dataesr/scientific-paragraphs-categorization huggingface dataset:
Ontology FAIRness Assessment with O'FAIRe: AgroPortal implemented the Ontology FAIRness Evaluation (O'FAIRe) methodology  [6, 42]  in a tool that automatically assesses the level of FAIRness -i. e. , to which degree a digital object adheres to the FAIR principles-of semantic artefacts within the portal. The assessment relies on 61 questions answered using the extended ontology metadata or the portal's own services. When working on O'FAIRe, we demonstrated the importance of relying on ontology repositories to harmonize and harness unified metadata and thus allow FAIRness assessment. Extended SKOS Support: AgroPortal added new functions to support SKOS resources as the standard OntoPortal code is still limited  [43]. The new functions handle and represent SKOS concept schemes, collections and SKOS-XL elements if used in the displayed thesaurus.
OntoPortal Open-Source Project Organization The OntoPortal Alliance has a main goal of synchronizing and sharing research and development efforts. The group's motivations are: (i) to represent OntoPortal adopters and end users; (ii) to maximize the OntoPortal state-of-the-art service portfolio; (iii) to improve OntoPortal software while managing several parallel and different installations; (iv) to increase semantic uptake in science communities; and (v) to increase the ecosystem's long-term support. The Alliance is committed to be an open community and is working to ease participation by providing installation and deployment procedures, detailed documentation, and in the future training and tutorials for all stakeholders. We are spending a considerable amount of time to create a resource for the community and support it (e.
g. , average of 4 support emails per day for BioPortal, 3 per week for AgroPortal) and document it. In 2022, we launched an annual 3-day-workshop  [44]  that we see as key to fostering our growing community. The Alliance is setting-up three documents (https://ont oportal. github. io/documentation) to reach multiple stakeholders: (i) A user guide documents domain-and portal-specific capabilities. This targets OntoPortal end users-either ontology developers who want to host an ontology on one of the portals, or users who want to access and reuse ontologies. The user guide can be specialized by each project in the Alliance to adjust to specific needs of a community or to document a portal specific feature. (ii) An admin guide documents how to set up the system and manage the content. This is typically addressed to the technical person involved in deploying, running and monitoring the server but also to the content administrator who will supervise the semantic artefacts loaded, perform artefact curation, and provide outreach to the end users.
(iii) A developer guide documents how to develop new features and make contributions to the core technology, thereby sharing work back with the rest of the Alliance. The code packaged and running within the appliance is available on the OntoPortal GitHub (https://github. com/ontoportal) and licensed BSD-2, so every administrator or developer can easily get the relevant branches or forks and redeploy the code in the appliance. We strongly encourage Alliance partners (and other open-source contributors) to fork the OntoPortal GitHub repositories to enable traceability and collaborative contributions via their pull requests. Besides code sharing, we use GitHub for issues, discussions, decision-making and overall project management. Wherever feasible the OntoPortal project follows best practices for developing and supporting open-source projects.
Current Open Domain or Project Specific OntoPortal Installation In September 2022, we conducted a survey among the 10 main Alliance participants to date (Table  3 ). We obtained a sense of a typical OntoPortal installation: a public (open and free) community repository where anyone can contribute ontologies, with on average 50/60 ontologies, of which more than 50% are exclusive to that repository (that is, unavailable in any other OntoPortal repository). The content is generally multilingual (77%)-despite a lack of support for this in the core software-and mostly in OWL or SKOS format. Ontologies are mostly added by content administrators performing significant or moderate content curation, even when end users can also add ontologies. This tends to change with broader adoption of the portals.
Other Running Installations of the OntoPortal Technology Beyond the domain-specific portal reuses in the Alliance, the OntoPortal technology is deployed by many external parties with other objectives. For instance, hospitals reuse the technology in-house to use services such as the Annotator on sensitive data. In the past, those uses of the OntoPortal technology were hard to track since users provide no feedback or report to the OntoPortal providers unless they need explicit support. Through 2015, the virtual appliance file was downloaded or deployed from Amazon Machine Images more than 140 times. Since version 3. 0 of the OntoPortal software in 2020, the appliance incorporates a "call home" feature and a free registration solution that together help track the number and status of other OntoPortal installations.
On our side, we will work to provide the best possible federation of our portals. Our challenge is to better coordinate to be sure semantic artefact metadata and versions are synchronized, ontology developers are aware where their resources are deployed (without having to explicitly deal with multiple portals), and our services federate results of services like search, annotation and recommendations. In the context of FAIR-IMPACT, a Horizon Europe project within the European Open Science Cloud, some members of the Alliance and other parties are investigating the life cycle of FAIR semantic artefacts. We are reviewing governance models for semantic artefacts and discussing the role the catalogues have to play in this governance. Within this project, we are also building on the Metadata for Ontology Description and Publication initiative (https://github.
com/FAIR-IMPACT/MOD) to provide a DCAT-based vocabulary to describe semantic artefacts and their catalogues. We expect to make these descriptions available via a standard application programming interface that ontology repositories and semantic artefact catalogues, including the OntoPortal technology and also extending beyond it, can implement to improve their interoperability and ease the reuse of their resources. The OntoPortal Alliance has many other development opportunities beyond the ongoing tasks described above. Each member of the Alliance brings unique vision and potential for improvements to the software. These improvements will make the OntoPortal systems steadily more interoperable, more interconnected, more powerful, and easier to install, operate, and update. However, these development opportunities are not without challenges.
Creating a common open-source project, and an emerging organization and governance model to coordinate changes and evolutions, only begin the work needed for a robust collaborative software capability. Achieving successful results require a combination of factors: community commitment to a common, yet improvable, technical solution; technical approaches for accepting modifications that allow each participating organization autonomy to select its own system configuration; funding commitments that allow both the OntoPortal Alliance and its contributing members to thrive; and continuing buy-in to the common mission of the Alliance.
Resource Availability Statement • Source code for OntoPortal is available here: https://github. com/ontoportal and documentation is centralized here: https://ontoportal. github. io/documentation. • The product and the Alliance are publicly and generally presented in the OntoPortal website here: https://ontoportal. org. • License terms are described here: https://ontoportal. github. io/documentation/admini stration/general/licensing. • OntoPortal support can be reach at support@ontoportal. org. • Each specific OntoPortal instance project can be contacted via their specific portal listed in Table  3. Those are generally public, open source and provide free support as well as the code on GitHub e. g. , https://github. com/ontoportal/ontoportal_web_ ui/forks. • Release notes are usually available in forked GitHub repositories.
Acknowledgement. This work has been supported by the  D2KAB  project ( ANR-18-CE23-0017 ), the  French NUMEV-IRT OntoPortal Alliance  ( ANR-10-LABX-20 ), the  Horizon Europe FAIR-IMPACT project  (grant # 101057344 ) and the  U. S. National Institutes of Health BioPortal   U24 grant  ( 1U24GM143402 ). The Alliance is also supported by the research bodies and organizations of its members. We thank and acknowledge here the discussions, engagement and participation of the Alliance members and any external parties that provide contributions or feedback on our technology or our running OntoPortal installations.
Industry and ENIT 2022 enit. fr manufacturing Managing the proliferation of semantic artefacts in the industrial domain, and evaluating the level of FAIRness of artefacts. Supported by H2020 OntoCommons, an EU project to standardize industrial data documentation through harmonized ontologies. Strong motivation on making things interoperable, aligning the semantic artefacts and ensuring their FAIRness VODANA (to come) Health/COVID VODAN Africa 2021 Advancing tracking and management of COVID-19 in Africa. VODAN Africa teams have adapted OntoPortal for redistribution to health centers and clinics throughout Africa, serving country-specific vocabularies from locally managed repositories. The VODANA project researched and demonstrated the best ways to package OntoPortal as part of an application set installed on pre-configured laptops BiodivPortal (to come) Biodiversity NFDI4Biodiversity 2023 NDFI4biodiv is a node of the NFDI infrastructure projects in Germany.
Modeling of the Interaction between WN170, WN191, and WN198 and Top1 As no experimental structures of complexes were available, we designed an approach to understand how our putative ligands could bind to Top1 and could intercalate DNA. The starting point was the crystal structure of human DNA Top1 in complex with topotecan poison (Figure  6A ) (PDB ID: 1K4T, 2. 1 Å). Based on the coordinates of the poison, we subsequently modeled the interaction of WN170, WN191, and WN198 by molecular docking. The full procedure of molecular modeling is detailed in Section 4. 7. The starting point was the crystal structure of human DNA Top1 in complex with topotecan poison (Figure  6A ) (PDB ID: 1K4T, 2. 1 Å). Based on the coordinates of the poison, we subsequently modeled the interaction of WN170, WN191, and WN198 by molecular docking.
Cell Viability Assay Cell viability was determined using the CellTiter 96 ® AQ ueous One Solution Cell Proliferation Assay (MTS test, Promega, Charbonnières-les-Bains, France). Cells were seeded in 96-well plates at a density of 2. 10 3 for 24 h before treatment with 0 to 100 µM of WN198, WN191, WN170, WN197, or cisplatin for 72 h. Cells were incubated for 2 h with 20 µL of CellTiter solution at 37 • C in 5% CO 2 , and the production of formazan from reduced 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) was measured at 490 nm (SPECTROstar Nano, BMG LABTECH, Ortenberg, Germany). GraphPad Prism V6. 0 software served to calculate IC 50. Statistical differences between WN198 and WN191 were ascertained by a Student's t-test (* p < 0. 05, ** p < 0.
Molecular Modeling Receptor structure was the crystal structure of human DNA Top1 in complex with topotecan poison (Figure  4A ) (PDB ID: 1K4T, 2. 1 Å). All ligands and water molecules were removed except DNA. Simulation of the binding modes of the receptor and the designed compounds was performed using the GOLD docking program  [60]. GOLD is based on a genetic algorithm and considered the ligand as flexible, while side chains of most residues were kept rigid. For the search procedure, a sphere of 30 Å was centered on the X, Y, and Z coordinates of the topotecan poison (Origin = 22. 7 -9. 4 26. 9). Twenty conformations were generated for each ligand using default parameters. Then, all the different binding poses were scored with the ChemPLP scoring function. Subsequent energy minimization was performed using the AMBER forcefield.
DSC calorimetry Microcalorimetry experiments were performed using a Nano DSC calorimeter (model 602000) from TA instruments with a sample/reference cell volume of 0. 3 ml. The oligonucleotides were prepared in K-RB buffer, pH 6. 54 at approximately 60 M strand concentration. CD spectra of samples were taken before and after DSC measurements to rule out any DNA contamination or degradation. Buffer and sample solutions were degassed for 10 min prior to loading into DSC cells. The experiments were performed at a constant pressure of 6 atm. The excess heat capacity is related to oligonucleotide strand concentration. The samples were first pre-incubated in the DSC cell for defined periods of time at defined temperatures (both data specified in figure legends), then rapidly cooled down to 1 • C and finally the thermograms were recorded in the range of 1-75 • C for both the heating and cooling direction using a scan rate of 1 • C/min.
The basic analysis of DSC scans was performed using NanoAnalyze Software (version 3. 7. 5 from TA instruments). First, heating/cooling buffer scans were subtracted from the corresponding sample scans. These DSC scans (representing heat flux [in watts] in the sample cell against temperature) were then normalized concerning scan rate and oligonucleotide concentration to obtain dependences of molar heat capacity [in kJ/(mol. K)] on temperature. Afterward, a manually defined polynomial baseline was subtracted. Finally, thermodynamic parameters were computed for all transitions (DSC bands) by directly integrating molar excess heat capacity against temperature using the conventional relations for enthalpy and entropy without the need to use a particular thermodynamic model. The transition (melting) temperature for each DSC band was then determined as the temperature corresponding to the maximum band signal.
ACKNOWLEDGEMENTS We acknowledge core -facility [Josef Dadok NMR Centre] of CIISB, Instruct-CZ Centre , supported by  MEYS CR  ( LM2018127 ) and  European Regional Development Fund-Project  ' UP CIISB ' (No. CZ. 02. 1. 01/0. 0/0. 0/18046/0015974 ) for access to research infrastructure. J. L. M. would like to dedicate this MS to the memory of  Prof. Michel Rougée , a pioneer in the analysis of hysteresis of nucleic acids.
FUNDING Czech Science Foundation  [ 19-17063S  to M. V. ,  19-26041X  to L. T. and M. G. ];  SYMBIT  project  [CZ. 02. 1. 01/0. 0/0. 0/15003/0000477 ] financed by the  ERDF  and  ANR grant  [ ANR-21-CE44-0005-01 ] 'ICARE'. Funding for open access charge:  SYMBIT  project  [CZ. 02. 1. 01/0. 0/0. 0/15003/0000477 ] financed by the  ERDF.
■ METHODS Cloning. The plasmid harboring wild-type Tsa1 (pET19b-Tsa1; corresponding to UniProt entry P34760) was originally obtained with an amino-terminal deca-histidine-tag in a codon-optimized manner from GenScript (kind gift from P. O. Widlund/T. Nystrom). A S78D   A, B ) Density of maximally frustrated contacts in human PRDX (A) in the oxidized and reduced states (PDB 5IJT 7 and 7KIZ, 50 respectively) and the Tsa1 mutant C47S that mimicks the reduced state (PDB 3SBC  94  ), plotted along the sequence position. The data have been obtained with the Frustratometer Web server. 95, 96 The solid line shows the average value over all available data in the decamer, and the shaded area denotes the range between the highest and lowest value found. (C, D) Clusters of maximally and minimally frustrated contacts, shown by red and green dashed lines, respectively, on the structures of human PRDX ox (C) and Tsa1 C47S.
57 ll stable isotopes were purchased from Merck. Solution NMR Spectroscopy. NMR measurements were performed on a Bruker Avance III HD 700 or 800 MHz spectrometer, using Topspin 3. 5 software and equipped with either TCI or TXO cryogenically cooled triple resonance probes. All experiments were performed either in 50 mM KPi, 50 mM KCl, pH 7. 4 buffer or PBS pH 7. 4 at the indicated temperatures. For the sequence specific backbone resonance assignments of [U-2 H, 15 N,  13  C] Tsa1 S78D the following TROSY-type experiments were recorded at 37 °C: 2D [ 15 N, 1 H]-TROSY,  23  3D trHNCO, 3D trHNCA, 3D trHNCACB, 3D trHNCOCACB. 58 Complementary sequence specific backbone and side-chain assignments of [U-15 N,  13  C ] Tsa1 S78D were recorded: BEST-type triple resonance experiments 3D BT-HNCA+, 3D BT-HNCO,  59  BT-HNCACB+,  59, 60  and CBCA(CO)NH.
69 Secondary chemical shifts were calculated relative to the random coil values calculated by the POTENCI program. 70 Methyl group assignment was performed using the MAGIC algorithm  71  and confirmed by a combination of manual analysis of the side-chain TOCSY, HMBC-HMQC, and NOESY data sets. This approach yielded the following degree of assignment (∼92%): Ile δ1 (13/13), Leu δ1, δ2 (30/34), and Val γ1, γ2 (34/36). Translational diffusion coefficients were measured by recording a series of 1D  13  C-edited DOSY spectra at different temperatures ranging from 25 to 60 °C, using a pulse scheme ( 13 C -edited BPP-LED  72  ) that is similar to a 15 N-edited BPP-LED experiment with  15  N and  13  C pulses interchanged. 73 The gradient duration δ was adapted to 3. 2 ms instead of 4. 8 ms used in the  15  N-filtered version, and a diffusion delay T of 400 ms and a τ of 0.
89 onlinear least-squares fits of relaxation data were done with NMRFAM-Sparky 1. 47. 90 Side-chain dynamics experiments were performed on a [U-2 H,  15  N, Ile-δ1-13 CH 3 , Leu-, Val-  13  CH 3 ] labeled Tsa1 S78D sample at 37 °C in 99. 9% D 2 O based NMR buffer. Side chain methyl order parameters (S 2 ) were determined by a relaxationviolated coherence-transfer triple-quantum experiment. 91 The buildup of triple-quantum (3Q) coherence was monitored in a series of 1 H-  13  C experiments at different delay times (13 values from 1 to 35 ms). The decay of single-quantum (SQ) coherence was followed in a set of reference experiments at the same delays. Ratios of the peak intensities of these two sets of experiments were fitted to obtain sitespecific order parameter S 2 using the separately determined (TRACTapproach) overall correlation time τ c in 100% D 2 O of 27 ns.
A series of 2D spectra with spin-lock durations ranging from 1 to 50 ms was recorded at RF field strengths from 10 to 41 kHz. This experiment detects μs-ms motion involving bond reorientation. 35, 36 All experiments are implemented in NMRlib. 80 he analysis of the NERRD data was performed using the pyDIFRATE program, a python-based tool for detectors analysis (github. com/alsinmr/pyDR). 45 We used either three or four detectors, both of which give similar results (see Figure  3 ). Given the large NERRD effects for almost all residues, which extends to even below 10 kHz, the analysis of Bloch-McConnell relaxation-dispersion (BMRD) data involved a correction for the NERRD effect, as outlined in Figure  S13. In brief, the detector parameters were used to back-calculate R 1ρ rate constants at all RF fields used in the BMRD experiment, and subtracted from the experimental rate constants.
These rate constants are close to zero. Analysis of the nonflat profiles (shown in Figure  4 ) was done with the program relax,  92  used on the NMRbox server. 93 As the program cannot handle negative relaxation rate constants, which result from the above-described subtraction procedure, a constant value (5 s -1 ) was added to all data. This plateau value is of no influence for the data analysis, because in the fit procedure the plateau value is fitted and generally not interpreted. Preparation of Reduced Tsa1 Samples. In order to obtain a reduced sample, 2. 1 mg of [U-2 H, 15 N,  13  C] Tsa1 WT were incubated at 30 °C for 30 min with 5 mM DTT. The protein solution was quickly centrifuged at 10,000g in a benchtop centrifuge to pellet down eventual aggregates. Protein supernatant was sedimented into a 1.
3 mm rotor overnight by ultracentrifugation at 50,000g, 6 °C. The protein sample showed stability in a reduced state for several weeks, as evidenced by 2D hNH MAS NMR experiments. Size-Exclusion Chromatography-Multiangle Light Scattering. SEC-MALS experiments were performed using a Superdex Increase 200 10/300 GL column (GE Healthcare) on an Agilent 1260 HPLC Infinity II in phosphate buffer (PBS or 50 mM KPi KCl pH 7. 4) at RT (ca. 297 K). Protein elution was monitored by three detectors in series namely, an Agilent multiwavelength absorbance detector (absorbance at 280 and 254 nm), a Wyatt miniDAWN TREOS multiangle light scattering (MALS) detector, and a Wyatt Optilab rEX differential refractive index (dRI) detector. The column was pre-equilibrated overnight in running buffer to obtain stable baseline signals from the detectors before data collection.
Molar mass, elution concentration, and mass distributions of the samples were calculated using the ASTRA 7. 1. 3 software (Wyatt Technology). A BSA solution (2-4 mg/mL), purchased from Sigma-Aldrich and directly used without further purification, was used to calibrate interdetector delay volumes, band broadening corrections, and lightscattering detector normalization using standard protocols within ASTRA 7. 1. 3.
Data Availability Statement The NMR chemical shift assignments of Tsa1 in the decameric oxidized state, decameric reduced state by MAS NMR and of Tsa1 S78D by solution-state NMR have been deposited in the BioMagResBank (accession numbers 51788, 51825, 51943). Raw data and fitted parameters ( 15 N MAS NMR and solution NMR relaxation data, methyl order parameters of Tsa1 S78D , relaxation-dispersion data, 2D spectra and analysis scripts/ jupyter notebooks) are deposited at the IST Austria Research Explorer data repository and are available at https:// www. doi. org/10. 15479/AT:ISTA:12820.
Introduction In the last several years, cryo-electron microscopy (cryo-EM) has been pushing the boundaries of structural biology in terms of structure resolution, system complexity, and macromolecular size  1, 2. Imaging single particles by rapid cryo-cooling and vitrification  3, 4  enables structural studies under near-native environmental conditions while offering sample protection from beam radiation. Technical advances in electron detectors, computational algorithms accounting for beam-induced motion, and automation of data collection and image analysis  5  have paved the way to a spectacular increase of high-resolution cryo-EM density maps. The Electron Microscopy Data Bank (EMDB) currently holds 15,800 single-particle cryo-EM density maps, which offer exquisitely detailed structural information about macromolecular systems of central importance in cell biology.
Recent developments in identifying and automating the search for appropriate CVs have increased the efficiency of this method, thus providing a remedy to the conformational sampling problem  [55] [56] [57] [58] [59]. While metainference has been combined already with metadynamics 60 , this was not the case for EMMI (Figure  1A ). Combining EMMI with enhanced sampling methods can lead to accurate and efficient determination of structural ensembles using the vast dataset of cryo-EM database, which can in turn provide atomistic insight to a range of biomolecular systems and processes. In this work, we present the MEMMI method, which incorporates metadynamics in EMMI in order to accelerate the ability of EMMI to sample structural ensembles with slowly interconverting states. We illustrate the application of this approach to determine the structural ensemble of an amyloid fibril formed by the full-length (residues 1-37) islet amyloid polypeptide (IAPP), an aberrant assembly associated with the degeneration of pancreatic bcells in type-2 diabetes (T2D).