dataesr/scientific-paragraphs-categorization huggingface dataset:
A new informatics tool based on artificial intelligence, AOP-helpFinder, has been developed to identify reliable associations between events and a chemical of interest  (Carvaillo et al. , 2019; Rugard et al. , 2020). Yet, additional automated tools are required to screen even larger number of texts and datasets with a higher efficiency and relevance. Here, we have developed an updated version of the AOP-helpFinder tool called AOP-helpFinder 2 and applied it to a set of pesticides that have been prioritized in the human biomonitoring in Europe project (HBM4EU) (i. e. pyrethroids, chlorpyrifos and glyphosate) (https://www. hbm4eu. eu/). Python codes and examples are freely available to the scientific community under GitHub. To improve access to the results obtained for this set of pesticides, an open access web application, called AOP4EUpest, was developed to provide an extensive overview of known MoA for these chemicals (Fig.
1 ). The web application is complementary to existing ones, such as HExpoChem that allows prediction of potential toxic mechanisms of environmental chemicals through protein complexes and phenotypic outcomes enrichment  (Taboureau et al. , 2013)  (http:// www. cbs. dtu. dk/services/HExpoChem-1. 0/), sAOP (stressor-AOP) that represent the full mapping of bioactive compounds from the ToxCast database on the AOP space (Aguayo-Orozco et al. , 2019) (http://saop. cpr. ku. dk/) and ProTox-II which uses structural information of chemicals for toxicity prediction  (Banerjee et al. , 2018)  (http://tox. charite. de/protox_II/).
Materials and methods To create the AOP4EUpest web application, we first improved the recently developed AOP-helpFinder tool that is based on Natural Language Toolkit (NLTK, version 3. 2. 5) (https://www. nltk. org/) and on Dijkstra graph theory  (Carvaillo et al. , 2019). There are two major improvements in the new version. First, whereas the previous version of the tool uses compiled abstracts from specific toxicological databases (e. g. U. S. NIH TOXNET platform), AOP-helpFinder 2 is designed to pre-process and automatically screen scientific abstracts extracted from the PubMed database (https://www. ncbi. nlm. nih. gov/pubmed) to retrieve links between a stressor (e. g. a chemical) and a dictionary of events (e. g. AOPs or other specific events). Thus, this new feature actually increases the scientific knowledge space to explore and to identify additional cooccurrences.
Second, since events from the AOP-Wiki database (MIE/KE/AO) are increasingly precise and complex (i. e. an event can be described by several words), the proposed updated version calculates the two scoring systems (i. e. the position score and the weighted score, see below) for all event types. This is different from the previous version in which the weighted score (wS) was calculated only for MIEs and KEs and the position score (pS) only for AOs. The wS identifies the shortest path between the words of an event in an abstract using the Dijkstra graph theory, meaning that abstracts are considered as acyclic graphs as described before  (Carvaillo et al. , 2019). The pS is used to decipher the position of the event in the abstract, based on both the position of the event term relative to the other words and the total number of words in the abstract  (Carvaillo et al.
, 2019). It is meant to take into consideration whether the AOP event is more likely to be a working hypothesis (at the beginning of an abstract) or a finding/conclusion (at the end). AOP-helpFinder 2 was applied to a list of pesticides prioritized by HBM4EU partners because of the lack of knowledge on their long-term health effects, and their exposure levels in humans (Supplementary Table  S1 ). For each pesticide, an individual screening of the PubMed database that contains 30 072 881 articles (as of August 21, 2019) was performed. All abstracts related to one pesticide were saved in. xml files. A dictionary of events extracted from the AOP-Wiki ('aop_ke_mie_ao. tsv' file, as of August 26, 2019) was developed and used to carry out the abstracts text mining (Supplementary Table  S2 ). This dictionary contains information regarding 165 MIEs, 704 KEs, 125 AOs, 12 MIE/KE (in the AOP-Wiki database an event can be defined as an MIE in one AOP and as a KE in another AOP) and 18 KE/AO (events defined as AO in some AOPs and as KEs in other AOPs) (Supplementary Table  S2 ).
To illustrate our approach, the search for glyphosate allowed for the identification of previously uncharacterized linkages for several events (Fig. 2 ). Such findings are complementary to those of existing databases such as AOP-Wiki and CompTox Dashboard, which do not include glyphosate-AOP links information  (Williams et al. , 2017). The AOP-helpFinder 2 tool explores, in an automatic way, existing published data. This was illustrated by the relevant information that the tool was able to retrieve for a set of highly prioritized pesticides and their linkages to AOP events. The AOP4EUpest web application is an easy and convenient presentation of those linkages and can assist in the identification of pesticides linked to AOPs and in the future, in the development of additional experimental studies.
Study Design and Participants The institutional review board and ethics committee of our institution had approved the current study protocol. All subjects (or legal guardians) signed an informed consent form. This cohort study was performed in accordance with the Code of Medical Ethics of the World Medical Association (Declaration of Helsinki, 2014). We reviewed a prospectively collected data base of 192 patients with bAVMs who underwent endovascular treatment at our institution between January 2008 and July 2019. A total of 57 patients underwent TVE. We assessed demographics, medical history, imaging results, postoperative examinations, and clinical follow-up among all patients. All HC were prospectively evaluated, and the location of the hemorrhage, its temporal relationship with TVE, clinical outcomes, mRS score, and imaging outcomes at discharge and after 6 months were recorded in a quantified fashion in a prospectively maintained data base.
The recorded anatomic data included the largest diameter of the nidus before any intervention, the diameter of the nidus immediately before TVE, the location of the nidus (cortical or deep), and the number of venous collectors. Three senior interventional neuroradiologists retrospectively evaluated whether draining veins were dilated or nondilated. The evaluator were also blinded to the outcome and a vein being dilated when it had at least twice the diameter of the corresponding contralateral vein. Additionally, we classified the nidi of bAVMs into 2 groups: those that were 0-3 cm in diameter and those of. 3 cm in diameter. In addition to adult patients, children and adolescents with bAVMs treated by TVE were included. Patients diagnosed with vein of Galen malformations and dural arteriovenous fistulas were excluded.
An adverse event was defined as a new neurologic deficit that occurred within 6 months of embolization. Angioarchitectural analyses considered only HC unrelated to endovascular access to avoid selection bias. Follow-up angiograms were obtained at 6 months postembolization. Embolization was considered curative when obliteration of the nidus with no evidence of early venous drainage was demonstrated on an angiogram. Follow-up data considered all patients, including those with incomplete endovascular therapy.
Statistical Analysis Statistical analysis was performed using R statistical and computing software, Version 3. 6. 1. (http://www. r-project. org/) and Excel for Mac, 2019 (Microsoft). Descriptive analysis was conducted to characterize the sample. Any missing data were described. Quantitative variables were expressed as the mean 6 SD, and qualitative variables were expressed as frequency and percentage values. The normality of the data distribution was assessed using a Q-Q plot. The Student t test was used to compare continuous variables when normality was determined. In cases of rejection, the Mann-Whitney test was used. The Fisher exact test was used to compare qualitative data and frequency of occurrence. A statistical significance level of 5% was used for all analyses. Multiple linear regression analysis was conducted to identify the independent predictors among the clinical-anatomic-demographic data for the HC.
Jézéquel et al. # the amazon Basin is an unquestionable biodiversity hotspot, containing the highest freshwater biodiversity on earth and facing off a recent increase in anthropogenic threats. The current knowledge on the spatial distribution of the freshwater fish species is greatly deficient in this basin, preventing a comprehensive understanding of this hyper-diverse ecosystem as a whole. Filling this gap was the priority of a transnational collaborative project, i. e. the AmazonFish project -https://www. amazon-fish. com/. Relying on the outputs of this project, we provide the most complete fish species distribution records covering the whole Amazon drainage. The database, including 2,406 validated freshwater native fish species, 232,936 georeferenced records, results from an extensive survey of species distribution including 590 different sources (e.
Climate change will probably exacerbate these threats further amplifying changes in the structure and function of fish communities  11, 12. Our knowledge on fish species occurrence and spatial distribution within the Amazon Basin is far from complete. Numerous new species are described each year  13, 14  and some large areas are still unknown in several portions of the basin  15, 16. This was among the key motivations of the transnational collaborative project AmazonFish (https://www. amazon-fish. com/) that aimed to compile the most complete and up-to-date information currently available on freshwater fish species distribution for the entire Amazon drainage basin and to initiate scientific collecting expeditions in under-sampled areas to fill the gaps. This database is thus the result of mobilizing information available from various sources (published articles, grey literature, field expedition reports, online biodiversity databases and scientific collections from museums and universities worldwide) and field expeditions organized during the project.
This compilation, covering a time span of almost two hundred years (1834-2019), currently comprises 2,406 valid native freshwater fish species recorded from 590 different sources representing more than 235,064 occurrence records (232,936 georeferenced and 2,128 non-georeferenced) and 21,500 sampled localities (hereafter called sampling sites). Two parallel compilation efforts on the distribution of freshwater fish species in the Amazon Basin have been recently released  17, 18. The field guide book from van der Sleen and Albert 17 delivers a general view of the current knowledge of fish ecology and distribution maps at the genus level only. The compilation from Dagosta & De Pinna  18  provides species lists for 30 Amazonian sub-drainages but suffers from a lack of information  19. Here, we complement and refine these previous initiatives by providing species-level distributions on a database format combining available information at both sampling site and sub-drainage grains (144 units).
By compiling the knowledge on the spatial distribution of freshwater fishes and addressing the taxonomic and sampling gaps, the Amazon Fish database should become a valuable and long-lasting source of information for ecological and conservation studies. The database is currently being used to analyse fish diversity patterns at the Amazon Basin scale  19  , to evaluate the potential effect of climate change  20  and fragmentation  21  on this biodiversity and to define diversity hotspots for the whole basin conservation priorities  22. Besides improving our fundamental knowledge of the patterns and processes involved in the generation of Neotropical freshwater fish diversity, the information provided can also help developing regional conservation programs and contributing to largescale transnational ecosystem management initiatives.
Species occurrences are delivered here at two spatial grains, sampling sites (with precise geographic coordinates) and sub-drainage (144 units) grains. The database is organised in two sub-datasets and one shapefile. The first dataset contains the species list by sub-drainage with the taxonomic FishBase reference name (Family, Genus, Referent species valid name and Author), the species status ('native' or 'exotic') and the occurrence species status ('valid' , 'to be verified' , 'marine'; see Technical validation for more details). The second dataset contains the geographic coordinates for the georeferenced records, the information source of each record, and the original name of the species cited in the source ('synonym' , 'typing error'). Finally, the shapefile delineates all the sub-drainages, along with the corresponding geographic information (e.
Methods information sources. The database results from the transnational collaborative project AmazonFish (ERANetLAC/DCC-0210) whose purpose was to identify and compile all known information sources available on freshwater fish species occurrences for the entire Amazon drainage basin. The original project included researchers from (1). All these partners brought into the project, besides their Neotropical fish taxonomic expertise needed to produce a high-quality database, existing fish databases from their own collections and expeditions, and a large networking capacity that was essential for identifying and involving other data providers. In order to build the AmazonFish database, an inventory of the possible data sources was conducted at the beginning of the project in early 2016 and data from a wide range of sources were compiled and standardized in a single dataset.
The information used includes five source types: A. Information extracted from the literature (published articles, books, grey literature) B. Data from online biodiversity databases (i. e. GBIF and others) C. Data from museums and universities collections D. Data held or compiled by the project partners (e. g. country level) E. New data from sampling campaigns organized within the framework of the project An inventory of all the literature sources (published articles, books, technical reports) existent for the Amazon Basin led to more than 800 different documents that were subsequently analysed, from which 459 provided valuable data on fish species distribution, not redundant with any official collection. An important amount of data was extracted from the most used and frequently updated online biodiversity databases (see details in Table  1 ).
These repositories release biological data under a Creative Commons licence in which the user agrees to acknowledge the data sources. Data from museums and universities collections not available through these online facilities were obtained by contacting the curators or researchers in charge and integrating them as official project collaborators (curators and researchers mainly from Brazil, Ecuador and Bolivia). The project partners (Colombia and Peru) compiled data at the country level. For Colombia  23, 24  , the data were previously published through the GBIF network. For Peru, the AmazonFish project has supported the numeric digitalization of the national freshwater fish collections  25, 26  , which is still an ongoing work (51% of the records have been digitalized so far). Finally, supplementary occurrence data were obtained during five sampling campaigns in Brazil, Colombia and Peru and targeting under-sampled areas identified during the project.
species, taxonomy and status. All occurrences not identified to species level were discarded (i. e. occurrences giving only genus names commonly abbreviated to sp. , species affinis commonly abbreviated to: sp. aff. , aff. , or affin. or species confer abbreviated to cf. ). All species scientific names are reported in the database as appearing in each information source and were carefully checked for typing errors and misspellings. Because taxonomy is a 'moving target' , species names were standardized and linked to an internationally accepted standardized name and associated taxonomic information in order to find synonymies and provide accepted names. All species names were first searched in FishBase through the 'rfishbase' package  27  from the R environment  28  allowing to easily obtain the valid species names.
e. non-native species introduced in the Amazon Basin)  5  and the occurrence species status is divided in three criteria: (1) 'valid' (species known to belong to the Amazon Basin); (2) 'to be verified' (species whose presence in the Amazon Basin is not certain because of possible mis-identification or localisation errors); and (3) 'marine' (species whose primary habitat is not freshwater, based on information available in FishBase or Eschmeyer's Catalog of Fishes). At this time, the database contains 2,406 'native' and 'valid' freshwater fish species, 837 'to be verified' species, 105 'marine' , 18 'exotic' and 294 'unknown' species. The species considered as 'native' and 'valid' , i. e. freshwater species belonging to the Amazon Basin, were the only species considered in all species numbers reported below.
sub-drainages delineation. The Amazon Basin was defined here as the area of land where precipitation collects and drains off into a common outlet. This excludes de facto the Tocantins basin and Guiana coastal streams (see Fig. 1 ), but constitutes for freshwater fishes an ideal grain for conducting biogeographical and/or macroecological studies  29. The hydrological sub-drainage units within the Amazon Basin were delineated using the HydroBASINS framework, a subset of the HydroSHEDS database  30. The levels 5 and 6 were combined with a constraint area of >20,000 km 2 , at the exception of sub-drainages located in the river mainstem where delineation was based on the distance between two main tributaries entering the mainstem. This led to obtain a total of 144 sub-drainages covering the entire Amazon system (Fig.
Data Records The database  31  provides a comprehensive overview of the current knowledge of the fish species diversity and distribution in the Amazon Basin, with 21,500 sites (Fig. 1 ), 232,936 georeferenced occurrence records and 2,128 non-georeferenced records from 590 different sources combining literature, scientific collections, sampling campaigns and partner's datasets. Some of the online biodiversity repositories (Table  1 ) showed some redundancies because often referring to the same collections. In this specific case, only one occurrence record was retained. The main sources of the database are online biodiversity databases (56% of the occurrences), followed by locally hosted data from the scientific partners (Peru and Colombia), museums and universities from Brazil, Bolivia and Ecuador (38%), literature data (5% of the records) and data obtained during sampling campaigns by partners from Colombia, Peru and Brazil (1%).
This represents 93 different collections from Scientific Institutions, 31 Partners references, 459 literature references and five AmazonFish expeditions. The database includes information for 56 families, 514 genera and 2,406 native valid freshwater species, virtually half of the circa 4,760 total number of species known for the whole Neotropical biogeographic region  5, 6. Among these 2,406 species, 1,402 are found exclusively in the Amazon Basin (i. e. species appearing nowhere else on Earth; Amazonian endemic species) based on the global species distribution provided by Tedesco et al. 5. Online Biodiversity Repository Sources with the complete name, the number of occurrences and institutions, the last consulted date and the internet link.
Biodiversity The lowland Amazon and its two main tributaries, the Negro and Madeira Rivers regroup the highest number of sites, occurrences and the highest diversity (Table  2 ), whereas less information is available for some small tributaries. At the sub-drainage grain, the density of sites presents an important spatial variability (Online-only Table  1  and Fig. 2 ). For instance, the Curuçá sub-drainage belonging to the Javari River, currently lacks information about its ichthyofauna. The 'updates and limitations' section below presents a more detailed overview of the spatial data gaps. The whole dataset is organised in three sub-sets  31  : a table of the species list by sub-drainage ('GeneralDistribution'), a table of occurrence records with sources ('CompleteDatabase'), and a shapefile of the 144 sub-drainages ('SubDrainageShapefile').
The first sub-set ('GeneralDistribution') contains the species list by sub-drainage with the taxonomic reference name (Family, Genus, Referent species valid scientific name and Author), the species status ('native' or 'exotic') and the occurrence species status ('valid' , 'to be verified' , 'marine'). The corresponding table has nine columns (see Table  3 ). The second sub-set ('CompleteDatabase') provides the geographic coordinates for the georeferenced sampling sites and the information source of each record. It is complemented with the original name of the species cited in the source ('synonym' or 'typing error') and those species with status 'unknown name at present'. The detailed sources contain the source type of the data ('Literature' , 'Online Biodiversity Database' , 'Partners Datasets' and ' AmazonFish Expedition'), the Biodiversity Repository source for the Online Biodiversity Database, the Scientific Institution Code and complete name, the GBIF Citation and DOI, the complete literature reference and the citation reference of the Partner dataset.
Finally, the non-georeferenced occurrences are separated in three categories, 'sub-drainage information' (species occurrence information at the sub-drainage grain), 'approximated coordinates' (species occurrence information at river or reach scales) and 'geographic error' (the geographical coordinates of a site do not correspond to the geographical location given in the source). The corresponding table has 22 columns (see Table  3 ). The third sub-set ('SubDrainageShapefile'), corresponds to shapefile delineating all the sub-drainages and their corresponding geographic information and is organized in eight columns: (1) the major Amazon tributary name (MTRIB_NM), (2) the unique major Amazon tributary code (MTRIB_CD), (3) the unique sub-drainage name (SBD_NM), (4) the unique sub-drainage code (SBD_CD), (5) the surface area of the sub-drainage (AREA, in km 2 ), (6) the main country where it belongs (COUNTRY), (7,8) the centroid longitude and latitude coordinates of the sub-drainage (CENT_X and CENT_Y).
The two table sub-sets ('GeneralDistribution' and 'CompleteDatabase') are in CSV format (columns separated by commas) and the shapefile sub-set ('SubDrainageShapefile') in ArcGis SHP format  31. Both formats can be linked to the species occurrence table using the unique sub-drainage code or name to visualize and analyse species distribution using any adapted software (e. g. R or QGIS, http://qgis. osgeo. org). The sampling coordinates and shapefile are in the World Geodetic System 1984 (WGS84) datum and geographic coordinate system. The files of the database are in 'CSV' format (UTF-8 encoding, comma separator) and can be uploaded by most statistical software, spreadsheets or any other database management systems. The current version of the database Fig. 1  (a) Distribution of sampling sites recorded in the AmazonFish database and (b) delimitation and codes of the 144 sub-drainages units (see corresponding names in Online-only Table  1 ), based on a modified version of HydroBASINS (see methods).