dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-processing of the fMRI dataset included a first step to verify the absence of any outlier (i. e. corrupted volume by spikes, etc. ). Standard preprocessing was performed using Nipype  [53] , including slice timing and motion correction, as in previous studies using the ARCHI dataset  [54]. The T1-weighted scan was used as a reference and both fMRI and fieldmap datasets were matched to this reference using a mutual-information based registration algorithm providing the corresponding rigid transformations T(fMRI→T1) and T(B0→T1). Unwrapping of the phase difference map included in the fieldmap scan was performed using a weighted full multigrid phase unwrapper algorithm that did not require the preliminary definition of a mask of the brain, and the unwrapped phase map was then matched to the fMRI dataset by composition of the former transformations in order to correct for susceptibility artifacts.
Results This section uses our definitions of dynamic communicability to interpret resting-state fMRI data (see Fig. 3a ). The data were recorded from 77 subjects during 244 frames each separated by a TR = 2. 4 s with eyes closed in a scanner. Our framework relies on a dynamic system and, for illustration purpose, the present study uses our recent optimization to fit the MOU model to BOLD signals  [18, 36] ; the python code is available on github. com/ MatthieuGilson/WBLEC_toolbox). This model optimization combines several key aspects that are developed in Methods: • A whole-brain approach  [16]  is necessary to properly take into account the distributed nature of information conveyed by BOLD signals, as was experimentally observed for high-level cognition  [59]  or neuropathologies  [60]. • The MOU process is optimized to reproduce the spatio-temporal structure of the BOLD signals, which was shown to convey information about the behavioral condition of subjects  [61, 62, 63].
7a ). For the studied dataset, MOU-EC determines functional communities in a much more accurate manner than FC (Fig. 7c ). This is an important point to analyze task-dependent communities, which is not possible using SC. Another interesting aspect of our formalism is the quantitative comparison between network dynamics. With surrogate networks such as ring lattices or random networks, it reveals the properties of the estimated MOU-EC (Fig. 4 ). Here we have compared global measures: the total communicability S C is the sum of all interactions at a given time and the diversity D C reflects their heterogeneity within the network. These time-dependent measures correspond to curves that are more or less distant, providing a metric to compare the data with specific topologies. Likewise, the comparison of the MOU-EC for two parcellations using communicability demonstrates a consistency across scales (Fig.
A related point concerns the single time constant τ for all ROIs chosen for each subject, which was motivated from the analyzed data. Further study -possibly with more datasets-is necessary to explore the possibility of finessing the model with ROI-specific τ. Recall also that any interpretation of BOLD in term of brain communication relies on the assumption that changes in neural activity are reliably reflected in the BOLD signals, which is under debate  [84, 85, 86]. The model optimization enforces a diagonal Σ, which has consequences on the estimated MOU-EC. This observed in a previous study for task-evoked activity  [36] , but not consistently explored so far. In the present version the MOU-EC does not incorporate common inputs, which means that the A estimates may compensate with stronger weights to explain the observed correlations in FC.
Acknowledgements This work has been supported by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 720270  ( HBP SGA1 ) and No. 785907  ( HBP SGA2 ). MG also acknowledges funding from the  Marie Sk lodowska-Curie Action  (Grant  H2020-MSCA-656547 ) of the  European Commission. GZL, NEK and GD acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 720270  ( HBP SGA1 ). GD also acknowledges funding from the  Spanish Research Project  (No. PSI2013-42091-P ). NEK acknowledges support from the " MOVE-IN Louvain " fellowship co-funded by the  Marie Sk lodowska-Curie Action  of the  European Commission. The " CONNECT/Archi Database " is the property of the CEA I2BM NeuroSpin centre and was designed under the supervision of  Dr Cyril Poupon  and  Dr Jean-François Mangin.
Observations and data reduction We observed the PZ Tel system with the ZIMPOL instrument at VLT/SPHERE  (Schmid et al. 2018) , obtaining simultaneous coronagraphic ADI observations in the Cnt_Hα and N_Hα filter. The data were taken on UT 2018-05-30 in two observation blocks before and after the meridian passage, to maximise the total field rotation while allowing flexibility in the observing schedule. Each observing block consists of a set of science exposures with an integration time of 220 seconds, which were bracketed with non-saturated observations of the star with DIT=52 seconds, that we denote as flux frames. We also recorded a centre frame at the beginning and end of each observing block, in which a pattern is applied to the deformable mirror creating 4 bright copies of the central PSF outside of the coronagraph (in a symmetric pattern around the central star) which are used to compute the stellar position behind the coronagraph.
The final centre and relative error are the mean and standard deviation of these 4 centres (for each filter). We then re-centre the science frames using the scipy. ndimage. interpolation. shift package with spline interpolation of order 3, and cut them to stamps of 1. 62×1. 62 arcsec end-ing up with 20 cosmetically reduced and centred science frames for each filter. Since the unsaturated star is offset from the coronagraph and therefore visible, we fitted a two dimensional gaussian to re-centre the flux frames, ending up with eight cosmetically reduced and centred flux frames for each filter. The parallactic angle for each frame is automatically computed by the ZIMPOL pipeline, and takes care of a constant known offset of 134 ± 0. 5 •  (Maire et al. 2016; Cugno et al. 2019)  for which the frames must be rotated in the counterclockwise direction.
Analysis and results The goal of this paper is to detect and quantify Hα emission from the companion around PZ Tel, to expand the sample of known brown dwarfs and planetary companions with Hα detection and better understand the formation and evolution of these objects. We also provide an additional astrometric measurement of the PZ Tel B, extending the time baseline by four more years. We clearly detected the companion in both Cnt_Hα and N_Hα filter, as shown in Figure  1. Even though the detection is clear in both filters, we also analysed the data with angular spectral differential imaging (ASDI) technique, shown in the rightmost panel of Figure  1. We refer to Appendix A, as well as  Cugno et al. (2019) , for a detailed explanation of the ASDI analysis.
Astrometry and flux contrast We quantified the astrometry and flux contrast of the companion, for both filters, using the ANDROMEDA package  (Cantalloube et al. 2015). This algorithm needs as input the cosmetically reduced frames, the corresponding parallactic angles, and an unsaturated PSF of the central star to create a model of the planetary signal signature, whose flux and position is fitted via a maximum likelihood estimation. We created this unsaturated image of the host star (for both filters) as median combination of all the flux frames, scaled to the DIT of the science frames. We set the Inner Working Angle parameter to 1. 0 λ/D (we refer to the ANDROMEDA paper for a detailed explanation of how the 58. 93±0. 5 59. 40±0. 5 Flux contrast (7. 4 ± 0. 9) × 10 -5 (29. 0 ± 3. 5) × 10 -5 Fig.
(2016). A possible explanation for this difference lies in the different boundaries applied:  Beust et al. (2016)  allowed not-bound orbits while in this work we only considered orbits with e < 1. Our best solution for the semi-major axis of 31. 3 au agrees with previous works (17. 86 < a < 1098 au in  Ginski et al. 2014  1 https://github. com/vortex-exoplanet/PyAstrOFit and a 24. 5 au in  Maire et al. 2016). We found a best inclination of 91. 6 degrees, which is in agreement with previous ranges of 91. 3 • < i < 168. 1 • for  Ginski et al. (2014)  and 91 • < i < 96. 1 • for  Maire et al. (2016). Previous confidence intervals for the longitude of ascending node were 50 • < Ω < 70 • for  Ginski et al. (2014)  and 55. 1 • < Ω < 59. 1 • for  Maire et al. (2016), and Ginski et al. (2014)  cited an interval of 122.
Discussion and conclusions We presented SPHERE/ZIMPOL observations of the known sub-stellar M dwarf companion around PZ Tel, taken in both Hα continuum and narrow band filter. We detected the companion in both datasets obtaining new astrometric and photometric measurements. This currently represents the second only Hα detection of a companion using the SPHERE instrument, and it further proves the capability of this instrument to detect Hα signatures in binary systems. We used our newly added astrometric data, together with values from the literature, to explore the allowed orbital solutions for PZ Tel B, finding orbital elements in agreement with what done in previous works (with the only exception being our lower boundary on the eccentricity). Our added data extends the available baseline for orbital studies of PZ Tel B up to ∼12 years.
Acknowledgements. Based on observations collected at the  European Southern Observatory  under  ESO  programme  0101. C-0672 (A). A. M. B. would like to thank the anonymous referee for the useful and constructive comments. J. S. acknowledges the support from the  Swiss National Science Foundation (SNSF) Ambizione  grant  PZ00P2_174115. A. M. acknowledges the support of the  DFG priority programme SPP 1992 'Exploring the Diversity of Extrasolar Planets'  ( MU 4172/1-1  and  QU 113/6-1 ). G. C. and S. P. Q. thank the  Swiss National Science Foundation  for the financial support under the grant number  200021_169131. This research made use of Astropy, 2 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018).
INTRODUCTION A few decades ago, digital technologies began to take an increasingly important place in our lifestyle. Indeed, in both our daily lives and work, they gradually multiplied, and became essential in certain fields. For instance, computing is now necessary to conceive architecture : the use of computer assisted drawing and 3D modeling now appear to be standard. In fields such as archaeology or in the analysis of artistic and architectural elements, different tools appeared. In the field of the statuary, photogrammetric acquisition is the starting point of a digitization. Thanks to precise photography techniques, it allows to reconstruct a point cloud on software such as Micmac or Photoscan. Some tools, such as Zbrush or 3ds Max, also make it possible to create skeletons, generated because of these acquisitions.
A merge with CIDOC CRM Once this work completed and extended to the entire field, the ontology core then defined will be formalized with a protected ontology publisher. Figure  13  : Presentation of the CIDOC-CRM It would then be relevant to consider a merger with CIDOC-CRM (Figure  13 ), a reference conceptual model and a generic ontology of reference, in the field of scientific documentation of cultural heritage. Once completed, a correlation, thanks to SQL/ SPQRL mapping, can then be created between the databases within the information system.
The IUPHAR/BPS Guide to PHARMACOLOGY (www. guidetopharmacology. org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe significant updates made over the seven releases during the last two years. The database is notably enhanced through the continued linking of relevant pharmacology with key immunological data types as part of the IUPHAR Guide to IMMUNOPHARMACOL-OGY (www. guidetoimmunopharmacology. org) and by a major new extension, the IUPHAR/MMV Guide to Malaria PHARMACOLOGY (www. guidetomalariapharmacology. org). The latter has been constructed in partnership with the Medicines for Malaria Venture, an organization dedicated to identifying, developing and delivering new antimalarial therapies that are both effective and affordable.
From 2015, it extended into the priority area of immunopharmacology through the Wellcometrust funded IUPHAR Guide to Immunopharmacology Nucleic Acids Research, 2020, Vol. 48 , Database issue D1007 (GtoImmuPdb; www. guidetoimmunopharmacology. org) (5) (6) (7). The funding period for GtoImmuPdb concluded in October 2018, at which point the extension was officially launched at a focused immunopharmacology meeting, the report of which is available here (https://www. guidetoimmunopharmacology. org/pdfs/ Edinburgh Immunoparmacology Meeting Report 2018. pdf). GtoImmuPdb delivers a knowledge base that connects immunology with pharmacology  (8) , providing vital support to the research and development of drugs targeted at modulating immune, inflammatory or infectious components of disease. In the preceding ∼20 years, increasing numbers of antibodies and small molecule drugs that target the immune system have been approved as licensed medicines (Figure  1 ), and this corroborates the importance of immunopharmacology.
The merit of extending GtoPdb into immune-relevant areas is strengthened by the partnership between IUPHAR and the International Union of Immunological Sciences (IUIS) to create standard tools and nomenclature. Since our 2018 NAR publication  (7) , we have undertaken a further extension of the database; this time to include new types of data relevant to malaria pharmacology. Malaria is a major global health challenge with a disproportionate impact on resource-limited countries. In 2017, there were an estimated 219 million cases of the disease, leading to 435 000 deaths worldwide, with over 90% of these occurring in the World Health Organization's Africa Region (9). Action is taken against the spread and progress of the disease at many levels, from control of the mosquito vector to prevention and treatment with antimalarial drugs  (10).
Resistance to these drugs has emerged in many regions of the world, and strenuous efforts are being made to develop alternative drugs or vaccines. The Medicines for Malaria Venture (MMV; www. mmv. org) is an international product development partnership, dedicated to identifying, developing and delivering new antimalarial therapies that are both effective and affordable for the communities they are intended to protect  (11). In the last twenty years, MMV and their partners have developed thirteen new medicines, some specialized for paediatric use or use in pregnant women, and have taken over stewardship of some drug development programmes from the Drugs for Neglected Diseases Initiative (www. dndi. org) (12) (13) (14). Recognizing the value of having a high-quality expert-curated database when designing drug development strategies, IUPHAR and MMV have collaborated to create the Guide to Malaria PHARMACOLOGY (abbreviated to GtoMPdb), a web resource specifically designed for malaria pharmacology.
We also review clinical trial registries and applications to the WHO for new INNs (International Nonproprietary Name) (which provides an indication of developments in the immunity/inflammation/immuno-oncology fields), and monitor new drug approvals. Targets. The expansion in GtoPdb content is summarised in Table  1. Individual targets in GtoPdb use a UniProtKB/SwissProt (15) Accession as their primary identifier and are organised into hierarchical target families. These are organised into top-level target classes (Table  1A ). GtoPdb contains 1781 human protein targets with curated ligand interactions (Table  1C ), 1525 of these targets have quantitative binding data. If the count of interactions is restricted to just those human targets with quantitative binding data to an approved drug, these number 638 interactions, 329 of which are cases where the protein is designated as the primary target of that drug (Figure  2 ).
This allowed Figure  1. Growth in approvals of monoclonal antibodies over the last 26 years. Data include approvals for monoclonal antibodies and immunoglobulin Fc domain-containing fusion proteins that act at human protein targets and which are used to treat human diseases. Approval status is derived from the FDA (U. S. Food & Drug Administration; https://www. fda. gov), EMA (European Medicines Agency; https://www. ema. europa. eu) and EMC (Electronic Medicines Compendium; https://www. medicines. org. uk) websites, as well as from published 'First global approval' articles, and other online resources. The earliest approval date is used for each drug. The data set does not include the increasing number of biosimilar products that have been approved in the last 5-6 years. Immunological data types.
Rather than simply acting as inhibitors, PROTACS direct their protein target to proteasomal degradation. As examples we have included JH-XI-10-02 (a CDK8 kinasedirected degrader; www. guidetopharmacology. org/GRAC/ D1010 Nucleic Acids Research, 2020, Vol. 48, Database issue LigandDisplayForward?ligandId=9887) and ARD-69 (an androgen receptor degrader; www. guidetopharmacology. org/GRAC/LigandDisplayForward?ligandId=10182), both of which have postulated anti-tumour potential  (20, 21). New classes of anti-inflammatory and analgesic drugs are also being developed, with examples such as naproxcinod (a NO-donating naproxen analogue prodrug; www. guidetoimmunopharmacology. org/GRAC/ LigandDisplayForward?ligandId=9551) and ATB-346 (a hydrogen sulfide-releasing naproxen derivative; www. guidetoimmunopharmacology.
org/GRAC/ LigandDisplayForward?ligandId=9534), which have both progressed to clinical evaluation as novel therapeutics for arthritis and osteoarthritis, respectively  (22, 23). PubChem submissions. The range of powerful utilities arising from GtoPdb ligand data integration into Pub-Chem  (24)  and the rest of the NCBI system (25), including PubMed and PDB ligands, has already been described  (7). Users are able to make domain-specific queries related to both immunopharmacology and malaria pharmacology via curatorial tagging within the depositor comment sections in the substance records (SIDs) (Figure  3 ). This allows queries to be executed from both the PubChem Substance (SID) and PubChem Compound (CID) interfaces  (26) , by using an advanced search of 'comments' fields, as outlined in Table  3.
PubChem Substances are communityprovided compounds, and many entries may exist for the same molecule. Each may contain different information about the molecule, depending on the information provided by the submitter. PubChem extracts the unique chemical structures from Substance records (standardization) and stores them as PubChem Compounds. This means that substance records from different data sources about the same molecule are aggregated in a common Compound record in PubChem. The wide range of GtoPdb-associated data mining that can be executed in PubChem is too large to be expanded in detail but selected worked examples have been described  (27). Informative statistics represented in the 7612 total CIDs (2019. 4 release) include the following: • 66% conform to Lipinski's Rule-of-Five (28,29) lead-like property limits.
• 148 entries are unique to GtoPdb as their only annotation source (as of 1 October 2019). • 78% have an exact match to an automated patent extraction source (not all of these are currently claimed structures but many can be linked to SAR data sets in patent documents to which links will be added). As mentioned a new 'Guide to Malaria Pharmacology' tag has been added to the depositor comments in our Pub-Chem submission. This means that the set of antimalarial compounds in GtoPdb (2019. 4 release) can be retrieved from PubChem (Figure  4 ), and specific statistics for 72 CIDs can be generated: • 44 conform to Lipinski's Rule-of-Five  (28, 29).
Website Disease summary pages. The website has been significantly enhanced by the development of disease summary pages (Figure  5 ). As part of the GtoImmuPdb extension, targets have been linked to ligands of immunological relevance to the diseases in which they play a role. The GtoPdb already contained limited disease information, which included pathophysiologies and clinically-relevant mutations for targets, but it was not organised in a manner to find all relevant data for each disease easily. The new development has allowed the consolidation of all the associated data and to present this to users on a single disease page. In total, there are over 1000 disease entries in the database linked to targets and/or ligands. For all diseases, there are external links to one or more entries in OMIM  (30, 31) , Orphanet (https://www.
orpha. net) and the Disease Ontology (32) and  list any known synonyms, which helps users to cross-check between different resources. The target section of the disease summary page displays information on targets associated with the disease. Ligands that are associated with the disease, that also have interaction data with the target, are listed alongside curatorial comments and indications if they are approved drugs. If there is further data on the targets role and any other therapeutically-used drugs acting via the target, these are also displayed. The ligand section lists all the drugs and other compounds associated with the disease, the majority of which come from the curation effort for GtoImmuPdb. Users can expand this section for additional information linking the ligand to the disease, such as clinical use and bioactivity comments.
WHO essential medicines. GtoPdb now includes an additional category of ligands: those currently included in the World Health Organization (WHO) Model List of Essential Medicines (21st, 2019; https://www. who. int/ medicines/publications/essentialmedicines/en/). These ligands are displayed under the 'WHO' category on our ligand list page (https://www. guidetopharmacology. org/GRAC/ LigandListForward?type=WHO-essential&database=all) D1012 Nucleic Acids Research, 2020, Vol. 48, Database issue and on individual ligand summary pages, inclusion in this list is marked under the classification section. Move to hypertext transfer protocol secure (HTTPS). In order to improve and protect the integrity of GtoPdb, we have completed the transition from HTTP to HTTPS. This helps to make end user communication more secure.
GUIDE TO MALARIA PHARMACOLOGY The Guide to Malaria Pharmacology (GtoMPdb) project was initiated in October 2017, with funding from MMV, to expand the content of the existing database to include antimalarial ligands and targets. This required extension of the GtoPdb to display the new data and to augment existing search and browse functions. A purpose-built portal has been developed, in consultation with key opinion leaders in malaria research, to provide optimized access to the antimalarial data. This initiative has enriched the GtoPdb and will hopefully advance innovation by providing, in a single expert-curated database, results from antimalarial drug discovery programmes and the scientific literature. The full public release of the GtoMPdb was made in September 2019.
Curation and content The focus for the GtoPdb has hitherto been the pharmacology and immunopharmacology associated with human non-infectious diseases. Targets identified and validated in Plasmodium, the genus of protozoan parasite known to cause malaria, had not previously been included in the GtoPdb. Indeed, there were very few, if any, data from non-  mammalian species in the GtoPdb. Similarly, the number of ligands with antimalarial activity was small. In extending the parent database to capture data of relevance to malaria pharmacology our curatorial procedures have been reviewed, allowing the identification of modifications required to the database schema and web interface. The sections below will expand on these aspects but an important consideration has been to utilize GtoPdb entity IDs rather than generating a separate identifier system.