dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this work, we have proposed a fully automatized approach to perform an instance matching task between two Knowledge Bases sharing their T-Boxes. This approach uses semantics at its disposal, but also uses statistics about roles and role-object pairs according to the most specific common concept between the compared instances. The results show that our approach is a promising way towards better interlinking. The recall is good, which means that our approach works well to find links. Nevertheless, there are some improvements that can be made on the algorithm to better take into consideration the structure of the datasets, as discussed in the previous section. Especially to address our false positive detection that can clearly do better. Thus, a first step could be to test other ways to aggregate the different weights.
RNA gel blot analysis RNA samples isolated from agroinfiltrated leaves at 3. d. p. i were separated in formaldehyde-MAE containing denaturing agarose gel and blotted to nylon membrane (Roche). Hybridization and detection were conducted according to the "North2South™ Complete Biotin Random Prime Labeling and Detection Kit" (Thermofisher) instructions. We have used ChemiDoc XRS + imaging system for detection and ImageLab 5. 0 software  (Bio-Rad, 1708265)  to analyse the mRNA blot.
Protein-immunoprecipitation (IP) and Western blot Protein extraction and IP was carried out as described by Baumberger and Baulcombe  (Baumberger and Baulcombe 2005 , except G-25 separation was omitted) at 3 d. p. i. ANTI-HA AFFINITY MATRIX (Roche) and ANTI-GFP-TRAP®_A BEADS (Chromoteck) were used for IPs. Samples were separated in SDS-PAGE, blotted onto AMER-SHAM PROTRAN MEMBRANE (Sigma) and hybridized with monoclonal ANTI-HA-PEROXIDASE (Roche), rabbit polyclonal ANTI-GFP (Thermofisher) and ANTI-FLAG (Sigma) antibodies. Chemiluminescent protein detections were conducted according to the instructions (ECL WEST-ERN BLOTTING SUBSTRATE (Promega) for HA and CDPstar (Sigma) for GFP and Flag). Western blots were scanned with ChemiDoc XRS + System and analyzed with ImageLab 5. 0 software (Bio-Rad).
Acknowledgements The authors are grateful to  Daniel Silhavy  for sharing the Bin61S-and TRV-VIGS vector system and for sending us the  DCP2 ,  CAF1 ,  PDS-SKI2  ( TRV-P-SKI2 ) vectors prior to publication. apum9-1 line were kindly provided by  Wim J. J. Soppe  and  Yong Xiang. We also wish to thank  Elise Réthoré  for helping with heat shock experiments.
Bioinformatic analysis Raw mass spectrometric data was analyzed in the MaxQuant environment, versions 1. 5. 5. 1, and employing Andromeda for database search with label-free quantification (LFQ), match between runs and the iBAQ algorithm enabled  [54]. The MS/MS spectra were matched against the UniProt Reference proteome (Proteome ID UP000002311) of S. cerevisiae (strain ATCC 204508 / S288c) (release 2018_04; http://www. uniprot. org) and 270 frequently observed contaminants as well as reversed sequences of all entries. The following settings were applied: spectra were searched with a mass tolerance of 7 ppm (MS) and 0. 5 Th (MS/MS). Enzyme specificity was set to trypsin/P, and the search included cysteine carbamidomethylation as a fixed modification and oxidation of methionine, acetylation (protein N-term) and/or phosphorylation of Ser, Thr, Tyr residue (STY) as variable modifications.
ACKNOWLEDGMENTS We are grateful to  E. Galati  for initial experiments on the slippage defects of glc7-10 mutant cells; to  S. Ibanes  for assistance with mass spec experiments; to A. Musacchio for structural analysis of Mad3-S268; to  K. Hardwick ,  A. Hoyt ,  J. Kilmartin ,  K. Nasmyth ,  M. Stark ,  W. Zachariae  for sharing reagents; to  V. Georget  for invaluable help with live cell imaging; to members of  S. Piatti's lab  for useful discussions. We acknowledge the imaging core facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future"). We acknowledge the  Functional Proteomics Platform of Biocampus Montpellier  where mass spectrometry experiments were carried out. This work has been supported by the  Fondation pour la Recherche Médicale  ( DEQ20150331740  to S.
Horses are the most common grave goods found in Icelandic Viking Age graves. Horse skeletons have previously been sexed based on pelvis shape and the presence of canine teeth in male horses over 4-5 years of age. Morphological data has shown that all horses from Icelandic burials that were amenable to sexing were male. Yet the use of morphological methods to determine sex is problematic since they rely on finding a well-preserved pelvis and/or robust canine teeth. Furthermore, quantitative data underlying the features of the horse pelvis used for sexing is lacking and canine teeth have been reported to occur in mares. In this study we build upon and extend recently developed methodologies to make use of shotgun sequencing of ancient DNA (aDNA) for molecular sexing of Viking Age horse remains.
19-23, 52-54)  and there is no collection of quantitative data regarding their size and morphology. Based on the above observations, the use of either the pelvis or the presence of canines for sexing horses can in itself be problematic. Yet poor preservation further limits the use of these features in archaeological specimens. First, it is rare to find horse pelvis bones that are complete enough for sexing (e. g. Bökönyi, 1974, pp. 269-270; Johnstone, 2004, p. 111; Lam et al. , 1999, p. 346; Leifsson, 2018, p. 242; Lyublyanovics, 2006, p. 242). Second, the occurrence of loose canines complicates the determination of which specimen they belong to when multiple individuals are present. And finally it is possible that not all bones and teeth were collected -a particular problem for material from old excavations-and the absence of canines may therefore reflect a collection bias rather than a sex bias.
Library builds, sequencing and read processing Blunt-end Illumina libraries  (Meyer and Kircher, 2010)  were built following  Schroeder et al. (2015)  as described in  Star al. (2017). Sequencing was carried out on an Illumina HiSeq 2500 at the Norwegian Sequencing Centre. The read data were processed with the PAL-EOMIX pipeline v. 1. 2. 4  (Schubert et al. , 2014)  as per  Star et al. (2017)  which utilises ADAPTERREMOVAL v. 2. 1. 7 to remove adapters, collapse and trim reads, with collapsed reads < 25bp in length discarded  (Schubert et al. , 2016). The backtrack algorithm implemented in BWA v. 0. 5. 10 was used to align reads to the EquCab 2. 0 genome  (Wade et al. , 2009)  and the Y-chromosome  (Wallner et al. , 2017)  with seeding disabled in PALEOMIX. Post-mortem DNA damage patterns were investigated using mapDamage v.
2 ). Although sample VHR098 had just 1010 mapped reads, this sample falls outside the observed X:A ratio range of female specimens in the dataset (X:A ratio = 0. 052, Fig. 2 , Table  1 ), hence this sample was identified as a male. The range of probability distributions of female and male Y:A coverage ratios fully differentiate the sexes with a minimum of 18 000 reads (Fig. 2 ). We were unable to confidently determine the sex of VHR098 using the Y:A method alone, but samples VHR095 and VHR096 with less than 5000 reads were identified as male, with ratios falling outside the range determined for the female horses in the dataset (Fig. 2 ). No indications of chromosomal abnormalities were observed in any of the horse specimens. Finally, we confirmed that males with low endogenous DNA (< 2. 4%) and number of reads (< 20 000) were not misclassified due to cross-sample contamination (Supplementary material note 1.
, 2017; Skoglund et al. , 2013) , we here extend these methods to improve their ease of application and provide statistical confidence by combining the X:A and Y:A ratio methods into a single, straightforward resampling approach that can be readily applied to any species with heterogametic sex determination. This includes many economically important species such as sheep, goats, cattle and dogs whose bones are common in archaeological faunal assemblages but are often hard to sex morphologically. Our data identified (in concordance with previous morphological findings) that there is an over-representation of male horses in Icelandic Viking Age burials (late 9th to early 11th century), with a male to female ratio of 18:1 in our dataset. A bias towards the use of male horses in burials has been noted in other time periods and locations from the earliest horse burials at Berel' 2300 years BP  (Librado et al.
An underlying sex ratio bias? Horse bones are relatively rare in non-burial contexts in Iceland and Northern Europe and often consist of single elements, which cannot be morphologically sexed (e. g. Dobney et al. , 1996, p. 46; Johnstone, 2004; Leifsson, 2018, p. 263; Levine, 1990; McCormick and Murray, 2007, p. 96; McGovern, 2009, p. 220; O'Connor, 1994, p. 31; Rackham, 1995; Sundkvist, 2004, p. 243). Sexing data has not previously been published for any Icelandic Viking Age horses from non-burial contexts. Here we present the first sexing of such horses using aDNA and identified all three specimens as female. These three horses originated from the Granastaðir farm site and Leynir cave. The horse bones from Granastaðir were interpreted as having been butchered for food  (Amorosi and McGovern, 1995)  and the Leynir cave individual is believed to have been carried into the cave as a source of food (pers.
Geldings or stallions? Detecting castration of animals archaeologically is problematic (Binois-Roman, 2016, p. 136) and we are unable to determine whether the male horses found in Viking Age burials were castrated (geldings) or uncastrated (stallions). The castration of horses adds to the complexity of morphological sexing, with the timing and method of castration influencing the effects on the skeleton. Specifically, castration can affect bone fusion times, skeletal proportions and the robustness of bones and sexual characteristics (Binois-Roman, 2016, p. 136), though very little data are available regarding these effects in horses specifically  (Johnstone, 2004, p. 112). Castration is not known to affect the presence and development of canine teeth  (Cornevin and Lesbre, 1894, p. 129; Vollmerhaus et al.
Acknowledgements The National Museum of Iceland gave access to material for sampling and we acknowledge  Ármann Guðmundsson  for his assistance there. We thank  M Skage ,  S Kollias ,  A Tooming-Klunderud  and  H Rydbeck  from the  Norwegian Sequencing Centre , and  Giada Ferrari  for translation assistance. The analyses were performed on the Abel Cluster, owned by the  University of Oslo (UiO)  and  Uninett/Sigma2 , and operated by the  Department for Research Computing at USIT , the  UiO IT-department. This work was funded by the  Research Council of Norway  project  230821/F20  and the  Icelandic Research Fund  project  162783051. LO received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  681605 ).
Invasive alien species are a major threat to native insular species. Eradicating invasive mammals from islands is a feasible and proven approach to prevent biodiversity loss. We developed a conceptual framework to identify globally important islands for invasive mammal eradications to prevent imminent extinctions of highly threatened species using biogeographic and technical factors, plus a novel approach to consider socio-political feasibility. We applied this framework using a comprehensive dataset describing the distribution of 1,184 highly threatened native vertebrate species (i. e. those listed as Critically Endangered or Endangered on the IUCN Red List) and 184 non-native mammals on 1,279 islands worldwide. Based on extinction risk, irreplaceability, severity of impact from invasive species, and technical feasibility of eradication, we identified and ranked 292 of the most important islands where eradicating invasive mammals would benefit highly threatened vertebrates.
5-halt the loss of biodiversity, and 15. 8-prevent and reduce the impact of invasive species on land and water ecosystems). Formal funding mandates by the Global Environment Facility  [14] , and commitments by nations striving to meet global targets (e. g. [15] [16] [17] ) further Competing interests: Jakob Fric is employed at, holds shares at and is a member of the board at the Nature Conservation Consultancy Ltd. The data provided in the present manuscript has been collected though past and ongoing LIFE-Nature projects cofunded by the European Commission through LIFE financial instrument. Jakob Fric is a member at the Hellenic Ornithological Society (BirdLife Greece), Hellenic Zoological Society and ANIMA -Hellenic Wildlife Care Association (Greece). Paolo Sposimo is employed by NEMO Srl.
We combined conservation opportunity and feasibility into a unified framework for identifying globally important islands where eradication of invasive mammals can benefit globally threatened birds, reptiles, mammals and amphibians. To achieve this, we built upon a recent global database on island characteristics and the distribution of threatened vertebrates and invasive species  [22, 23]  allowing comprehensive analyses to identify islands where vertebrate extinctions can be prevented by eradicating invasive mammals. We then 1) determined globally important islands where the eradication of invasive mammals can benefit native amphibian, bird, reptile and mammal species listed as Critically Endangered or Endangered on the IUCN Red List (hereafter, "highly threatened vertebrates"), 2) identified which islands meet current technical feasibility criteria, and 3) evaluated if eradication planning or operations are socio-politically feasible to initiate by 2020, 2030 or not in the foreseeable future.
General data collection Data describing the distribution of highly threatened vertebrates breeding on islands, invasive mammals on these islands, and impacts of invasive mammals to highly threatened vertebrates were collated from the International Union for the Conservation of Nature (IUCN) Red List, BirdLife International's species factsheets (www. birdlife. org), the Global Invasive Species Database (www. iucngisd. org), and the Threatened Island Biodiversity database  [22, 23]. We summarize the data collected and, include definitions of the parameters used. We included highly threatened vertebrates based on conservation status on the IUCN Red List (2013. 1). Marine mammals and sea turtles were excluded. We included species with breeding distributions classified as either insular or continental and insular, and where breeding status was defined as confirmed, probable or potential (based on definitions in  [23] ).
Note that we purposefully masked islands that were considered sensitive locations as identified by experts, or if they were <100 ha and contained a reptile species, in order to limit the distribution of information that could be used for illegal wildlife trafficking. The island breeding distributions of highly threatened vertebrates and invasive mammals are tabulated in the Threatened Island Biodiversity database (available online at http://tib. islandconservation. org). We defined invasive mammals as terrestrial vertebrate species whose introduction or spread by direct human action outside their natural distribution has been documented as negatively impacting native biodiversity  [25]. To identify invasive mammals for our study, we first identified non-native mammals co-occurring with highly threatened vertebrates on the same island.
We considered each non-native mammal and highly threatened vertebrate species cooccurrence as an interaction and classified the potential negative impact of that interaction as confirmed, suspected or none (see definitions in Table A in S1 File). For highly threatened birds, impact was based on the timing, scope and severity of invasive species threats  [26]. For highly threatened reptiles, amphibians and mammals, impacts were derived from the literature, an expert review and additional data from a UK Overseas Territories assessment  [24]. For invasive mammal presence on islands, we included records in our analyses if presence was confirmed or suspected (based on definitions in  [23] ), including if they were subject to ongoing eradication operations because the removal of these populations had not yet been assured at the time of analysis.
If no impact data were available for a species, we assumed that maximum impacts recorded for a highly threatened vertebrate by an invasive mammal would be analogous to other highly threatened vertebrates in the same taxonomic family. Despite this, we acknowledge that there may be impacts not accounted for in our study, because the effects of some invasive mammals, such as invasive mice (Mus spp. )  [31] , are less understood. To assess the potential impact of rodents being present on islands where status is unknown, we re-ran analyses assuming they were present (S1 File). Step 1b: Potential Conservation Risk (PCR). Having determined the greatest potential conservation gains from eradication (CCR), we reduced this list of islands to include only those where eradication was technically feasible (Potential Conservation Risk or PCR).
To inform PCR, we first assessed eradication feasibility for each invasive mammal species on each island, using thresholds for human population size and island area for each invasive mammal type (S1 File and Table B in S1 File). These two factors significantly limit the feasibility for invasive mammal eradications and are useful for large geographic scale comparisons, such as in this study  [32, 33]. Area and human population thresholds were extracted from the Database of Islands and Invasive Species Eradications (accessed in March 2015 and October 2016)  [11]  for invasive mammals with demonstrated precedent for successful eradication, and for eradications currently in progress or planned, and reviewed by expert practitioners (S1 File). If an invasive mammal was deemed feasible to eradicate from an island, the severity of negative impact score (Z) was set to zero because that negative interaction would no longer occur on the island once the invasive mammal was eradicated.
Step 2: Socio-political feasibility. For those islands deemed technically feasible in Step 1, we conducted a systematic assessment of the socio-political feasibility of invasive mammal eradication. As data are not available to quantify socio-political feasibility world-wide, we chose to use an expert-elicitation method to assess feasibility based upon practitioners' knowledge. Between 2015-2017, we sent data to 116 conservation practitioners with expertise within the region of interest. Each expert was asked to assess feasibility to initiate an invasive mammal eradication project within their area of expertise by 2020, 2030 or not in the foreseeable future. We considered the eradication of all invasive mammal populations concurrently within the same timeframe. Each expert received a questionnaire, which addressed: 1) the history of previous invasive vertebrate eradications on islands and capacity for invasive vertebrate eradications in the country or territory, 2) political acceptability at a national or territory level, 3) influence of permanent human habitation on any proposed invasive mammal eradication, and 4) social acceptability at the local scale (S1 File).
Those identified as socio-politically feasible to initiate by 2020 or 2030 may not be exactly initiated by these timelines if resources and political will prove lacking, but we do expect that, on average, eradications identified in the 2020 timeframe will be more feasible sooner rather than those identified in the 2030 timeframe. To provide context for the socio-political assessment and provide a metric for the expertise applied to the analysis, we collected data on the experts contributing to this assessment. We asked experts to tell us how many years they had spent actively working (e. g. research, planning, implementing) in conservation 1) in any capacity; and 2) dealing with the threat of invasive alien species.
Acknowledgments We thank  April Burt ,  Araceli Samaniego ,  Clare Stringer ,  Eric Delcroix ,  Frauke Fleischer-Dogley ,  Gregg Howald ,  Jean-Francois Butaud ,  Joel Miles ,  Jose Vargas ,  Julien Baudat-Franceschi ,  Ivan Ineich ,  Keith Broome ,  Mark O'Brien ,  Nicola Baccetti ,  Paul Donald ,  Pete Wood ,  Ray Nias ,  Thomas Ghestemme ,  David Wege ,  Veronica Anadon ,  Mike Crosby ,  Simba Chan ,  Ivan Ramirez ,  Roger Safford  and others for contributing to socio-political surveys. We thank contributors to  BirdLife International 's IUCN Red List assessments, plus  UC Santa Cruz undergraduate volunteers, Coastal Conservation Action Lab members , and hundreds of expert contributors to the  Threatened Island Biodiversity Database. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the  U.
The underlying data from the Threatened Island Biodiversity database used in the manuscript was collected through research funded by the  David and Lucile Packard Foundation  (https://www. packard. org); National Fish and Wildlife Foundation  (http://www. nfwf. org/);and, Island Conservation (https://www. islandconservation. org/). Nick Holmes  and  Dena Spatz  received salaries from these same sources. These funding sources financially supported the data collection and writing of this report but did not have any other involvement in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Nature Conservation Consultants Ltd. provided support in the form of salaries for author  Jakob Fric , and contributed data collected through past and ongoing European Union's  LIFE-Nature  projects, co-funded by the  European Commission , but did not have any additional role in the study design, analysis, decision to publish, or preparation of the manuscript.
Bait design We used a targeted sequencing approach to recover markers for phylogenetic inference and obtain an estimate of conotoxin gene diversity from Conidae species. For the phylogenetic markers, we identified loci using a previous Conidae targeted sequencing dataset  (Phuong & Mahardika 2017 ) and the Conidae transcriptome data from  (Phuong et al. 2016 ). In the Conidae targeted sequencing dataset, the authors generated a phylogeny using 5883 loci across 32 species  (Phuong & Mahardika 2017). For our sequencing experiment, we only retained loci that were >180bp and were present in at least 26 out of 32 taxa with at least 10X coverage. We chose to only include longer loci to increase confidence in identifying orthologous fragments in other Conidae species. To identify additional phylogenetic markers from the transcriptome data.
2. 1. 1 (http://broadinstitute. github. io/picard) (4) fixed assembly errors by calling single nucleotide polymorphisms (SNPs) using samtools v1. 3 and bcftools v1. 3  (Li et al. 2009)  (5) aligned sequences per locus using mafft v7. 222  (Katoh et al. 2005)  (6) calculated uncorrected pairwise distances within each locus for all possible pairwise comparisons (7) removed sequences if the uncorrected pairwise distance was greater than the 90 th percentile for those pair of species (8) denoted exon boundaries by comparing the transcriptome sequences to the Lottia gigantea genome reference  (Simakov et al. 2013) , retaining exons >180bp For all retained phylogenetic markers, we also performed the following: (1) we generated an ancestral sequence using FastML v3. 1  (Ashkenazy et al. 2012 ) between a Californiconus californicus sequence and another Conidae sequence that had the highest amount of overlap with the C.