dataesr/scientific-paragraphs-categorization huggingface dataset:
Schema. org and RDFa for biological databases Schema. org is a collection of extensible schemas that webmasters can use to mark up structured data on their web pages with the aim of improving search engine performance and enabling the creation of other applications. The initiative was founded by Google, Bing and Yahoo! as a collaboration to improve the web and their search results by using such structured data. More than 700 item types have been listed in schema. org, some of which have been supported by these search engines. If webmasters mark up their content in an acceptable markup format (e. g. Microdata, microformats or RDFa), then web crawler programs can detect these structured data and they can be rendered as rich snippets in the search results. During the BioHackathon, the members of this working group proposed two item types for a schema extension: "Biological-Database" and "BiologicalDatabaseEntry".
We discussed what item properties would be suitable for our purposes and how to label them in markup. Finally, we decided to use the Microdata format to mark up web pages and proposed five original properties: "entryID", "isEntryOf", "taxon", "seeAlso" and "reference". Work in this area is now being carried forward by the bioschemas. org project. We also publicized our proposal and encouraged BioHackathon members to mark up their databases. A Microdata crawler was created to extract these structured data. We modified "Sagace"  96  , a web-based search engine for biomedical data and resources in Japan, developed at the NIBIOHN in collaboration with NBDC. We confirmed that marked-up data showed up as rich snippets in search results. Ten databases have been marked up with our new proposal and so can help improve the readability of search results.
Bio2RDF2SADI Discovering and reusing data requires substantial expertise about where data are located and how to transform them into a more useable form for further analysis. While the Bio2RDF project transforms dozens of key bioinformatic resources into RDF, and is made available through public SPARQL endpoints, a key challenge still remained: how to identify which datasets contain the entities and relations that are of interest to solve a particular problem. To this end, Bio2RDF now generates and publishes summaries of the dataset contents in each of its SPARQL endpoints, thereby simplifying lookup, and reducing server load for expensive and common queries. During the hackathon, an architecture was developed for an automated approach that utilizes the metadata from Bio2RDF's content summaries to automatically generate SADI Semantic Web Services that provide discoverable access to this Bio2RDF data  104.
Quality assessment A large amount of biomedical information is available via SPARQL endpoints, often in a redundant way. Life Sciences databases often integrate information from different sources to enrich the data they provide, and some information resources are pure aggregators whose value is in the harmonization of the information that they collect. As these resources publish their information on the Semantic Web, the result is that the same information is present in multiple endpoints. As a consequence, to decide which endpoint to use to access some particular data of interest is not a trivial task. Two hackathon activities addressed this issue. The development of a dataset descriptor is useful to know which data are present in an endpoint, with information on version, representation and update policies.
But even if such a descriptor is provided, there is still an issue of the reliability of endpoints. It is also difficult to know which endpoints are actively maintained and which are not. YummyData is a project that monitors endpoints by periodically running queries and performing a few tests. By collecting data over extended periods, it can provide a proxy for the reliability of an endpoint and the dynamism of the information it provides. More specifically, YummyData periodically queries datahub. io for datasets tagged as being of biomedical interest. It combines the result with a list of curated endpoints and, periodically, it runs a series of tests and queries and stores their results. YummyData performs some tests to determine whether the endpoint provides a VoID descriptor (see section above), as well as to measure response time.
Conclusion To fulfil the mission of the DBCLS, which is to integrate life sciences databases, the annual BioHackathon series was started in 2008 to explore state-of-the-art technological solutions. The utilization of Semantic Web technologies as a means for database integration was introduced in BioHackathon 2010  3. Since then, we have collaboratively worked as a community to promote the use of RDF and ontologies in life sciences. As one of the demonstration products, DBCLS released the first RDF-based genome database, TogoGenome, in 2013. Subsequently, the EBI RDF Platform was released by EMBL-EBI and PubChem RDF was published by NCBI, and these provide fundamental database resources in genomics to the wider biomedical research community as well as the pharmaceutical and biotechnology industries.
○ In the Provenance of data section, I missed the reference to W3C PROV, which btw is used by the nanopublication approach and is a fundamental standard for reasoning over the data provenance. Reviewer Expertise: Ontology engineering, semantic interoperability, FAIR data principles, e-Health semantics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Reviewer Report 18 November 2019 https://doi. org/10. 5256/f1000research. 19950. r54207 I do have some suggestions below where I think the manuscript could be improved -primarily to help developers and users put this work into context. Most of these are are not critical flaws, however, and I would leave to the authors' discretion how they wish to respond to these comments and suggestions.
PROTEIN STRUCTURES AND EXPRESSIONS "the Worldwide Protein Data Bank (wwPDB) will abolish the conventional PDB format" and "wwPDB will start assigning protein chain identifiers, which will also be encoded as URIs in the wwPDB/RDF. " 2016 is three years ago, so if this has already been accomplished, this should be in past tense. "Recently, the mzTab data exchange format was introduced" -recently meaning 2014? "the "omics" group in the BioHackathon 2014 made the first steps towards the development of the ProteomeXchange Interface (PROXI) for protein expression data exchange59. " -my impression is that this effort has evolved considerably since 2014. If true, it would be valuable to put this into context for how the technology is used currently by the ProteomeXchange consortium. "integration of the two corpora, CRAFT and GRO, into PubAnnotation, demonstrated significantly improved utility" -is there supporting evidence for this claim? "Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data -this is the core of the FAIR Data Principles83.
" -it's odd to reference this manifesto of principles that postdates the hackathons by at least two years. This would be less awkward if there was a table or section devoted to developments *since* the 2014, i. e. "where are they now?" DATASET METADATA "The resulting guidelines, finalized under the auspices of the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG)" -there's no link or citation given here. The 4th pgph redundantly includes the same coverage list: ". elements of description, identification, versioning, attribution, provenance, and content summarization. " "The group is currently evaluating the specification with implementations for dataset registries such as Identifiers. org50 and IntegBio Database Catalog, as well as Linked Data repositories such as Bio2RDF84.
VOID FOR INTERMINE AND UNIPROT The relationship of prior work on VOiD (if any) to the outcome from the hackathon is not clear to me. "there are now InterMine databases available" -is "now" as of writing, or as of the time of the hackathon? The VoID file [for Uniprot] is updated every release in synchrony with our production" -replace use of "our" which I presume refers to the Uniprot team and not to the collaborative team of authors here.
SCHEMA>ORG AND RDFA FOR BIOLOGICAL DATABASES what's "NIBIOHN"? NANOPUBLICATIONS A more explicit explanation of the relationship between NANOPUBLICATIONS and the overarching topic of PROVENANCE would be helpful. "n-quads" -please explain or provide a reference. "At the time of writing, YummyData has collected data for about a year" -what is the time of writing? CONCLUSION "The NBDC RDF portal launched in 2015 complements the above resources. " -again, this would be appropriate for a "where are they now?" section. "will be addressed in the upcoming BioHackathons" -they have been held annually since 2014, so it is odd to not mention what *was* addressed in subsequent events. And I hope those reports will be published in a more timely fashion! Reviewer Expertise: Evolutionary genetics, genome evolution, bioinformatics and computational biology.
Is I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000. com.
RDF. After the BioHackathon 2012, a group from DBCLS and DDBJ developed an ontology which can capture semantically the data model of the INSDC, such as the records from GenBank, DDBJ and ENA, with restrictions on terms used in the feature table and qualifier key-values. A converter for INSDC and RefSeq 23 entries to RDF was developed based on the ontology, and the RDFized data is used in the TogoGenome application. TogoGenome integrates information on genes, proteins, organisms, phenotypes and environments. Because the genes in TogoGenome are currently extracted from INSDC and RefSeq records, the URI for each annotation is constructed as a fragment using Identifiers. org URIs in the form: <http://identifiers. org/[insd c or refseq]/[entry_id]#[fragment]>. For example, the human APOE gene on chromosome 19 in the RefSeq record NC_000019.
10 is internally represented in TogoGenome as <http://identifiers. org/refseq/NC_000019. 10#feature:44905782-44909393:1:gene. 1424> and the information can be accessed at <http://togogenome. org/gene/9606:APOE> where 9606 is the taxon ID corresponding to human in the NCBI Taxonomy database and APOE is the gene name used in the record. This approach is slightly different from the proposed UBSID model which encodes sequence alignment with comments but can distinguish the source of information and feature types annotated in the INSDC/RefSeq record. The location of each gene and exons in TogoGenome RDF are described by the FALDO ontology.
Acknowledgements BioHackathon 2013 and 2014 are supported by the Integrated Database Project ( Ministry of Education, Culture, Sports, Science and Technology of Japan ) and hosted by the  National Bioscience Database Center (NBDC)  and the  Database Center for Life Science (DBCLS). We thank  Yuji Kohara , the director of DBCLS, for his supporting the BioHackathons.
Extended data Records of the BioHackathon 2013 and 2014 meetings are aggregated at https://github. com/dbcls/bh13/wiki and https://github. com/dbcls/bh14/wiki respectively. Zenodo: dbcls/bh13: Included repositories related to BH13. http://doi. org/10. 5281/zenodo. 3271508  107 is project contains the following extended data: • dbcls/bh13-v1. 0. 0. zip (BioHackathon 2013 records) Zenodo: dbcls/bh14: Included repositories related to BH14. http://doi. org/10. 5281/zenodo. 3271509  108 is project contains the following extended data: • dbcls/bh14-v1. 0. 0. zip (BioHackathon 2014 records) Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0).
Extended data Movie 1 Fibroblast spreading assay: cell edge analysis of GFP-βII-spectrin and RFP-Actin A) Representative images of MEFs immunostained for βII-spectrin (green), αII-spectrin (red) and F-actin (magenta), visualized by TIRFM (scale bar: 10 µm). Correlation analysis of pixel intensities between the different channels is reported in the two scatter plots. B) Gene expression analysis of different spectrin e isoforms in MEFs used for this study, measured by qPCR (n=3, n. d. =not detected, Statistical analysis: oneway ANOVA with multiple comparisons, p < 0. 0001). The genes sptan1 and sptbn1 encode for αII and βII spectrin respectively. C) βII-spectrin protein expression in different cell lines analyzed by western blot in total cell lysates. D) Different cell lines were immunostained for F-actin (magenta) and endogenous βIIspectrin (green).
CONFOCAL and TIRF Microscopy Confocal microscopy was performed on a Leica TCS SP8 laser scanning confocal microscope mounted on a Leica DMi 8 inverted microscope, equipped with a motorized stage and controlled by the software Leica Application Suite X (LASX) ver. 3. 5. 2. 18963. For image acquisition, we used a HC PL APO CS2 63X/1,40 oil immersion objective. DIC, Epi-Fluorescence and Total Internal Reflection Fluorescence microscopy (TIRFM) of fixed specimens, live time-lapse of spreading cells, drug treatments, osmotic shocks, cell stretching (EPI mode only) and cell compression were performed on a Leica AM TIRF MC system. Two different objectives have been used: HCX PL APO 63X/1. 47NA oil immersion and HCX PL APO 100X/1. 47NA oil immersion. The lasers used for fluorochromes excitation were 488nm, 561nm, 635nm.
A specific dichroic and emission filters for each wavelength have been used. The microscope was controlled by Leica Application Suite AF software (Ver. 2. 6. 1. 7314) and images were acquired with an Andor iXon DU-8285_VP camera. For live imaging experiments, environmental conditions were maintained thanks to an Okolab temperature and CO 2 control system if needed. Micropatterning by Quartz Mask Borosilicate glass coverslips (Corning) were washed with 70% Ethanol, airy-dried, activated with plasma cleaner (Harrick Plasma) for few minutes and incubated with 0. 1 mg/ml PLL-PEG for 1 hour at room temperature. A quartz mask (Delta mask B. V. ) wash washed with 70% Ethanol and activated under UV light using a UV lamp (UVO Cleaner, Jelight). PEGylated coverslips were put on the desired pattern in the mask, which then was illuminated under UV light for 7 minutes.
Proteins were detected with ECL Western blotting reagents (Amersham GE-Healthcare), by the digital Chemidoc XRS+ run by the software Image Lab (Biorad). Fixed Spreading assay MEFs were seeded on 10 μg/ml fibronectin-coated coverslips/glass-bottom dishes in complete media, as previously described. A total of 10 5 cells were seeded and fixed at different time points (between 5 and 20 minutes after seeding) by 4% paraformaldehyde diluted in PBS for 10 minutes. Before detergent permeabilization for immunostaining, fixed cells were incubated with membrane dye FM4-64 FX (Thermo Scientific) according to manufacturer specification (2. 5-5 μg/ml). A second fixation step was applied to preserve cell membrane staining by the fixable dye and avoid diffusion after a subsequent permeabilization step in 0.
1% Triton-X100 (1-2 minutes). Immunostaining for βII-spectrin and F-actin (phalloidin) was performed as previously described for TIRFM investigation. From the raw images, the signal background was subtracted, while edge-preserving filters were applied to the FM4-64 fluorescence signal to generate a binary mask of the projected cell area in the TIRF plane. The Fiji built-in "Analyze particle" tool was then used to extract the projected area value as well as the integrated density of the two immunostainings (βIIspectrin and F-Actin). Linear regression analysis between the three parameters was then performed by R software, and raw data points plotted by the software Graphpad Prism. Live Spreading assay The spreading assay was performed on custom-designed 2-way aluminium slides, sealed on the two planar faces by 22x22 mm glass coverslips welded by high vacuum grease (Sigma-Aldrich).
Values were considered positive when the edge moved away from the centroid and negative when it moved closer. For comparisons between independent cells, actin and spectrin intensity measurements were normalized to 1 at null speed. Results were analyzed and plotted with R and the ggplot2 R package. Correlated Spectrin and actin flow velocity analysis (PIV) Correlation between speed and directionality of the two fluorescent channels have been performed on the same live TIRFM datasets during the spreading analyzed for cell edge dynamics. The signal background was subtracted. Particle Image Velocimetry (PIV) was performed independently on single fluorescent channels by a custom macro in Fiji, excluding the portion of the cell close to the edge (50 pixels) and the frames corresponding to the initial spreading phase P1 from the analysis (50 frames).
Addition of media exceeded the volume of the imaging chamber to avoid dilution of final drug concentrations and rebound effect. Specifically, 1µM Latrunculin A, 10µM Blebbistatin, 5 µM Nocodazole and 5mM Diamide were singularly used (Sigma-Aldrich). Perturbed cells were then imaged for 30 minutes at 1-5 minute/frame. Intensity calculations were carried out in Fiji by subtracting the background and creating a dynamic binary mask of the GFP-βII-Spectrin signal; the built-in "Analyze particle" tool was applied to obtain mean fluorescence intensity values at different time points. Only untreated, 5 and 30 minutes after treatment time points were plotted using the software Graphpad Prism. For experiments that required osmotic shocks, MEFs cells were treated following the same procedure described earlier for cytoskeletal perturbations.
Cell stretching Cell stretching experiments were performed using an automated cell stretching dish (International patent: WO 2018/149795 A1). The components of the cell stretching dish were designed using SolidWorks software and 3D printed using a stereolithography-based 3D printer (Form 2, Formlabs) coupled with an autoclavable and biocompatible dental resin (Dental SG resin, Formlabs). The printed parts were rinsed in isopropyl alcohol for 5 minutes to remove any uncured resin from their surface, and then post-cured in a UV box to finalize the polymerization process and stabilize mechanical properties. The printed parts were then polished and assembled to create the lower (cell chamber) and the upper portion (aperture driver) characterizing the stretching dish. Before the experiments, the components of the lower portion were autoclaved to be sterilized and assembled to clamp a deformable silicone membrane (thickness 0.
Cell compression Cell compression experiments were performed using a cell compression device (International patent: WO 2019/086702 A1) capable of applying dynamic compression stress to single cells. The main components of the compression device were designed using SolidWorks software and 3D printed using a stereolithography-based 3D printer (Form 2, Formlabs), following the same procedure described in for the cell stretching device. The cell compression device consists of an air chamber connected to an air pressure regulator. Before the experiments, a Polydimethylsiloxane (PDMS, Sylgard 184) piston was microfabricated to have circular pillars (200 µm in diameter) on its surface, and attached to a deformable silicone membrane (thickness 0. 005f, SMI) through plasma bonding. The membrane with the piston was then clamped to the air chamber.
This first step hardly caused a reaction monitorable by TIRF at basal cell level but allowed consistency between independent experiments, for this reason six different cycles of compression (2 minutes) and relaxation (2 minutes) were performed at increasing pressure, as schematized in D; the 2 minutes compression step duration was chosen to allow adaptive mechanisms to occur and avoid longterm detrimental effects on cell integrity. Intensity calculations were carried out in Fiji by subtracting the background and drawing ROIs across the perinuclear rim formed during compression. Pre-stretch mean intensity for each single compressive events was divided by maximal mean intensity registered during the subsequent 2 minutes compressive step. Data were plotted using the software Graphpad Prism.
5 seconds for PE-only). FRAP data were analyzed and curves fitted to the oneexponential recovery equations (one-phase association) by the software Graphpad Prism: I= I 0 +Imax*[1-e -(k)*(t) ] Where I represents the relative intensity compared to the pre-bleach value, k the association rate, and t the half time recovery expressed in seconds. For the dual FRAP assay, cells were seeded on a fibronectin-coated glass coverslip and mounted on the 2way imaging chamber that allows on-stage media exchange as previously described for spreading assay and osmotic shocks. The first FRAP measurements were conducted in serum-free 1x Ringer solution; at completion, the media was exchanged and independently supplemented with the cytoskeletal drugs at the concentration used before. Specifically, 1 µM Latrunculin A, 10 µM Blebbistatin, 5 µM Nocodazole and 5 mM Diamide (Sigma-Aldrich).
Raw images of the two clusters were then independently stacked and z-projected for median intensity values. Gaussian blur filter (1 pixel radius) and a scale factor of 10 were applied to the projected images to homogenize the signals. Scaled-projected images of the two particle-size groups were then matched for signal intensities between corresponding channels. Normalized radial plots were generated by the Radial Profile plugin (FIJI) on the final 2x2 µm projected images. For live imaging datasets, Unsharp Mask (1 pixel radius, 0. 6 weight) and Gaussian blur filter (1 pixel radius) were applied to the raw images before ROIs selection. No additional filters were applied to the final projected images. Similar filtering strategy was applied in live datasets during osmotic shocks.
Acknowledgements We are grateful to the IFOM imaging facility personnel, in particular  D. Parazzoli ,  M. Garre and  E. Martini  for technical support. IFOM cell culture facility and qPCR facility personnel. We thank  Jagadish Sankaran  (NUS) and  Toh Kee Chua  ( NUS ) for the preliminary experiments of this project. We thank MBI protein core facility personnel, in particular  Chen Hongying  for providing the constructs. We thank  Paulina Nastaly  ( IFOM ) for the help with micropatterning. We thank  Mike Sheetz  (MBI),  Marco Foiani  (IFOM),  Sarah Barger  ( NYU ) and all the members of Gauthier's, Scita's and Maiuri's groups for helpful discussion. We thank  Sara Sigismund  ( IEO ) for kindly providing additional constructs. This work was supported by  IFOM starting package, Mechanobiology Institute of Singapore  grant  WBS R-714-016-007-271 , and the  Italian Association for Cancer Research (AIRC) ,  Investigator Grant  (IG)  20716  to NCG, by H2020-MSCA individual fellowship to AG (796547) and by  Fondazione Umberto Veronesi  (FUV) doctoral fellowship for CG.