dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work is a part of a project that has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (CartographY ; grant agreement ID  804752 ). A. J. L. , G. R. D. , and W. J. C. acknowledge the support of the  Science and Technology Facilities Council. D. H. acknowledges support from the  Alfred P. Sloan Foundation , the  National Aeronautics and Space Administration  ( 80NSSC19K0597 ), and the  National Science Foundation  ( AST-1717000 ). M. B. N. acknowledges support from the  UK Space Agency. R. A. G. acknowledges the funding from the  PLATO CNES  grant. We acknowledge the use of the and packages to produce figures in this work. We thank  Ilya Mandel  and  Sean Matt  for their discussion regarding this We also thank the referee for their immensely helpful comments which have improved the paper.
IV. DATA ANALYSIS In order to obtain reliable values of the corresponding appearance energy (AE) for positive ions from nimorazole ionization measurements, we implement a similar procedure as thoroughly presented and discussed in the study by Meißner et al. ,  45  and therefore, only a brief description is given here. The AE values are obtained by fitting the ion efficiency curves with a modified Wannier type threshold law  46  including a Heaviside function θ of the type f (E) = b + c ⋅ (E -AE) n θ(E -AE), (1) where b is a constant background signal, c is a scaling constant, E is the electron energy, AE is the respective threshold energy, and n is the exponential Wannier factor. The Heaviside function θ(E -AE) is equal to 0 for E ≤ AE and equal to 1 for E > AE. The fitting input parameters are related to the energy region around the estimated AE at a given resolution, and the number of iterations depends on the number of data points, being higher for larger datasets.
For each iteration, the region around the obtained AE value will become narrower, and so either the set number of iterations is reached or otherwise the change in the fitting range falls below a pre-set input value. The energy scale calibration was performed with the well-known ionization energy of neon. The uncertainties of the AE values mentioned in the text and Table  II  were derived by combining the calibration error (±10 meV) and the statistical uncertainty of the individual measurements. Note that the energy resolution of the HEM and the error on the AE value are two different parameters. Finally, for cations showing more than one AE, the number of thresholds is a further input parameter. The first onset is fitted as described above, and subsequently, the fit is subtracted from the data and the same method is applied on the next threshold.
In this context, we note that the mass spectrum available through the NIST database  48  shows only a mass  scan limited to the mass range of 40 u-180 u. Additional fragment ions with lower yields appear in the mass spectrum of Fig. 5 , but their experimental AEs will not be addressed here. Figure  6  shows the ion yield curves close to threshold for six of the investigated cations (226 u, 180 u, 100 u, 56 u, 42 u, and 28 u) as a function of the electron energy, while The experimental threshold for reaction (8) yielding the positive ion (NIMO-NO 2 ) + at 180 u is 8. 33 ± 0. 01 eV (Table  II ). The small difference of 0. 17 eV to the AE of the parent cation is notable since the bond dissociation energy of C-NO 2 in NIMO is 3. 5 eV. Thus, we may tentatively suggest that this cation, which is an even electron species, may be formed through simple cleavage of the C-NO 2 bond and subsequent H transfer to the carbon site of the imidazole ring where NO 2 was ripped off.
Nearly all fragment anions show a dominant peak between ∼3 eV and 3. 5 eV, which can be assigned to a common state of the transient negative ion. In their calculations for the nitroimidazole molecule, Kossoski and Varella 47 obtained the π * 3 resonance close to 3 eV, which may decay in a large variety of fragment anions as observed in the present study. We have also investigated electron ionization close to threshold and measured the ion yield of the NIMO parent cation and five different fragment cations in the electron energy range from about 7. 2 eV up to 17 eV, with some of the data supported by quantum chemical calculations. A close comparison of the mass spectrum with the available information The Journal of Chemical Physics ARTICLE scitation. org/journal/jcp from the NIST database 48 revealed that electron ionization yields are not limited to the mass region of 40 u-180 u as suggested by the database spectrum.
ACKNOWLEDGMENTS This work was supported by the  FWF, Vienna  (Grant No. P30332 ). R. M. and P. L. -V. acknowledge the  Portuguese National Funding Agency FCT  through Grant No. PD/BD/114452/2016  and Research Grants Nos. CEFITEC ( UIDB/00068/2020 ) and  PTDC/FIS-AQM/31281/2017. This work was also supported by  Radiation Biology  and  Biophysics Doctoral Training Programme Grant  Nos. (RaBBiT,  PD/00193/2012 ) and  UCIBIO  ( UIDB/04378/2020 ). L. F. is thankful for the support from the  University of Innsbruck  via Grant No. P7440-035-011  and the  Institut de Physique des 2 Infinis  and acknowledges the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  for its financial support within program " Investissements d'Avenir ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
How to understand better the knowledge provided by Google results to build future "smart vehicle-centric" applications? What is the knowledge expertise required to build a smart vehicle application (e. g. , driver assistance system)? Automotive companies (e. g. , Toyota, BMW, Renault) are employing Internet of Things (IoT) and Semantic Web technologies to model the automotive sector. We aggregate this "common sense knowledge" in an automotive dataset which comprises 42 semantics-based projects between 2005 and 2019. The knowledge is already encoded with knowledge representation languages (e. g. , RDF, RDFS, and OWL) and supported by the World Wide Web Consortium (W3C). However, only a subset of those projects share their expertise by publishing their ontologies online. For this reason, at the current time of writing, only 16 ontologies are processable.
Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology analyzes what are the most popular terms required to build a smart car, it provides: 1) a set of keyphrase that are synonyms to smart cars to find domain-specific knowledge, 2) synonyms are used to build a corpus of scientific publications to train the k-means machine learning algorithm, 3) a dataset of smart car ontologies that we collected, is analyzed by the k-means algorithm, and 4) the extraction of the most common terms from the ontology dataset for the automotive sector. Our KEAS findings can be used as a starting point for further domainspecific investigations (e. g. , Volvo willing to integrate semantic web) and for future information extraction from structured knowledge.
The survey  [7]  compares 11 ontologies according to 7 criteria: 1) Precision (relationship diversity, axiom complexity), 2) Evaluation, 3) Knowledge management services, 4) Generality, 5) Granularity, 6) Competence, and 7) Span. We designed the "semantic-based IoT smart vehicle" LOV4IoT dataset thats collects common sense knowledge for the automative sector. We classified 42 projects between 2005 and 2019 since they claim that the knowledge is already encoded with knowledge representation languages (e. g. , RDF, RDFS, and OWL) and supported by the World Wide Web Consortium (W3C). However, only a subset of those projects share their expertise by publishing their ontologies online. For this reason, at the current time or writing, our dataset comprises only 16 processable ontologies.
-RQ2: What are the most used entities (e. g, concepts, instances) within those ontologies? Statistical methods can help to achieve this task. Contributions are as follows: Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology understands the "common sense knowledge" required to build smart vehicle applications which provides: 1. C1: A set of keyphrase synonyms for the smart vehicle domain to find domain-specific knowledge in past or current projects that published their results within scientific publications, 2. C2: Synonyms are used to build a corpus of scientific publications to train the k-means machine learning algorithm, 3. C3: A dataset of smart car ontologies is built and analyzed by the k-means algorithm to cluster knowledge, and, 4. C4: The extraction of the most common knowledge for the automotive sector.
Knowledge Extraction for the Automotive Sector (KEAS) Methodology The long-term vision of the Knowledge Extraction from IoT-related ontologies project is depicted in Figure  1. In this paper, we are focused on the Ontology code extraction algorithm and the Ontology Dataset components applied to the smart vehicle domain. [58]  2005 Ontology of transportation networks Table  2. Other Ontology-based IoT automotive projects that cannot be employed since ontologies are not available (even upon request). Legend: Ontology Availability (OA) has been search on Google and Google Scholar a set of specific keywords, as an example those keyphrases: 1) start with ontology-based, 2) finished with ontology, or 3) start with semantic-based. Keyphrases are as follows: - For the set of scientific publications, we focused on the following criteria: -Are ontology URLs available within the scientific article? Frequently, URLs are missing.
Authors have been contacted to retrieve ontology code and we enriched the dataset when receiving positive answers. 1 summarizes the 16 ontologies that share their ontologies online, which is the smart vehicle ontology dataset later analyzed. Other ontology-based projects are referenced in Table  2 , unfortunately, the ontologies cannot be processed yet since they are not accessible. -Are sensors mentioned within the paper? -Are there reasoning mechanisms and already defined rules to interpret data generated by the smart vehicle applications? -Is the reference section provide more resources to investigate? We enrich our scientific publication dataset accordingly (e. g. , LOV4IoT-transport knowledge repository). The main difference between our survey and the existing ones, is that our survey is the result of a continuous enrichment of the LOV4IoT ontology catalog since 2012 and we provide tools to support the reuse of the survey outcome (e.
Building the corpus of knowledge for the transportation domain To train the dataset, we need to build a corpora of knowledge for the transportation domain. word2vec helps in transforming texts from either scientific publications or ontologies into vectors that can be processable by machine learning algorithms. word2vec performs the training of the term embeddings and the process of building a word2vec model for all identified unique words. The word2vec algorithm is based on neural networks and builds a vocabulary from a pre-training text model and attaches the vector representations to each word. Around 20 of the terms were not part of the pre-trained model thus we removed those terms from the list of words. The output of this step is the word embedding vector space representation. The genism python library is used to implement word2vec.
Transport Ontology Dataset: We have collected 16 ontologies that can be downloaded and analyzed (as depicted in Table  1 ): 2 ontologies are excluded since they were in Chinese or German. We released the list of ontology URLs within an online table  18. For the convenience of the developers, we created a tutorial web page (http://lov4iot. appspot. com/?p=queryTransportOntologiesWS) to either use the web service or easily download the dump of the ontology code that we collected. For instance, the developer can query the web service http://lov4iot. appspot. com/perfectoOnto/getOntoDomain/?domain=Transportation which returns the list of the projects relevant for the smart car domain that we collected within the LOV4IoT ontology catalog for transport  19  , it includes: the name of the project and the ontology, the ontology URL, and additional information such as the scientific publication describing the ontology (see Figure  2 ).
-Domain experts involved (not computer scientists) since they share their human expertise. -Ontology code that can be downloaded, because, in science, the experiments should be replicable, following the FAIR (Findability, Accessibility, Interoperability, and Reuse) principles. Ground Truth Dataset Design: Domain experts can participate in the questionnaire to design the ground truth (a similar questionnaire for smart cities, weather, and smart home is available online 21 , see  [62] ) for detailed information). Experts were either involved in developing smart car ontologies or are an open audience having the domain expertise to describe each ontology using three keywords. The participants' level of expertise in the automotive domain and knowledge engineering, is asked in a Likert scale of five levels, from 'totally.
Conclusion and Future Work The Systematic Literature Survey (SLS) in any research topics is a time-consuming approach. Finding knowledge returned by Google results still require a huge work on learning, classification and summarizing. To ease this time-consuming task, we built a "common sense knowledge" dataset for the automotive sector comprising 42 projects between 2005 and 2019. However, only 16 ontologies are processable and published online with knowledge representation standards. Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology aims to analyze the most popular knowledge required to build smart vehicle applications by applying the k-means machine learning algorithm to a dataset of 16 ontologies that we collected. This work highly encourages researchers to share their reproduceable experiments by publishing online their smart vehicle ontologies.
, 2021). How does one get fluctuations of sufficient amplitude to trigger these transitions? Let us take a closer look at fluctuations in our von Kármán flow. They are shown in Fig. 11a , from data issued from a direct numerical simulation of our experiment by Hugues Faller and collaborators, using the SFEMANS code  (Cappanera et al. , 2020). Large velocity fluctuations are primarily observed at two locations: one close to the "corner" of the experiment (i. e. close to the impellers and the outer wall) and the second at mid-height. The first location obviously corresponds to the fluctuations induced by the moving boundary conditions which are then advected by the mean velocity of the spring state of Fig. 3a. The second location is associated with a strong shear layer resulting from the counter-rotation of the two main toroidal cells of the spring state.
Extension of Liu's method for Gaussian beams truncated using circular apertures As previously mentioned, an Airy disk is the diffraction pattern obtained at the focal position of a lens of limited aperture when irradiated by a uniform plane wave. In practice, this corresponds to a perfectly top-hat beam facing the lens, or the use of a circular aperture much smaller than the size of a nearly-Gaussian beam. Therefore, in this section we show the calculations and corrections factors to account in the Liu's method for more realistic cases using truncated Gaussian beams with circular apertures at focusing lens position. For obtaining the diffraction pattern for different truncation conditions we rely on the PSFLab software  27  for calculations based on rigorous vectorial theory. We calculate the point spread function and in particular the fluence radial profile at the focus for different focusing and illumination conditions.
This point will be confirmed just after. Moreover, this imperfect shape at the focal position also suggests that the beam before the lens is also imperfect and not Gaussian. Accordingly, the Fourier transformed function resulting from the application of a circular aperture is not necessarily corresponding to the rigorous analysis made in section 4, unless the aperture is sufficiently closed. Figure  5 (c)  shows the beam profile resulting after placing a circular aperture with P T = 75%. A central spot and a surrounding ring are observed, where the maximum value of the ring (after a analysis with ImageJ software) corresponds to 7. 4 ⋅ 10 -3. This is very close to the theoretical value of 7. 7•10 -3 in Table  2  and confirms the applicability of the analysis made in section 4 suggesting that the cut pedestal was constituting the main difference with a Gaussian beam.
Methods Numerical calculation of the diffraction beams for different truncation conditions are obtained with PSFlab software 27 (version 3. 5). The calculation of Liu's method parameters (section 3, section 4 and section 5), the obtention of the Airy disk function (section 3) and the calculation of the integrals enabling to obtain the ISD (section 3 and section 4) are performed by programming respectively the Equation  7 , Equation 8 and Equation  4. In this manuscript those calculations were performed by using MATLAB (R2020a under a licence of Universidad Autónoma de Madrid). Other programming languages (e. g. Python or C++) would be also appropriate for those purposes. Analysis of experimental beam images is performed by using ImageJ software (version 1. 53a). The complete experimental methodology is detailed on section 5A.
Although the whole understanding of σ1r physiological functions and pathohistological roles is not achieved yet, it is well established that σ1r is tightly regulated by ERmediated Ca 2+ dynamics and, conversely, plays a crucial role for Ca 2+ homeostasis, proteostasis, and overall cellular adaptation capacity  (Christ et al. , 2019; Hayashi and Su, 2007; Mori et al. , 2013). σ1 receptor agonists prevent Aβ-induced neurotoxicity  (Marrazzo et al. , 2005). Moreover, σ1r activation is associated with a significant slow-down of AD-related NMDA receptor excitotoxicity, thus promoting synaptic remodeling with improved plasticity  (Hayashi and Su, 2007; Maurice and Su, 2009; Mori et al. , 2013). A multicenter phase IIa clinical trial, ANAVEX2-73-002 (NCT02244541, 2014), performing a 57-week treatment of 32 mild-to-moderate AD patients (aged 55-85) with the selective σ1r agonist Blarcamesine (ANAVEX®2-73, Anavex Life Sciences Corp.
Regulatory challenges of drug repositioning Medical research is increasingly focusing on how drugs licensed for treating particular diseases can be examined for use in other therapeutic areas. New approaches to drug research and analytical methods in combination with the availability of big data sources, such as electronic medical records and claims data, are increasingly raising the interest in developing systematic approaches to drug repurposing. Research into new indications for existing drugs is often driven by life cycle management activities in order to extend the patent life of drug products. This has now led to a systematic use of high-throughput screening platforms, "omics", and in silico approaches, and the application of real-world data (RWD) to identify potential candidates for drug repurposing  (Cha et al.
, 2018). Major players in the field of drug repurposing include research institutes, pharmaceutical and repurposing technology companies as well as regulatory agencies. In its strategic reflection on regulatory science to 2025, the European Medicines Agency (EMA) supports the development and implementation of a repurposing framework as part of addressing the need for and the availability of medicinal products to tackle emerging health threats (European Medicines Agency, 2018). The aim is to reduce the time and costs of drug development and to offer further therapeutic options to patients. To support repurposing, the EMA identified several areas that requires consideration such as potential incentives like new data protection period or its extension and disincentives, sources of evidence for repurposing, involvement of academia and potential for changes in marketing authorization (European Medicines Agency, 2018).
In this context, a balance needs to be found to obtain benefits for companies to refinance their research efforts without increasing the costs for the "new" product at the expense of health insurance too much. Therefore, the EMA suggests discussions with stakeholders and proposes a formal framework for drug repurposing, including more regulatory advice on generation of repurposing evidence for marketing authorization application submission. Further points suggest a summary of how third-party data, relevant RWD and non-clinical datasets can be used without infringing the interest of researching companies, translation of experience with EMA's registry pilot for RWD, and examination of low-intervention clinical trials for evidence generation (European Medicines Agency, 2018).
Cholinesterase inhibitors in Alzheimer's disease: a reappraisal ChEI therapy of AD has been considered to be a relatively short-term intervention with symptomatic effects maximally lasting three to six months. However, both open and randomized placebo-controlled trials, lasting 12-24 months or more, suggest that stabilization of the disease and maintenance of clinical effects for more than 12 months is a feasible goal in many patients  (Giacobini, 2001)   (Table 2 ). Published data (Table  2 ) indicate that beneficial effects can be maintained in a number of patients, for up to 36 months, using treatments with four different inhibitors: Donepezil, Metrifonate, Rivastigmine, and Galantamine. In terms of cognitive global improvement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) score scale, this benefit represents a total of 15-20 point gain which means an 18-24-month difference in the disease history, as compared to placebo-treatment.
Acknowledgements This research and this manuscript was part of the translational research program "PHOENIX", awarded to HH, and administered by the  Sorbonne University Foundation  and sponsored by la  Fondation pour la Recherche sur Alzheimer. HH is an employee of  Eisai Inc. This work has been performed during his previous position at  Sorbonne University, Paris, France. At Sorbonne University  he was supported by the  AXA Research Fund , the  "Fondation partenariale Sorbonne Université" and the "Fondation pour la Recherche sur Alzheimer", Paris, France. AV is an employee of  Eisai Inc. This work has been performed during his previous position at  Sorbonne University, Paris, France.
Genetic Comparisons of the Vaccine and Challenge Strains Strains included in the genetic analyses included the strains of the vaccines Porcilis PRRS (Strain DV; accession number KJ127878), Unistrain PRRS (Strain Amervac; accession number GU067771), Suvaxyn PRRS (Strain 96V198; accession number MK876228) and the challenge strains Horsens (accession number MN603982) and Finistere (PRRS-FR-2005-29-24-1; accession number KY366411). A phylogenetic tree was constructed based on the ORF1-ORF7 nucleotide sequences of the PRRSV strains utilized in this study together with global representative PRRSV-1 strains. The alignment was performed with MUSCLE (Multiple Sequence Comparison by Log-Expectation), and the phylogenetic tree was constructed with the Neighbor-Joining algorithm and 1000 bootstrap replicates and visualized with FigTree v.