100031  (ML, MJ, RS, EC), the  ARC DECRA  scheme  DE190100431  (ML),  ARC  Linkage  Loop3D LP170100985  (ML, MJ, GP, JG),  MRIWA  Project  M0557  ( NP, MJ ) and  MinEx CRC  (ML, MJ, JG, GP). JG acknowledges support from  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 101032994. The work has been supported by the  Mineral Exploration Cooperative Research Centre  whose activities are funded by the  Australian Government's Cooperative Research Centre Program. This is MinEx  CRC Document 2022/28. We thank  CSIRO Mineral Resources  staff  Vaclav Metelka ,  Sandra Occhipinti  and  Andrew Rodger  for guidance and discussion on prospectivity modelling, mineral systems and forest-based classification.
Similarly, it can identify inequalities -such as continents, countries or other regions that are net receivers of IAS or their costs -that should be addressed to meet the Sustainable Development Goals (for instance  Goal 10 [25] ). It could also highlight opportunities for prioritized biosecurity actions, such as risk screenings for imported goods or early-warning surveillance systems for potentially costly IAS from speci c origins. Identi cation of socioeconomic and environmental predictors of invasion costs could also help to inform proactive management  [26]. The InvaCost database (Supplementary Note 1) documents economic costs of IAS reported globally. It allows for standardizing and comparing costs at taxonomic  [11] , sectoral  [15] , regional  [27] , and global levels  [5]. To investigate the spatial distribution of senders and receivers of sustainability challenges triggered by invasions, we quantify the monetary burden within and owing between sender and recipient regions (continents or countries).
Continent-level patterns Numbers of IAS sent and their costs were unevenly distributed across continents (Figures 1). Northern continents (Europe, Asia and North America) both sent (67%) and received (66%) the majority of IAS. "IAS" here, and throughout the results, refers only to non-domesticated species, with costs in the InvaCost database, and for which we could identify ≥1 native continent or country (see Methods). The Northern continents also sent 82% and received 95% of the total invasion cost in our dataset ($467 billion from 459 IAS; all costs reported herein are in 2017 United States of America [USA] dollars). Our database did not contain any costs attributable to single species in Antarctica. Asia sent the largest share of IAS (29%), followed by North America (25%), Europe (13%), South America (13%), Africa (13%) and Oceania (7%).
Additional analyses including species without country-level origin information substantially changed the top ten receiver countries, resulting in several more entries from Asia (Supplementary Figure  4 ). Of the 223 countries in our dataset, only 17 were net receivers (that is, costs incurred from IAS in these countries were greater than costs of IAS native to these countries). The largest net receivers were the USA ($335 billion), Canada ($10 billion) and the Philippines ($2 billion). There was at least one net receiver country on every inhabited continent. The other 206 countries were net senders (that is, costs of IAS native to these countries were greater than costs incurred from IAS in these countries); 143 of these sent costs without receiving any, whilst 63 both sent and received costs.
Predictors of cost ows The country-level cost ow model indicated signi cant positive effects for the number of IAS, total cost sent by each country, recipient country area, presence of a shared biome across the two countries, presence of a common language, and gross domestic product (GDP) of the two countries (Figure  4 , Supplementary Table  3 ). This model ts signi cant negative effects for the existence of a present-day free trade agreement between countries, the presence of a shared border between the countries, the distance between countries, the number of cost references included in InvaCost for the origin country, population size of the receiving country, and historical (1990s) trade volumes. In other words, countries receiving the highest costs had a larger area, higher GDP and smaller human populations.
Data gaps and caveats It is important to highlight and caveat factors that may have strongly in uenced the trends exhibited in the present study. Firstly, InvaCost is entirely dependent upon recorded costs reported in original studies and such reporting of economic costs of biological invasions is distributed highly unevenly, geographically and taxonomically  [5] , and frequently lacks speci city. Indeed, costs in InvaCost are known to be skewed towards just a few well-studied taxa in certain places  [5] , with several hyper-costly species (for example, Formosan termite, yellow fever mosquito, Aedes aegypti and boll weevil, Anthonomus grandis) likely to disproportionately in uence global trends, while indicating massive data gaps for other known damaging IAS  [39]. Secondly, publication biases may in uence cost ows, where regions with greater reporting effort in common languages (English) such as North America are better represented.
Although updates to InvaCost now include data in 21 non-English languages, regions such as Asia and Africa remain heavily underrepresented, with numerous countries having no costs in InvaCost  [20]. Thirdly, purchasing power affects the cost of damage and management incurred by a region; all else being equal, regions with higher purchasing power (such as Asia, Europe and North America) would inherently incur higher costs. Fourth, socio-cultural factors will also change the likelihood of invasion management in ways we cannot capture in this analysis. For instance, impacts on ecosystems and health are di cult to monetize, but are also a key motivator for management action  [9]. Fifth, we do not account for the many IAS of unknown origin, or for the precise invasion trajectory taken by each one.
Cost data and processing We extracted cost data from the latest version of the InvaCost database (version 4. 1, publicly available at 10. 6084/m9. gshare. 12668570 [20, 41] ). InvaCost has been generated following a systematic, standardized methodology to collate invasion costs from peer-reviewed scienti c articles, o cial reports, grey literature, and stakeholder and expert elicitation. Following a thorough and hierarchical screening of each source document for relevance, costs were extracted, standardized to a common currency (2017 USA dollars/US$), and adjusted for in ation through the Consumer Price Index (https://data. worldbank. org/indicator/FP. CPI. TOTL? end=2017&start=1960) to be comparable across space and over time  [41]. Costs were categorized under a range of descriptive elds pertaining to the original source (such as title, authors and publication year), spatial and temporal coverage (such as period of estimation, study area), cost estimation methodology (such as method reliability and acquisition method) and the cost estimates per se (such as nature and typology of cost relating to damage and/or management costs).
Detailed information on all descriptive variables can be found in an online repository of the InvaCost database (https://doi. org/10. 6084/m9. gshare. 12668570, "Descriptors4. 1. xlsx"). Costs can occur over varying periods; for example, a one-off cost associated with a one-time eradication effort versus a multi-year cost associated with recurrent, annually estimated damages to crop production. To homogenize the temporal occurrence of these cost entries in the database, they were all converted to annual costs using the expandYearlyCosts function of the invacost R package  [42]. This function provides annualized cost estimates for all entries, based upon the probable starting and ending years of the cost occurrence provided in the database ('Probable_starting_year_adjusted' and 'Probable_ending_year_adjusted' columns).
These lters thus allowed us to consider costs (i) from individual IAS in de ned recipient continents or nations, and for which regional origins could be determined, (ii) that were actually incurred, reported and estimated through "highly reliable" methods, and (iii) at appropriate, distinct spatial scales. The aforementioned lters, however, also mean our reported costs are underestimated and uneven due to reporting differences regionally. Unless speci ed differently, all results are provided for the ltered dataset.
Species origins As a rst step in determining species' countries of origin, we employed a web scraping script to gather data from the Centre for Agriculture and Bioscience International (CABI) Invasive Species Compendium (ISC, www. cabi. org/isc), the International Union for Conservation of Nature (IUCN) Global Invasive Species Database (GISD, http://www. iucngisd. org/gisd/) and the Global Biodiversity Information Facility (GBIF, www. gbif. org) (see www. github. com/emmajhudgins/Givers_Takers for more information). CABI's ISC contains a variety of information on IAS around the world, including their current distribution and countries of origin  [18]. Our script searched using the species names as entered within InvaCost (harmonized using the GBIF. org Backbone Taxonomy;  [39] ) as well as synonyms in the Integrated Taxonomic Information System (ITIS) database via the taxize R package  [43].
If a species match was found within the CABI ISC, we searched for a "Distribution Table" portion of the species' entry. If found, we extracted country or region (within country) names tagged as "Native" within this table. GISD contains geographical information for many IAS, and was used as an alternative to CABI where distributional data were missing. Our script searched for GISD distributional data points tagged "Native" and compiled them at the country level. Finally, we checked for matching entries in GBIF -a global database of all types of species distribution -tagged as "Native" at the country level within the occ_search function of the rgbif package version 3. 6. 0 [44]. We used present day political border de nitions for each country as de ned by ISO3C codes in the countrycode package  [45].
In these two cases, we classi ed recipient regions based on human population, because of the role of humans in transporting IAS  [46]  and incurring economic impacts  [15]. Since most of Turkey's population is in Asia and the majority of Russia's population is in Europe, we assigned them accordingly to these continents. As a third exception, China's Special Autonomous Regions (Hong Kong and Macau) and Taiwan were merged with mainland China due to them representing a much smaller landmass, as well as being strongly linked to China politically, economically and geographically. All origin assignments were checked manually by co-authors (where we ensured that there existed 1 reliable sources that agreed on the origin continent at least), or were entered for the rst time when information was unavailable from GISD and CABI, using available literature and databases.
Literature was identi ed through ad hoc, informal searches, so it is possible that some known native countries were missed. However, this is likely to be a small issue compared to the number of native countries that have never been identi ed in the literature. A list of literature sources used to check the species' origins is provided in Supplementary Note 4. Some species were allocated only to a continent of origin, due to the absence of country-level data (see later). Origin information was identi ed for 467 unique species with cost records that met our aforementioned lters (high reliability, observed records within de ned continents, cost incurred 1960-2020, non-pathogens). Of these, eight were removed due to a domesticated status (cat, Felis catus; dog/wolf, Canis lupus; sheep, Ovis aries; dromedary camel, Camelus dromedarius; pig, Sus scrofa; horse, Equus caballus; donkey, Equus asinus; and goat, Capra hircus, and with cow, Bos taurus, and ferret, Mustela furo having been removed by previous lters).
This set of species does not have clear native ranges due to their long domestication and/or hybridization history. In contrast, we opted to retain species such as the European rabbit (Oryctolagus cuniculus) with a wellde ned native range  [47]. The remaining 459 unique species were recorded in six origin and recipient continents, amounting to 4,107 cost entries reported across 539 independent publications (expanded to 8,060 total entries; Figure  5 ). When subset to entries with a country-level resolution, our dataset was further restricted to 412 unique species in 223 origin and 80 recipient countries, corresponding to 3,685 raw cost entries, 436 unique publications, and 7,112 expanded entries. Overseas territories were removed from this portion of the analysis because they lacked trade volume, GDP, and/or population data, which were implemented in models (see Predictor variables).
Predictor variables We separated our analysis by decade. Then, from InvaCost version 4. 1, we generated a variety of predictor variables that we hypothesized would in uence the magnitude of cost ow to and from different locations (where the cost ow from Region A to B refers to the costs of IAS in Region B that are due to native species from Region A). Firstly, we extracted the number of unique cost references associated with each receiving country in each decade, as a proxy for research effort ("Reference_ID" eld in the InvaCost database). Secondly, we summed the total number of species causing the impact ows between countries for each decade. Thirdly, we summed the total cost, incurred between 1960 and 2020, of IAS originating from each country. Beyond these InvaCost-speci c predictors, we employed several external variables hypothesized to in uence the magnitude of cost ows due to biological invasions  [48].
We extracted the total volume of imported goods (in metric tonnes) for each country pair from the Centre d'Études Prospectives et d'Informations Internationales' BACI database  [49]  for the years 1995-1999 inclusive and 2015-2019 inclusive, selecting the HS92 designation of harmonized import and export records (see Supplementary Table  2  example data from 10 pairs). We calculated the mean annual ow of goods between each country pair for the 1995-1999 period and dubbed this 'historical trade'. Historical trade can be more predictive of present-day invasion risk due to invasion lags (see  [48] ), but we note that consistent import data are not available for the entire period of our cost data. The mean annual ows for 2015-2019 re ect recent trade (prior to the COVID-19 pandemic). To assess the role of origin and recipient biodiversity in dictating the ow of invasion impacts, we downloaded species richness data for each country from Mongabay, which tallies species richness for amphibian, bird, sh, mammal, reptile, and vascular plant species  [50].
As a proxy for environmental matching, we identi ed countries that shared at least one terrestrial biome using data from  [51]  and Global Administrative Areas v3. 6. We assume that country pairs sharing terrestrial biome(s) also share some freshwater and marine environments with similar conditions. Note that we also tested the effect of a shared climate zone variable, but the biome model had greater deviance explained. We also used the mean annual GDP and human population of each decade, and surface land area (reported in 2018, and measured in square km, including inland waters, but excluding marine Exclusive Economic Zones) from the World Bank using the wbstats R package, and the distance between countries variable from the Centre for Prospective Studies and International Information (CEPII) database (calculated between two most populous cities), which has been previously employed to model invasional ows  [2].
From this same paper, we also extracted information on common language (spoken by at least 9% of the population in each country), shared geographical borders, the existence of a free trade agreement between countries, and a shared colonial history (CEPII GeoDist and Gravity Databases). Missing predictor variable values were lled in with either the closest decade of available data (372 missing sender country+decade pairs for GDP, one missing sender     3  for the complete list of estimates and associated p-values. All models displayed a 'worst-case' concurvity value below 0. 8, indicating they were not over t.
Acknowledgements The authors thank the  French National Research Agency  ( ANR- 14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project and the InvaCost database development. The present work was conducted in the frame of the InvaCost workshop conducted in November 2019 (Paris, France) and funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  Alien-Scenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. EJH is funded by a  Fonds de Recherche du Québec -Nature et Téchnologies Postdoctoral Fellowship. RNC is funded through a  Leverhulme Early Career Fellowship  ( ECF-2021-001 ) from the  Leverhulme Trust  and a  Humboldt Postdoctoral Fellowship  from the  Alexander von Humboldt Foundation.
Article No : 491 ¨ LE ¨ TYPESET MS Code : þ CP þ DISK In Fig. 7A  the SDS-PAGE analysis of the hard corona is shown. Clearly, the treatment with SHDS affected the corona composition since treated and untreated samples had different protein patterns. Proteomic analysis by mass spectrometry for these samples identified more than 200 proteins for the corona of both SHDS-treated and untreated FNPs (Fig. 7B ). The top 20 most abundant proteins are listed in Table  2. Label free quantification (LFQ) calculated with Perseus was used to compare the protein abundance between the two samples. Within the top 20 most abundant proteins, some serine proteases derived from SHDS were found in the SHDS treated sample. Different chymotrypsins derived from pancreatin were also detected among the less abundant proteins (data not shown).
9A  and B ). We did not detect any significant cytotoxicity changes between SHDS-treated and untreated FNPs (Fig. 9C ), while HNPs were the only NBM showing lower cytotoxicity after SHDS towards both Caco-2 (Fig. 8D ) and HCoEpiC cells (Fig. 9D ). These data show a different behaviour on epithelial intestinal cells in relation to the nature of NBMs that can undergo different modifications during SHDS. Moreover, the choice of in vitro model to evaluate cytotoxicity is also of paramount importance, as demonstrated by the different sensitivity between Caco-2 cells, HCT116 cells and primary non-transformed cells. The genotoxicity of NBMs was then evaluated on HCT116 cells because, although it is a cancer-derived cell line, it bears wild-type p53 contrarily to Caco-2 cells. None of the tested NBM induced any significant increase of DNA strand breaks as assessed by counting 53BP1 DNA repair foci, while the positive control, i.
NBM intestinal barrier crossing The results obtained with the Lucifer Yellow test suggested that the tested NBMs are unable to cross the barrier, even after SHDS-treatment. To monitor the NBM possible transcellular translocation, the barrier model was exposed to CNPs and FNPs and the particle concentration in the basolateral compartment of Caco-2 GI barrier was monitored by Nanoparticle Track Analysis (NTA). These samples were selected because the technique is more sensible on samples with a highly refractive index. No particles were found in the basolateral compartment after treatment with both SHDS-treated and untreated CNPs and FNPs (data not shown). These data were confirmed for FNPs using the FerroZine ™ assay which showed no significant presence of iron (data not shown).