dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Although in line with results from other ice sheet models participating in ISMIP6-Antarctica, the contribution of the Antarctic ice sheet to global sea level rise simulated by GRISLI is relatively limited. Amongst the different experiments, the largest contribution by 2100 is 150 mmSLE (NorESM1-M PIGL with a high oceanic sensitivity) while most experiments produce a contribution no greater than 80 mmSLE. If a relatively moderate Antarctic ice sheet contribution to future sea level rise by 2100 has been suggested in other studies  (Bamber et al. , 2019; Edwards et al. , 2019, e. g. ) , it seems nonetheless in contradiction with the acceleration in mass loss reported by modern observational techniques  Rignot et al. (2019). One reason for this disagreement is that we use a data assimilation procedure that produce an initial condition in quasi-equilibrium with present-day forcing.
Increased precipitation simulated by most of the climate models in the future tend to increase the grounded ice volume, partly mitigating the effect of ice shelf reduction. By the end of the century, we simulate the largest changes in ice thickness and ice dynamics in the Filchner-Ronne and Ross basin with only moderate changes elsewhere. The geographical pattern of these changes remains mostly consistent amongst the different climate forcings. The CMIP6 climate models do not change drastically the simulated ice sheet in the future with respect to the CMIP5 models. Under low greenhouse gas emission scenario, the Antarctic ice sheet present much less ice volume changes suggesting that the ice sheet volume could be mitigated with a reduction in greenhouse gas emission. The oceanic forcing is a major source of uncertainty since the use of the melt model calibrated against the Pine-Island glacier data instead of the standard calibration produces a much faster ice shelf retreat and, as a result, a larger ice sheet volume contribution to sea level rise in the future.
https://doi. org/10. 5194/tc-2020-140 Preprint. Discussion started: 2 June 2020 c Author(s) 2020. CC BY 4. 0 License. melt rate response for a given change in thermal forcing than the reference parametrisation calibrated for the whole dataset. The experiments that use this calibration are labelled PIGL in the following. As for the standard calibration, three temperature sensitivities are provided, low, medium and high, for the PIGL calibration.
identifier 10. 5281/zenodo. 3819782. The outputs in the zenodo repository are the standard GRISLI outputs on the native 16 km https://doi. org/10. 5194/tc-2020-140 Preprint. Discussion started: 2 June 2020 c Author(s) 2020. CC BY 4. 0 License. grid and, as a result, they may slightly differ from the post-processed outputs available on the official CMIP6 archive on the Earth System Grid Federation (ESGF). In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6, the participating modelling groups, and the ESGF centres (see details on the CMIP Panel website at http://www. wcrp-climate. org/index. php/wgcm-cmip/about-cmip). The forcing datasets are available through the ISMIP6 wiki and are also made publicly available via https://doi.
org/xxx. CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6. This is ISMIP6 contribution No X. 14 https://doi. org/10. 5194/tc-2020-140 Preprint. Discussion started: 2 June 2020 c Author(s) 2020. CC BY 4. 0 License.
Acknowledgements. We thank the  Climate and Cryosphere (CliC)  effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the CMIP data and providing access, the  University at Buffalo  for ISMIP6 data distribution and upload, and the multiple funding agencies who support  CMIP5  and.
PPR is applied on the disambiguation graph and then ranks each node according to its contribution to the coherence of the graph. The model was evaluated on the dataset AIDA and achieved a disambiguation accuracy of 91. 7%. Guo et al. [10]  described a NEL method for Wikipedia entities that determines the local similarity between textual mentions and entities (using lexical and statistical features) and a disambiguation graph that maximises global coherence between the candidates for the entities in a document. The algorithm then performs Random Walks in the graph to derive the semantic similarity between every pair of entities. These two approaches share some similarities with our method, in the sense that both are graph-based models and both apply the Random Walks algorithm over a disambiguation graph to maximise the global coherence between entities in a given document.
75 and 84. 65 and in the ShARe/CLEF eHealth Challenge corpus and the NCBI Disease corpus. More recently, Ji et al. [20]  fine-tuned the BERT model and two of its variants, ClinicalBert and BioBert, to the NEL task, evaluated them in the ShARe/CLEF, NCBI Disease and TAC2017ADR (drug labels) datasets and obtained an accuracy of 91. 10%, 89. 06% and 93. 22%, respectively. J-REED  [21]  is a model able to perform both NEL and RE. However, this approach performs both tasks sequentially and does not improve NEL with RE, which is the main goal of the present work. To the best of our knowledge, our method is the first attempt to use the RE output to improve the performance of graph-based NEL methods.
In order to add a layer of differentiation for the nodes in the disambiguation graph, the PageRank of each node n in relation to a source node s is multiplied by the information content (IC) of the node n: The IC of a concept is a measure of its "rareness": rare concepts will have higher information content. In the present work, we use the extrinsic IC definition, as described by Couto et Lamurias  [22] , in which the IC of a concept is associated with the frequency of its instances in an external dataset (for example a corpus). Pershina et al. [11]  and Guo et al. [10]  do not include the IC in their approach. Instead, Pershina et al. [11]  use the Freebase (3) Coherence(n) = s∈G coherence s (n) (4) Coherence s (n) = PPR(s → n) • IC(n) Fig. 2  Relation between terms in the text. Example of a relation between the entity mentions "alcohols" and "methylsalicylate" that is described in an article, but it is not expressed in the ChEBI ontology structure.
Models To determine the impact of the relation extraction in the NEL performance, we evaluated the following models described below in several datasets: • "String matching": first baseline approach. For each entity to disambiguate, this model selects the candidate with highest lexical similarity (lowest edit distance) through string matching. • "PPR-IC": second baseline, corresponds roughly to PPR-SSM  [5]  without using semantic similarity measures. Consists in the application of the PPR algorithm with the inclusion of the IC for each node. The edges in the disambiguation graph are exclusively based on KB relations between concepts (link mode KBlink). The scoring function is Eq. 4. • "REEL(Corpus)": the application of the PPR-IC model, but using only relations described by the text to build the edge structure of the disambiguation graph (link mode Corpus-link).
Datasets The "Colorado Richly Annotated Full-Text" (CRAFT) corpus is a set of 67 full-text biomedical articles from PubMed Central Open Access subset  [23]. This gold standard contains, among others, 4548 manual annotations of ChEBI entities. The set of the corpus with ChEBI annotations will be further designed as "CRAFT-ChEBI". In this work, we used the version 3. 0 of this corpus  [24]. To demonstrate that REEL can easily be adapted to include relations extracted by different tools, we evaluated the performance of the model on the BC5CDR corpus  [25]  openly available  [26]. This gold standard was developed for the disease named entity recognition (DNER) task and the chemical-induced disease (CID) RE task in the for BioCreative V. This corpus consists in 1500 PubMed abstracts annotated with 4409 chemicals, 5818 diseases and 3116 chemical-disease interactions.
Ontologies The first ontology we used was the ChEBI ontology, acronym for "Chemical Entities of Biological Interest", which represents low-molecular weight chemical entities with biological relevance for living organisms  [27]. As of 1 September, 2019 this repository contained 56090 annotated entries  [28]. In the experiments described in this work we used the data from the release 179  [29]. The second ontology was the MEDIC Disease vocabulary from the Comparative Toxicogenomics Database (CTD), which is an hierarchical vocabulary that represents descriptors from the "Diseases" category of MeSH controlled vocabulary and genetic disorders from Online Mendelian Inheritance in Man (OMIM) repository  [30]. As of May, 2020, this vocabulary contained 7246 entries representing distinct diseases entities  [31]  and in the experiments of this work we used the data from the referred month release  [32].
Evaluation metrics In each document of the corpus, repeated instances of an entity mention with the same surface form count as a unique entity. True positives (tp) refer to the number of entities correctly disambiguated, false positives (fp) to the number of entities wrongly disambiguated and false negatives (fn) to the number of entities that the model does not disambiguate. The performance of each model was evaluated in each dataset through the determination of the precision, recall and micro-averaged F1-score: (5) Precision = tp tp + fp • 100.
Pre-processing The module dedicated to the pre-processing of corpus documents was implemented in Python 3. 6. 8. This module generates the candidates lists through the Fuzzy-Wuzzy Python library, i. e. , for each entity mention the module obtains the first ten ontology concepts with more lexical similarity with the mention (and the synonyms for the candidates) and discards the candidates without a valid ontology identifier. The module also converts the ontologies into graph objects with the Networkx Python Library, which further allows the determination of the ontology relations between concepts. Alternatively, this module can also include information about relations between entities described in text, either from corpus annotations or from the output of any RE tool. In the end, the module creates a candidates file for each original document in the corpus, files that contain all the information necessary to build the disambiguation graph (nodes and edges).
Results and discussion The evaluation results for the models in the different datasets are available in the Table  1. In the CRAFT-ChEBI dataset, the two REEL models achieved the same performance, a F1-Score of 85. 8%, which is an improvement of 2. 5% and 7. 9% comparing with the "PPR-IC" and "String matching" baseline approaches. The precision achieved was 91. 3%, an increase of 4. 2% and 13. 5% comparing with the "PPR-IC" and "String matching" models. In the BC5CDR-Diseases dataset, the model "REEL(Corpus)" achieved the highest F1-Score, 80. 9%, which represents an improvement of 2. 0 % and 2. 4% comparing with the "PPR-IC" and "String matching" models. The precision achieved was 86. 9%, an increase of 3. 5% and 4. 2% comparing with the "PPR-IC" and "String matching" models. In the BC5CDR-Chemicals dataset, the models "REEL(Corpus)" and "REEL (KB+Corpus)" obtained the highest F1-Score, 90.
3%, but the increase from the baseline approaches was lower: 0. 2% and 1. 1% comparing with the "PPR-IC" and "String matching" models. The precision increased by 0. 3% and 2. 1% from the precision of the "PPR-IC" and "String matching" models. The two REEL models ["REEL(Corpus)" and "REEL(KB+Corpus)"] consistently achieved the best F1-Score in all datasets and respective sets. The higher F1-Score comparing with the baseline approaches is directly related with increases in the precision, as the recall did not substantially differ across models. These results demonstrate that the initial hypothesis of improving the precision of graph-based NEL models through RE is true. The use of a RE tool (BO-LSTM) and the inclusion of chemical-disease interactions of the BC5CDR corpus overcame the lack of domain knowledge in the KB and originated denser disambiguation graphs, which by its turn, improved the performance of the PPR algorithm.
The results obtained by REEL are explained by the fact that there is semantic information encoded in text and not expressed in the ontologies structure. For example, in the following sentence of document 14737183 of the CRAFT-ChEBI dataset "(. ) skin from at/at mice reveal an abrupt dorsoventral transition of DOPA staining, which probably reflects the additive effects of reduced melanin content" there are two entities mentions: "DOPA", annotated with the identifier "CHEBI:49168", and "melanin", annotated with the identifier "CHEBI:25179". In the ChEBI structure these entities are not linked, but BO-LSTM can infer that the content of "melanin"' affects the "DOPA staining" through contextual features. Another example, more complete, is shown in Fig. 3. In the document 15630473 of the CRAFT-ChEBI dataset there were, among others, the entity mentions "Cl", "sodium vanate" and "sodium deoxycholate".
The NEL performance of REEL is indirectly related with the performance of the RE tool that is used. The RE performance of BO-LSTM is lower than the inclusion of the gold standard annotations present in the BC5CDR corpus: BO-LSTM is not able to extract all the relations between entities present in the CRAFT-ChEBI dataset, contrarily to what happens in the BC5CDR corpus, where all the relations are known. The use of a RE tool is a more realistic scenario than the inclusion of gold standard annotations, because not always these are available, so we measured the NEL performance by REEL in these two different scenarios. According to Table  1 , We can conclude that the NEL performance increases comparing with baseline approaches in these.
Model In the ChEBI ancestors channel of BO-LSTM, the model links each entity mention with a ChEBI identifier through string matching, which limits the performance of the tool when extracting the relations in text. So, the performance of REEL benefits with the BO-LSTM output, but could by its turn be used to improve BO-LSTM performance, more concretely by replacing the string matching method and improving its disambiguation component. The REEL framework can be potentially adapted to any domain, as long as there is an ontology or other structured KB available. This feature is specially relevant for the biomedical and life sciences domains, where there is a lack of text mining tools, but there are many digital libraries with scientific information available. REEL could be used in any gold standard dataset as long it contains annotations of biomedical ontology concepts and in any biomedical text where the entities are already recognized by a Named Entity Recognition tool.
Error analysis Despite the positive results achieved by REEL, there were some errors that prevented an even higher performance. One type of error is associated with the presence of composite mentions in the BC5CDR-Diseases dataset. For example, the disease mention "detrimental effect on memory and cognition" is annotated with two different gold labels: "detrimental effect on memory" (D008569) and "detrimental effect on cognition" (D003072). REEL is not adapted to recognise and deal with this type of entity mentions because only one candidate is selected per entity. The second type of error is related with the candidate generation step, as many entity mentions did not have the correct disambiguation in the respective candidates list, which impacted mainly the recall of the model. The string matching technique to generate candidates is useful to restrict the field of possible ontology candidates but sometimes leaves out correct candidates with little lexical overlap with the entity mention.
Conclusion We developed REEL, which leverages extracted relations described in the text to build dense disambiguation graphs and then applies the PPR algorithm for candidate ranking and disambiguation. The framework was evaluated on three different gold standards, the CRAFT-ChEBI, the BC5CDR-Diseases and the BC5CDR-Chemicals datasets and achieved a F1-Score of 85. 8%, 80. 9% and 90. 3%, respectively, which represents an improvement comparing with two baseline approaches. This improvement is due to increases in the precision. The results show that REEL can be used to mitigate the problems associated with the application of PPR for NEL using sparse disambiguation graphs. Our framework improved the performance of NEL when the output of a deep-learning RE tool (BO-LSTM) is included and also when relations annotated in a gold standard (BC5CDR corpus) are included, which demonstrates that the framework have the flexibility to easily integrate relations provided by any source.
For future work, we intend to explore pre-trained language models, like BioBERT  [37] , to further improve the determination of local similarity between mentions and ontology concepts. Besides, it would be interesting to adapt this method to other types of KBs other than the ontologies, like Wikipedia, which are less formally defined. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Availability of data and materials Project name: REEL. Project home page: https ://githu b. com/lasig eBioT M/ REEL. Operating system: Ubuntu 18. 04 LTS. Programming language: Python ≥ 3. 6, Bash. Other requirements: JDK ≥ 11. 0. 6, BO-LSTM. License: Apache License 2. 0 The data supporting the results and the code necessary to reproduce the results are openly available on the referred GitHub repository, as well a Dockerfile to properly setup the environment to run the code. The data used in this work is available in the following links: ChEBI ontology file is available on ftp:// ftp. ebi. ac. uk/pub/datab ases/chebi /archi ve/rel17 9/ontol ogy/. CTD's MEDIC Disease vocabulary and CTD's Chemical Vocabulary files are available under conditions on http://www. ctdba se. org/repor ts/CTD_disea ses.
obo. gz and on http://www. ctdba se. org/repor ts/CTD_chemi cals. tsv. gz, respectively. CRAFT Corpus is available on https ://githu b. com/UCDen ver-ccp/CRAFT /relea ses/ downl oad/3. 0/craft -3. 0. zip. BC5CDR corpus is available on https ://githu b. com/ JHnlp /BioCr eativ e-V-CDR-Corpu s/blob/maste r/CDR_Data. zip Alternatively, the script "get_data. sh" in the GitHub repository automatically downloads all the necessary data.
Data analysis Given the small sample size, for the primary outcome measures, we report medians and interquartile ranges, supplemented with nonparametric tests to explore group differences and changes over time (within groups). Results from these hypothesis-driven tests should be interpreted with caution given that our study was underpowered. Transcripts from the semi-structured interviews were analysed using inductive thematic analysis which permits themes and codes to be strongly linked to the data  [29]. This method involves a five-phase approach: (a) familiarization with the data, (b) generating initial codes, (c) searching for themes, (d) reviewing themes and (e) defining and naming themes  [29]. NVivo 10 [30]  was used to facilitate the coding process, and the analysis continued in an iterative process whereby raw data was continually analysed to identify themes (which could be merged, removed or stratified if redundant).
For this purpose, aptamers (without biotin) were dissolved in the binding buffer (20 mM Tris pH 7. 6, 100 mM NaCl, 2 mM MgCl 2 , 5 mM KCl, 1 mM CaCl 2 , 0. 02% v/v Tween 20) and folded at 4 μM according to the SELEX protocol provided above. Recording of UV-spectra ranging from 200-320 nm was done at 20˚C and results are displayed as mean residue molar ellipticity. For further G-quadruplex analysis, singular value decomposition was conducted using an R-script developed by del Villar-Guerra et al. [22].
Mass spectrometry of heat-treated rituximab Three aliquots of rituximab solution, 50 μl each, were kept for 1 min at 80˚C, 5 minutes at 70˚C or 5 minutes at RT respectively. Subsequently, all samples were subjected to disulfide bond reduction, alkylation and tryptic digestion. Chromatographic separation of 1 μg digested rituximab was carried out on a Dionex™ UltiMate™ 3000 Rapid Separation system, after which mass spectrometry was conducted on a Thermo Scientific TM Q Exactive TM benchtop quadrupole-Orbitrap mass spectrometer equipped with a Thermo Scientific TM Ion Max TM ion source housing with a heated electrospray ionization (HESI) probe. Data acquisition was conducted using Thermo Scientific TM Chromeleon 7. 2 Chromatography Data System (CDS). Data analysis of mass spectra and peak assignment was performed using Thermo Scientific TM BioPharma Finder software 3.
MK, SW, CR, CGH, GG: This study was funded by the  Austrian Federal Ministry for Digital and Economic Affairs ; the  National Foundation for Research, Technology and Development ; a  Startup Grant  of the Province of Salzburg; the  priority program  " Allergy-Cancer-BioNano Research Centre " of the  University of Salzburg  and the  Doctorate School Biomolecules at PLUS. "The funder provided support in the form of salaries for authors MK, SW, and  CGH , but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Novartis and  Thermo Fisher Scientific GmbH  provide financial support for the  Christian Doppler Laboratory for Innovative Tools for Biosimilar Characterization, Salzburg, Austria.
Cloning, expression and purification of SFTSV full-length L protein The L gene of SFTSV strain AH12 (GenBank accession no. HQ116417) with a C-terminal StrepII-tag was chemically synthesized (Centic Biotech, Germany) and the plasmidintegrated gene was amplified via PCR. In the same step, primers were used for the introduction of mutations to the gene in the case of the catalytically inactive L proteins (D112A and D1126A). Amplified genes were cloned into an altered pFastBacHT B vector using the In-Fusion HD EcoDry Cloning Kit (Clontech). After transformation of DH10EMBacY Escherichia coli cells (kindly provided by Imre Berger), which contain a bacmid as well as a plasmid coding for a topoisomerase, with the pFastBacplasmids, recombinant bacmids were isolated and transfected into Sf21 insect cells for recombinant baculovirus production.
Cloning, expression and purification of SFTSV cap-binding domain The L gene region corresponding to amino acid residues 1695-1810 of SFTSV strain AH12 (GenBank accession no. HQ116417) was cloned into a pOPINF vector (24) using the NEBuilder HiFi DNA Assembly Cloning Kit (New England BioLabs). The protein, which based on sequence alignments corresponds to the CBD of RVFV  (11)  and therefore referred to as SFTSV CBD, was expressed as a wildtype version or mutated version (with single amino acid exchanges F1705A, Q1707A or Y1719A) in E. coli strain BL21 Gold (DE3) (Novagen) at 17 • C overnight using TB medium and 0. 5 mM isopropyl-␤-D-thiogalactopyranoside for induction. After pelleting, the cells were resuspended in 50 mM Na-phosphate pH 7. 5, 100 mM NaCl, 10 mM imidazole, Complete protease inhibitor EDTA-free (Roche), 0.