dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Funding for this project comes from  NSF  grants  EAR-1050020  and  EAR-1946911  to  Whitney and Teyssier , as well as funding from the  College of Science and Engineering at the University of Minnesota. Funding for the electron microprobe facility used in this research was provided by  NSF  grant  EAR-1625422. SIMS analyses were carried out at the  University of Wisconsin-Madison department of Geology, WiscSIMS  is supported by  NSF  ( EAR-2004618 ) and the  University of Wisconsin-Madison. We thank  Patrice Rey  for collaborative field work to locate and collect the dome-core eclogites,  Andrew Kylander-Clark  for assistance with LASS-ICP-MS analyses and data processing at the  University of Santa Barbara , and  Natalie H. Raia  as SHRIMP-II zircon data analyst. We also thank  John V.
Methods Literature and clinical trial searches were performed in PubMed, Dimensions, Embase and Google (1999-2020). The searches focussed on the use of AI and its various forms to facilitate literature searches, clinical guidelines development, and clinical trial data extraction in uro-oncology. To illustrate how AI can be applied to address questions about optimising therapeutic decision making and individualising treatment regimens, the Dimensions-linked information platform was searched for 'prostate cancer' keywords (76 publications were identified; 48 were included).
1 ). There are multiple online resources with comparable functionality to PubMed (a free search engine that can be used to access references in the MEDLINE database), including Google and Google Scholar, as well as researchspecific databases, e. g. Dimensions, Embase, Medscape, and Scopus  [3]. Other important resources include clinical trial databases, clinical decision support recommendations through Epocrates, DynaMed Plus, LexiComp, MD Consult, Micromedex, UpToDate, UroToday (https://www. urotoday. com/), UROSource (https://urosource. uroweb. org/), WebMD (Table  1 )  [4, 5] , and, increasingly, social media accounts. Challenges and limitations can be difficult to address given the volume and breadth of channels. Social media and popularity bias ultimately influence the quality and type of content made available in searches  [6].
Ad hoc searches for answers to clinical questions in PubMed and other databases can provide incomplete results as they are not always optimised to filter or display relevant information in response to queries, and clinicians often do not have the time or the search expertise to process the high numbers of references generated from the keywords selected  [7, 8]. In addition, access is often limited to abstracts and not full publications due to publisher paywalls  [9]. These challenges will only expand as the rate of research and publications, as well as search resources, continues to increase. To address the bottlenecks that occur between formulating clinical questions, acquiring and finally interpreting data, companies and academics are developing tools that apply artificial intelligence (AI), a loosely defined collection of automated systems that can perform tasks considered to require 'intelligence'.
Methods A bridging review on the application of AI to facilitate literature searches, the development of clinical guidelines and decision making, along with clinical trial synthesis and data extraction in uro-oncology was completed. This review provides an opportunity to 'bridge' or consolidate the diverse applications of AI and translate them into practical applications in uro-oncology. Literature and clinical trial searches were carried out in PubMed, Embase and Google between 1999-2020. Keywords included the following: 'artificial intelligence'; 'natural language processing'; 'machine learning'; 'urology'; 'prostate cancer'; 'uro-oncology'; 'oncology'; 'literature extraction'. Primary manuscripts on the application of AI in the field of uro-oncology were selected from these searches for review, and searches were limited to English-language publications.
Additional articles formed the basis of the bridging review, including results on physician literature searching behaviour, the role of PubMed and other literature search tools, and progress in the use of automated literature searching in systematic literature reviews and clinical guideline development. Using this strategy, 76 relevant publications were identified and 48 were included. To supplement the literature search and further illustrate how AI can be applied to facilitate these searches, a separate investigation was performed using the Dimensions database. The database was queried using a complex search strategy using the keywords 'prostate cancer' or synonyms like 'prostatic neoplasms' in the title or abstract. The search was further fine-tuned using the following terms: 'sequence', 'sequencing', 'sequential', 'first', 'second', 'third', 'secondary', 'prior', 'subsequent', 'previously', 'after', 'earlier', 'later', 'pretreated', 'cross-resistance', 'progressed'.
For example, clinicians who seek information or write their own articles or grants may be unaware of the utility of AI for the synthesis and automation of literature and clinical trial database searches. The development of AI-based tools can be considered in three main areas: literature searches, data extraction, and data synthesis or insight generation  [17]  (Fig. 2 ). Advancements in AI suggest that it exhibits even broader potential for use via these constructs in question and answer (Q&A), clinical guideline development, as well as the development of interactive data and data visualisation  [3, 18, 19]. For example, large, general-purpose language models such as the 'Generative Pre-trained Transformer 3' model have been developed for Q&A  [20]. These models enable the user to perform various tasks from scientific reasoning, to classification, to generating language.
Ultimately, the desire is to have systems, like humans, able to perform tasks associated with language (such as answering questions, basic reasoning, or connecting series of information) based on only a few examples or simple instruction. There is growing capability to assess strength of evidence, e. g. whether research relates to a randomised controlled trial and the extent of bias, which can be judged, for example, by the Cochrane Risk of Bias Tool, Version 2 (RoB2; https://methods. cochrane. org/bias/resources/rob-2revised-cochrane-risk-bias-tool-randomized-trials) [21]. Recent studies have also evaluated publicly available platforms for their potential to filter out irrelevant information from literature searches  [22, 23]. To date, models have been tested largely on PubMed or other single databases.
In these models, expanding searches to include additional databases improves results  [24] [25] [26]. Other approaches to literature searches include use of a machine learning (ML) algorithm to enable reference clustering to facilitate creation of review articles by identifying a list of relevant references  [27]. ML refers to the study and use of algorithms that improve experiences and outputs by deducing patterns from past data and using it to make predictions  [10]. ML models have been shown to be at least as good as manual searches  [24, 25, 28] , while reducing abstract screening workload  [8, 24, 26]  and review time for conducting meta-analyses by ~84%  [24]. Clinicians who begin using AI should understand the provenance of the data ingested by the platforms that   1 )  [29] [30] [31].
Artificial intelligence (AI) A loosely defined concept describing automated systems that can perform tasks considered to require 'intelligence' [10] Gold standards (or set) A dataset that exists in order to train (in the case of supervised learning), test and evaluate algorithms that do empirical analysis. The same analysis is performed on small numbers of example data to provide annotations that establish the truth. This truth specifies for each example what the correct output of the analysis should be. This can also be referred to as ground truth Machine learning (ML) The process of developing systems with the ability to learn from and make predictions using data  [10]  Natural language processing (NLP) An area of AI that focusses on problems involving the interpretation and understanding of free text by a non-human system  [  ), and generic names of treatments, stratified by clinical trial information or review articles, generated a diverse number of outputs.
Some of these overlapped with previous manual searches and others were discarded, having no relevance to the applied filter string. Thus, a manual literature search requires user input of specific and complementary terms to an increasingly complex filter string in order to narrow the scope of returns with articles relevant and applicable to the identified topic. Results of searches will also be spread across multiple outputs, making it challenging to prioritise information of interest and obtain insights. In comparison, utilisation of AI to address queries requires a different approach that ultimately allows for a closer dissection of focussed papers. An AI-based platform, Dimensions, was used to conduct queries inclusive of 12 clinical trial databases and >100 scientific literature sources globally, including PubMed, PubMed Central, arXiv, Crossref, and ~130 publishers.
An interactive visualisation was created based on searches for emerging therapeutic sequences and/or drug combinations (Fig. 3 ; platform can be accessed here: https://reports. dimensions. ai/pfizer-prostate-cancer-agentcombinations/). The searches were constructed with pairs of agents mentioned within five words of each other in the document (full text of a publication or a clinical trial description), with an additional requirement that the main topic be 'prostate cancer treatment'. A tab on clinical trials provides a similar view into study type and phase, geography, and investigators that may not be as apparent or accessible in manual searches of individual clinical trial databases. The dashboard is updated as the user selects specific data of interest for a more focussed analysis. The dashboard is not without limitations.
Because the platform uses a proximity-based search, users are limited in their ability to separate a combination study from a sequencing study, as semantic relationships were not extracted, which is particularly problematic. Semantic analyses are currently being used to separate combination and sequencing studies and will be shared in a future publication. The dashboard also does not directly answer queries, and subject matter expertise is required to interpret the data. However, the resulting platform demonstrates how AI can assist urologists and oncologists to readily obtain focussed information on a specific topic of interest from rich datasets, in this case published literature and clinical trials on specific combinations or sequences of therapeutic agents, including subdivisions according to drug, geography, investigator, and type of article/grade of evidence.
Information extraction from electronic health records also has been applied by researchers to facilitate structured data entry into clinical databases  [41]  and assess patient outcomes  [42]  or to extract information from note entries  [43]  to identify and evaluate patients. These literature-based discovery systems implement co-occurrence-based metrics to analyse the strength of associations and allow for a real-time search to unveil the indirect associations between findings in vast databases. Finally, using AI to directly address a clinical question represents a step further than aggregating meta-data from scientific publications to assist clinicians  [44, 45]. Systems for general question answering (e. g. Apple Siri, Amazon Alexa, Google Assistant) are rapidly increasing in sophistication, but have not been developed specifically with healthcare questions in mind  [44].
Few peer-reviewed publications currently employ AI-based tools to aid development of uro-oncology guidelines  [19]. This is despite the fact that advances in AI can enable clinicians and reviewers to increase the speed of timeconsuming tasks in the process. Advances in online platforms and ML techniques may enable the goal of creating 'living' systematic reviews and guidelines that are updated with near real-time frequency as supporting evidence is released instead of scheduled updates  [19, 36]. Other potential future directions include using AI-based models to assist in medical writing or peer review. In conclusion, while there currently is no single resource or database that is completely comprehensive, through NLP and the aggregation of meta-data, AI exhibits the potential to support more tailored, accurate, and reliable clinical decisionmaking from the multiple sources of information available  [46].
Causaly Search and analysis; covers scientific literature, clinical trials, and sideeffect databases Dimensions https://www. dimensions. ai/Digital Science and Research Ltd Search and analysis; covers scientific literature, grants, patents, clinical trials, datasets, and policy information EvidScience https://www. evidscience. com/InferLink Corporation Gap and landscape analysis and comparative effectiveness studies; covers scientific literature Galactic AI TM https://www. biorelate. com/Biorelate Literature search and curation in the drug discovery process; covers scientific literature  Google Scholar https://scholar. google. com/Google LLC Literature search and analysis of scientific web content; covers scientific literature and patents Iris. ai https://iris. ai/Iris. aIAS Research landscape mapping; covers scientific literature Meta www.
meta. orgMeta  ULC Search  and curation of feeds on biomedical topics; covers scientific literature Scibite https://www. scibite. com/Scibite Search and analysis; extracts scientific terminology from unstructured text Scite. aIhttps://scite. ai/Scite Search and analysis; checks if citations have been supported or disputed by others Scopus https://www. elsevier. com/solutions/scopusElsevier B. V. Search and analysis; covers scientific literature and is curated by an advisory board Semantic Scholar https://www. semanticscholar. org/AI2,  Alan Institute for Artificial Intelligence Search  and analysis; covers scientific literature Web of Science https://clarivate. com/webofsciencegroup/solutions/web-of-science/ Clarivate Search and analysis; covers scientific literature Acknowledgements Use of the Dimensions database was funded by  Pfizer.
Editorial support was provided by  Michelle Mancher ,  MPH of Ashfield Healthcare Communications , and  Donna Vining ,  DO ,  Laura Geuss, PhD , and  Kathryn Marshall  of  Onyx Medica , funded by  Pfizer , according to Good Publication Practice guidelines (https://www. acpjournals. org/doi/10. 7326/M15-0288 ). The ultimate responsibility for opinions, conclusions, and interpretation lies with the authors. Disclosure of Interests Arnulf Stenzl reports being an advisor/consultant for  Roche Holding AG ,  Janssen Pharmaceuticals, Inc. ,  Steba Biotech Ltd ,  Synergo International, Ferring BV, Ipsen Pharma, Sanofi-Aventis S. A. ,  Astellas Pharma US Inc. , and  AstraZeneca  plc; receives research grants from  Amgen Inc. ,  Immatics Biotechnologies GmbH , and  Karl Storz SE & Co. KG. Cora N. Sternberg reports being an advisor/consultant for  Pfizer Inc.
Sequencing and combining of therapeutic agents in prostate cancer is an example of an area that is challenging to organise and understand acquired knowledge. Treatment choices and sequencing decisions are made against a backdrop of abundant published information coupled with lack of consensus [33]. Clinicians may initially seek out recent and past citations to papers that are interesting to identify further reading, query colleagues, or perform more targeted literature searches via PubMed and other search engines. Questions will evolve and become more personalised/specific as the search matures -and require additional ad hoc searches. 10] Ontologies Means of communicating and resolving semantic and organisational differences between biological databases [48] Semantics Refers to the meaning that is conveyed by a text [16] © 2021 Pfizer Inc.
ACKNOWLEDGMENTS We thank  J. Chen  for assistance with the mass spectrometry analysis,  K. Rittinger  and  T. Sommer  for reagents,  H. Walden  and  N. Zilio  for constructive comments on the manuscript, and  IMB's media lab  for supplies. This work was funded by  ERC Advanced  ( AdG 323179 ) and Proof-of-Concept (PoC 786330) grants to H. D. U. and by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), project  ID   393547839-SFB 1361.
7 °C and control pCO 2 436 µatm; (2) control temperature 28. 8 °C and elevated pCO 2 929 µatm; (3) elevated temperature 31. 3 °C and control pCO 2 449 µatm; and (4) elevated temperature 31. 4 °C and elevated pCO 2 982 µatm. Salinity and total alkalinity were measured weekly. Total alkalinity was analyzed by Gran titration from water samples of replicate tanks in each system to within 1% of certified reference material (Prof. A. G. Dickson, Scripps Institution of Oceanography). Seawater pCO 2 was calculated using the program CO2SYS  30  with the constants of Mehrbach et al. 31  refit by Dickson and Millero 32 and Dickson  33  for K(HSO 4 -). Water conditions for each treatment are reported in Table  1. Light levels, measured as photosynthetically active radiation (PAR), were 340 µmol photons m -2 s -1.
Shell organic content. Elemental analysis was performed on subsamples of powdered shell (mass range: 1. 51-3. 31 mg) after combustion in a CHN Elemental Analyser (Model EA1110, CE Instruments, Wigan, UK). Because preliminary analyses of bivalve shell material revealed a carbon content of ca. 12% by mass, l-cysteine (ca. 30% carbon by mass) was used as a standard for calibration of all T. squamosa shell samples. Samples were run in duplicate (data reported as means) and all elements (i. e. , C, H, and N) were analyzed simultaneously. Statistical analyses. The effects of elevated seawater temperature and pCO 2 and their interaction on morphometric components of new shell growth (i. e. , clam wet mass gain, shell length gain, shell height gain, shell width gain, and ornamentation width gain) were analyzed using a two-way multivariate analysis of covariance (MANCOVA), with temperature and pCO 2 as fixed factors.
As all pre-exposure morphometric variables were strongly, and significantly correlated (Table  S2 ), we used only one of them (i. e. , pre-exposure shell length) as a covariate in this initial MANCOVA. As a follow-up to the MANCOVA, a series of two-way ANCOVAs were conducted on each of the five morphometric response variables using the corresponding pre-exposure morphometric trait as the covariate: e. g. pre-exposure shell length for length gain analysis, etc. Unlike morphometric response variables, which were only quantified once per individual at the end of the experiment (i. e. , growth over the course of treatment), mineralogical and organic data were collected multiple times for each individual (i. e. , sampling of both newly-formed and older-growth shell and scute). For analysis of these data, any measurement with a value less than zero (i.
As explained previously for morphometric data, a similar suite of MANCOVAs (and follow-up ANCO-VAs) were run on the shell organic and mineral content data using post-exposure total wet mass as the covariate. Before performing MANCOVAs for morphometric, organic, and mineralogical variables, assumptions of normality were visually assessed for all response variables (i. e. , five morphometric variables, three organic variables, and nine mineralogical variables) with Q-Q plots. Homogeneity of variance was tested for all variables using Levene's F test. Based on Levene's F test results, the homogeneity of variance assumption was satisfied for all organic variables but was not satisfied for one of the five morphological variables and several of the mineralogical variables (i. e. , P < 0. 05, see Table  S3 ).
4. 0. 2) and "car" (v. 3. 0. 9) packages, respectively  37  , in the statistical computing software program R (v. 3. 6. 1)  38 , and results were considered statistically significant (moderate evidence of an effect) and marginally significant (weak evidence of an effect) at alpha values ≤ 0. 05 and between 0. 05 and 0. 1, respectively. Finally, a series of post-hoc analyses (i. e. Bonferroni pairwise comparisons) were performed using estimated marginal means calculated with the R function emmeans in the package "emmeans" (v. 1. 5. 0)  39 with FDR (i. e. Bonferroni) multiple-comparison p-adjustments to examine individual mean difference comparisons across all levels of experimental treatment. The effect sizes (i. e. , partial η 2 ) were calculated using the etaSquared function of the "lsr" package (v.
Acknowledgements The authors would like to thank  University of Plymouth  technicians  Marie Hawkins ,  Andrew Fisher , and  Andrew Tonkin  for their expertise and advice in carrying out analyses. The authors would also like to thank the members of the  MBERC research group at the University of Plymouth  for their constructive input during experimental design. This work was supported by funding from the  Australian Research Council Centre of Excellence for Coral Reef Studies [S. -A. W. ],  Ian Potter Foundation  [20130107 S. -A. W. ], and  Save Our Seas Foundation  [273 S. -A. W. ] as well as with US Government support to  E. J. A. under and awarded by the  Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG)  Fellowship, 32 CFR 168a.
I. Introduction Proteolysis is a key posttranslational modification. Every single protein will encounter a protease, at some point in its lifetime, to either remove a signal peptide, cleave a propep-tide for activation, process a protein to modify its functions, or initiate ubiquitination and degradation  (Fortelny et al. , 2014; Kappelhoff et al. , 2017). Proteases are fundamental enzymes implicated in all aspects of biology, representing $3% of the human genome, and are divided into five ABBREVIATIONS: ABP, activity-based probe; ACPP, activatable cell-penetrating peptide; ADAM, a disintegrin and metalloproteinase; ADAMTS, a disintegrin and metalloproteinase with thrombospondin motif; AP, activator protein; BAY 12-9566, tanomastat; BB-94, batimastat; BBB, blood-brain barrier; B-CLL, B cell chronic lymphocytic leukemia; BM, basement membrane; BRET, bioluminescence resonance energy transfer; CD, Crohn's disease; CDn, cluster of differentiation n; CLP, cecal ligation and perforation; CNS, central nervous system; EAE, autoimmune encephalomyelitis; ECM, extracellular matrix; EMMPRIN, extracellular matrix metalloproteinase inducer; FDA, US Food and Drug Administration; Fra, FOS-related antigen; FRET, F€ orster resonance energy transfer; GI, gastrointestinal; GM6001, ilomastat; GPI, glycosylphosphatidylinositol; HBV, hepatitis B virus; HIV, human immunodeficiency virus; IBD, inflammatory bowel disease; ICU, intensive care unit; IFN, interferon; IL, interleukin;  INXN-1001, veledimex ; LAP, latency-associated peptide; LPS, lipopolysaccharide; LRP1, low-density lipoprotein receptor-related protein 1; LTBP, latent TGFb binding protein; mAb, monoclonal antibody; mCD100, membrane-bound CD100; Met, methionine; MMP, matrix metalloproteinase; MPO, myeloperoxidase; MRI, magnetic resonance imaging; MS, multiple sclerosis; MT-MMP, membrane-type matrix metalloproteinase; NF-jB, nuclear factor kappaB; NIR, near-infrared; PAR, proteaseactivated receptor; PDB, Protein Data Bank; PTM, posttranslational modification; RA, rheumatoid arthritis; RECK, reversion-inducing cysteine-rich protein with Kazal motifs; ROS, reactive oxygen species; sCD100, soluble CD100; SDS, suppressor of defective silencing; SpA, spondyloarthritis; Tat, transactivator of transcription; TGFb, transforming growth factor-beta; TIMP, tissue inhibitor of metalloproteinase; TNFa, tumor necrosis factor-alpha; UC, ulcerative colitis; VE-cadherin, vascular endothelial cadherin; VEGF, vascular endothelial growth factor.