dataesr/scientific-paragraphs-categorization huggingface dataset:
The centroid of the goose colony and distances between the centroid and transects were calculated for each year with the rgeos R package  (Bivand et al. 2014). Transects were performed only once each year and almost all (91%, n = 267) were repeated at least four years. To account for potential pseudo-replication, transect or nest number was used as a random factor in all the analyses  (Bolker et al. 2009 ). Because transects were performed over a 23-d period and because the detection of predator or nesting plovers, as well as predation of artificial nests, could change over time, standardized day of year was also included as a random factor in all our analyses. Models were selected according to their corrected Akaike information criterion (AICc). Parameter estimates were derived from averaging all models (model.
ACKNOWLEDGMENTS This paper was supported logistically by  Sirmilik National Park (Parks Canada ) and by the  Bylot Island  field station. Funding was provided by  ArcticNet ,  Arctic Goose Joint Venture, Fonds de recherche du Qu ebec-Nature et technologies (FRQNT) ,  Indigenous and Northern Affairs Canada ,  Natural Resources Canada  ( Polar Continental Shelf Program ),  Universit e du Qu ebec a Rimouski, Natural Science and Engineering Research Council of Canada (NSERC) ,  W. Garfield Weston Award  for  Northern Research and EnviroNord  ( NSERC   CREATE Training Program in Northern Environmental Sciences ). We are grateful to  E. Chalifour ,  L. Paquette ,  M. Trudel ,  P. Royer-Boutin ,  C. Doucet , and  D. -J. L eandri-Breton  for their help on the field. M. -C. Cadieux  and  N. Casajus  provided support with data handling and analysis.
This approach, known as systems pharmacology  (Boran and Iyengar, 2010b; Westerhoff et al. , 2015) , is underpinned by the expansion in pathway, pharmacology and medicinal chemistry databases. For example, WikiPathways held 804 human pathways (http://www. wikipathways. org/index. php/WikiPathways: Statistics) with 253 added in 2015  (Kutmon et al. , 2016). Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY holds 518 pathway maps  (Kanehisa et al. , 2017)  (http://www. kegg. jp/kegg/docs/statistics. html). Reactome currently holds 2148 human pathways involving 10 684 proteins and isoforms (http://reactome. org/stats. html) (Croft et al. , 2014; Fabregat et al. , 2016). ChEMBL version 23  (Gaulton et al. , 2016)  includes 14 675 320 bioactivities, and the International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to Pharmacology (GtoPdb) contains 15 281 curated interactions in its 2017.
In particular, we build a dynamic ordinary differential equation (ODE) model of the pathway, which we parameterize as far as possible from the literature. We identify relevant pharmacological agents that act on this pathway and parameterize them as far as possible from the literature and online databases. We then use computational optimization techniques to identify the drug combinations that are most effective at suppressing the outputs of the pathway that lead to cholesterol production and that minimize off-target effects. In completing our analysis, we identify many of the problems that prevent this type of work being undertaken routinely, and we suggest solutions that would enable systems pharmacology to make a regular contribution to therapy development. As explored in previous studies  (Mazein et al.
Pathway production We started from the representations available in KEGG  (Kanehisa et al. , 2014) , MetaCyc  (Caspi et al. , 2016)  and the GtoPdb  (Southan et al. , 2016)  taking these resources to be representative of the community of online pathway databases. We reviewed the primary literature to establish the structure of the mevalonate portion of the cholesterol biosynthesis pathway, in particular the enzymes involved in the pathway, the reactions they catalyse, their subcellular localization, the species in which they were identified and any known isoforms. Diagrams of the pathway were created using the Systems Biology Graphical Notation (SBGN) (Le  Novère et al. , 2009) , the yEd diagram software (yWorks GmbH, http://www. yworks. com/products/yed) and the SBGN-ED add-on to VANTED  (Czauderna et al.
Pathway parameterization We identified the kinetic parameters that quantify each reaction unambiguously (K m and K cat ) using the Braunschweig Enzyme Database (BRENDA)  (Chang et al. , 2015)  and verified the values described against those in the primary literature. In many instances, enzymes were associated with multiple kinetic parameter sets. We selected kinetic parameters based upon the following criteria: (i) specificity to the wild-type enzyme in one of the three main mammalian model species: human, mouse or rat; (ii) sourced from a primary literature reference describing in vivo or in vitro experimental data as opposed to computationally derived structural modelling data; and (iii) sourced from a reference that could be accessed and therefore verified. For many enzymes, this yielded a range of values for each parameter, and where this was the case, we used the mean of the values obtained.
Inhibitor list Inhibitor compounds not already indexed in GtoPdb were identified for each reaction from ChEMBL and BRENDA, databases that we took to be representative of the community of target databases. We included a compound in our set if it met three criteria: (i) the enzyme used in the assay was wild-type from one of the three main mammalian model species: human, mouse or rat; (ii) an experimentally determined reaction-specific inhibition constant (K i ) was reported; and (iii) the assay conditions were reported. Crucially, all data were checked against the primary literature references. Where this yielded a range of inhibition constants for nominally identical compounds, the most potent K i values were used. We verified the correct chemical structures of the inhibitors by cross-referencing the original references against the online chemical databases PubChem  (Kim et al.
, 2016)  and ChemSpider  (Pence and Williams, 2010). The actual chemical structures of the marketed statin drugs were established by checking the FDA labels and the international nonproprietary name-assigned structures on the World Health Organization MedNet site (https://mednet-communities. net/inn). Comparison of unique structural identifiers allowed us to identify duplicates within the ChEMBL, BRENDA and literature-derived dataset, and to establish whether the chemical structure reported in a given reference matched the marketed drug or research compound structures. Curated content describing the enzymes in this pathway, their substrates and small molecule inhibitors was used to consolidate and expand GtoPdb using the same approach and guidelines as described elsewhere  (Pawson et al. , 2014).
mathworks. com) in parallel with a population size of 200 and a function tolerance of 10 À6. Matlab was chosen as the modelling platform for its flexibility, stability and comprehensive libraries. The genetic algorithm started with one instance of a set of drug concentrations where each drug was assigned a concentration equal to its K i. A 199 further instances of sets of drug concentrations were automatically generated from this instance by adding Gaussian noise to the concentration of each drug (with standard deviation 1, the default setting). These 200 instances comprised the first generation of candidate interventions. All instances of sets of concentrations were evaluated for their efficacy at suppressing squalene synthesis whilst maintaining geranylgeranyl diphosphate production. Two hundred new instances were created as a second generation of candidate interventions from the two most effective instances of the first generation and with the addition of Gaussian noise.
Inhibition constants could be obtained for 8 of the 10 enzymes in the pathway. Where reported, these values came from studies conducted across a range of pH levels from 6. 8 to 7. 5 and temperatures from 25°C to 37°C. Both explicit structure and name-to-structure (n2s) ambiguities existed around the reporting of inhibitor entities. In some cases, the common or trade name of a compound was used, without specification of the exact chemical structure and stereochemistry. In other cases, we found a different n2s assignment across different database resources or indeed within the same resource. For example, under the HMGCS1 entry of BRENDA, the same inhibitor is listed twice as L-659,699 and (E,E)-11-[3-(hydroxymethyl)-4-oxo-2-oxytanyl]-3,5,7-trimethyl-2,4undecadienenoic acid. Several results recorded in ChEMBL were transcribed against the incorrect drug target.
Three inhibitors listed against the enzyme HMGCS1 describe results obtained from experiments with HMGCR  (Balasubramanian et al. , 1989). There were also cases where the incorrect species had been recorded. For example, the compound with ChEMBL ID CHEMBL88601 cited in one study  (Procopiou et al. , 1994)  (ChEMBL document ID CHEMBL1151052) is listed against the human squalene synthase (FDFT1) enzyme, whilst in fact, the paper describes results for the yeast Candida albicans and rats.
In order to identify the optimal multi-drug combination, it was necessary to use a high-performance computing (HPC) platform. However, the HPC demands were modest. Using an eight-node desktop computer running MATLAB in parallel, we can see that the score (a dimensionless value, greater than or equal to zero, that quantifies how effectively the best performing multi-drug intervention identified The results of our curation of the pathway and the inhibitors that target it are available in GtoPdb at http://www. guidetopharmacology. org/GRAC/FamilyDisplayForward? familyId=104, an example of which is shown in Supporting Information Figure  S2. The model produced is available from http://biomodels. org  (Chelliah et al. , 2013)  (ID: MODEL1506220000).
Drug development. Few multi-drug treatments make it through the development process. The number of combinational therapies listed in the Therapeutic Target Database at the time of writing is 115  (Qin et al. , 2014). A combination therapy, LCZ696, with the brand name Entresto, was approved in 2015 and is in Phase III of clinical trials for the treatment of cardiovascular disorders. Establishing drug combinations using a conventional drug development pipeline creates significant challenges as development essentially replicates the single drug development process multiple times. Systems pharmacology is therefore critical to expanding the range of multi-drug interventions available in a cost-effective manner. Although it may add extra steps to the preclinical stages of the drug development process, it could have a significant positive impact on the cost-efficiency associated with each success by reducing the attrition rate in the later stages of the pipeline  (Bowes et al.
Problem 3: curation errors Online databases can contain errors. We have identified cases where the incorrect structures, enzyme targets, species and parameter values had been recorded. Errors were at low frequency, but some would undermine systems pharmacology approaches, and these fell into two groups: errors that derived from mistakes in the literature itself, such as from misinterpretation of data, and errors that derived from the incorrect transcribing from the literature to the database. The former derive from verbatim acceptance of results from manuscripts following author error. The latter errors can be introduced by databases themselves, either from semiautomated triage tools or inadvertent curator mistakes, and this can be associated with a lack of clarity in the original document.
In the present study and for the GtoPdb, we reviewed the primary literature when expanding our datasets and re-curated existing database coverage. Solution 3: quality control in curation of results. Using teams of curators to validate each other's work can reduce errors. This can be arranged systematically into error-identifying or errorcorrecting curation quality control programmes. In an erroridentifying programme, each result is independently curated twice and where disagreements are identified, the data is reviewed. Such approaches have been discussed within the International Society for Biocuration  (Bateman, 2010). However, the funding limitations of most public databases preclude this degree of validation. In an errorcorrecting programme, each result would be independently curated three times and where a disagreement is found, the consensus would be accepted automatically as correct.
Systems pharmacology for the future A workflow for future studies and hypothesis generation. With an adequate set of standards and a well-characterized experimental system, it should be possible to develop intervention hypotheses that can be tested to inform future therapy development and to contribute to iterative refinement of databases. To make this a consistent, high confidence process, it would be advantageous to work in one experimental system. Such an experimental system could be in vivo or in vitro. However, an in vitro model would offer more control and consistency. Such an in vitro system would serve as a first approximation to in vivo physiology and would contribute to determining how in vitro parameters are mapped to in vivo parameters in order to maximize their value. An advantage of using an in vitro system is that it would lend itself to automated hypothesis generation and testing and it could be used to systematically search for new protein-protein and drug-target interactions.
It has been suggested that artificial intelligence methods would be suitable for this purpose in the laboratory  (King et al. , 2004). Automation would both minimize the time required for study and reduce the risk of misreporting or mis-curating the results. Our current systems-level understanding has grown to a scale where manual manipulation is no longer feasible. Standards such as SBML, SBGN and SBGN-ML and repositories such as BioModels have been developed partially to address this and automated model development allows the full value of databases to be realized  (Swainston et al. , 2011). Open Pharmacological Concept Triple Store  (Williams et al. , 2012)  is a consortium responsible for a number of pharmacological and life science databases whose aims The feasibility of systems pharmacology BJP include the improvement of data availability through the use of data standards, the incorporation of contextual data through semantic web standards and the cross-platform linkage of datasets through an identity mapping service.
Developing multi-drug hypotheses is a challenge that grows exponentially with the number of drugs and interactions considered. HPC resources are likely to be essential for this development. The following workflow would enable the process to be automated (see Figure  3 ). (VI) Hypothesis testing. Success yields a candidate therapy and provides validation of the database. Failure initiates further exploration of the underlying interactions that in turn refine the databases. (VII) Candidate Intervention. Following success, the group of compounds enters an optimization pipeline that reduces them to a minimal set of lead compounds for preclinical testing to establish their efficacy and safety.
Conclusion The growth in our understanding of pharmacological interactions and the continuing development of our ability to computationally model pathway biology will increasingly enable us to explore drug combinations that target multiple points on multiple pathways to reprogram system level behaviour. In this way, systems pharmacology may lead to more effective therapies with fewer side effects. Here, we explored this approach for the mevalonate arm of the cholesterol biosynthesis pathway, and in doing so, we identify many of the current barriers to progress. We attempted to build a systems pharmacology model of the mevalonate arm of the cholesterol biosynthesis pathway, but gaps and inconsistencies in the data prevented us from achieving this to a high level of confidence. In particular, we.
8, 62617146. 82, 107344511. 7, 166981644. 3, 250474415. 3, 375714567. 5, 584449515. 1, 1 001 921 461, 2 254 341 919) nM and for Zaragozic acid A the IC10-IC50 concentrations were  (0. 5, 0. 9, 1. 3, 1. 7, 2. 1, 2. 6, 3. 3, 4. 3, 6. 4 ) nM. Figure  S2  A representative reaction from the mevalonate arm of the cholesterol biosynthesis pathway, as described on the IUPHAR/BPS GuidetoPharmacology (GtoPdb). Table  S1  The publicly available pathway and chemical databases used. Table  S2  The inhibitors used in the model of the pathway with structural information. The inhibitors selected were those with the greatest efficacy in humans. Table  S3  The ten best performing drug combinations identified using the genetic algorithm as part of hypothesis generation, together with their scores. File S1 Supplementary_Mevalonate_Pathway.
I) Pharmacological literature seeds databases of pharmacological interactions. (II) Pharmacological and chemical databases containing sufficient information for experimental results to be reproduced accurately. Database Application Program Interfaces (APIs) facilitate extraction of results for hypothesis generation. (III) Interaction literature seeds databases of biological pathways. (IV) Pathway databases containing sufficient information for experimental results to be reproduced accurately. Database APIs facilitate extraction of results for hypothesis generation. (V) Hypothesis generation for single drug and multi-drug interventions using data obtained through APIs from the pharmacological and pathway databases.
Acknowledgements Initial calculations of optimal multi-drug interventions were completed using the supercomputing cluster made available by the  Intelligent Systems Research Centre at the University of Ulster. We gratefully acknowledge the teams and funders that support the range of external database resources used, without which this work would not have been possible. Any observations of error are meant to form part of a constructive discussion rather than criticism. We are indebted to the late  Prof Anthony Harmar  (dedication below) for his engagement and enthusiasm during the early phases of this project. The  IUPHAR/BPS Guide  to PHARMACOLOGY database is funded by the  International Union of Basic and Clinical Pharmacology , the  British Pharmacological Society  and  Wellcome Trust Biomedical Resources  grant  099156/Z/12/Z  (H.
( I) Pharmacological literature seeds databases of pharmacological interactions. (II) Pharmacological and chemical databases containing sufficient information for experimental results to be reproduced accurately. Database Application Program Interfaces (APIs) facilitate extraction of results for hypothesis generation. (III) Interaction literature seeds databases of biological pathways. (IV) Pathway databases containing sufficient information for experimental results to be reproduced accurately. Database APIs facilitate extraction of results for hypothesis generation. (V) Hypothesis generation for single drug and multi-drug interventions using data obtained through APIs from the pharmacological and pathway databases.
Methods We conducted an observational, retrospective study of patients older than 70 years diagnosed with spontaneous CAD admitted to the Stroke Unit of the University Hospital of Nancy (northeastern France) from January 1, 2004 to December 31, 2015. Patients were identified from our institutional anonymized database. A search of reported cases in the literature was performed using the database Medline/Pubmed for articles published over the last 30 years with the keywords: "cervical artery dissection" and "spontaneous cervical artery dissection". We extracted case reports of patients older than 70 years and studies with an age range over 70 years. Cases describing intracranial dissections only, atherosclerotic aneurysm, and dissection due to proven traumatism, surgery, or cancer were excluded.
Results During the study period, two patients older than 70 years with spontaneous CAD were admitted to our Stroke Unit. This represents ,0. 1% of overall patients seen in the Stroke Unit and ,0. 1% of patients over 70 years diagnosed with CAD. Literature review found only eight studies about CAD including a population with an age range over 70 years,  [4] [5] [6] [7] [8] [9] [10] [11]  and two case reports describing a 70-and an 80-year-old patient with spontaneous CAD. 2, 3 Data from these references and our cases are detailed in Table  1. Out of all the reported cases, the sex was known for five patients of whom two were men and three were women. The oldest patient was 89 years. Location of CAD was documented in 10 cases -the internal carotid artery (ICA) for six patients, the vertebral artery for three patients, and dissection of the common carotid artery (CCA) for one patient.
Background To be considered as rare, an individual disease must affect a limited number of people within the population. It is defined, in Europe, by a prevalence lower than 1 per 2000 (European Regulations on Orphan Medicine)  [1]. More than 7000 such clinical entities have been described of which between 70 and 80% are genetic  [2]. In the Orphanet database  [3] , rare Head and Neck disorders comprise 2153 diseases  [4]. These include rare facial disorders (such as lip and cleft palate, isolated or associated with syndromes like Goldenhar syndrome or van der Woude syndrome), oral and dental disorders (such as isolated oligodontia or associated with syndromes such as ectodermal dysplasia), ORL disorders (such as cervicofacial fistulas associated with BOR syndrome), and cranial disorders (such as craniofaciosynostosis associated for example with Crouzon syndrome).
The RD Healthcare network in France was first organized in 2005. The Head, Neck and Teeth National Network is one of the 23 French RD healthcare networks created under the national policy on RD (http://www. orpha. net/ actor/EuropaNews/2006/doc/French_National_Plan. pdf. One hundred and thirty-one expertise centres called reference centres for RD have been established in France, creating highly specialised university and non-university hospital teams for diagnosis and care. A national repository of data on patients with rare diseases, referred to centres involved in one of the rare disease networks, was established during the second French national plan. It is called BNDMR (Banque Nationale de Données Maladies Rares) (http:// enlord. bndmr. fr/#homepage). Each sector includes RD reference centres and RD competence centres.
Centres of expertise are called centres of reference in France, and the regional centres are called competence centres. Reference centres are intended to coordinate the definition of referential and therapeutic protocols as well as epidemiological supervision and education and research activities. At regional level, the competence centres identified by the reference centres establish the diagnoses of rare diseases, implement therapies when available, and organise patient care in relation to the designated reference centres and actors, and health and medico-social structures  [21]. Reference and competence centres work with many health facilities, through diagnostic laboratories, medico-social professionals, fundamental, clinical and translational research teams, and patient associations. Data collected from these centres are recorded in The French Rare Diseases Repository (BNDMR) with the following objectives: 1) To measure and describe the time taken to diagnose and therapeutic care.