Molecular cloning and characterization of a distinct human phosphodiesterase gene family: PDE11ALindsay Fawcett, Rhona W. Baxendale, Peter Stacey et al.|Proceedings of the National Academy of Sciences|2000 We report here the cloning, expression, and characterization of human PDE11A1, a member of a distinct cyclic nucleotide phosphodiesterase (PDE) family. PDE11A exhibits </=50% amino acid identity with the catalytic domains of all other PDEs, being most similar to PDE5, and has distinct biochemical properties. The human PDE11A1 cDNA isolated contains a complete open reading frame encoding a 490-amino acid enzyme with a predicted molecular mass of 55,786 Da. At the N terminus PDE11A1 has a single GAF domain homologous to that found in other signaling molecules, including PDE2, PDE5, PDE6, and PDE10, which constitutes a potential allosteric binding site for cGMP or another small ligand. Tissue distribution studies indicate that PDE11A mRNA occurs at highest levels in skeletal muscle, prostate, kidney, liver, pituitary, and salivary glands and testis. PDE11A is expressed as at least three major transcripts of approximately 10.5, approximately 8.5, and approximately 6.0 kb, thus suggesting the existence of multiple subtypes. This possibility is further supported by the detection of three distinct proteins of approximately 78, approximately 65, and approximately 56 kDa by Western blotting of human tissues for PDE11A isoforms. Recombinant human PDE11A1 hydrolyzes both cGMP and cAMP with K(m) values of 0.52 microM and 1.04 microM, respectively, and similar V(max) values. Therefore, PDE11A represents a dual-substrate PDE that may regulate both cGMP and cAMP under physiological conditions. PDE11A is sensitive to the nonselective PDE inhibitor 3-isobutyl-1-methylxanthine (IBMX) as well as zaprinast and dipyridamole, inhibitors that are generally considered relatively specific for the cGMP-selective PDEs, with IC(50) values of 49.8 microM, 12.0 microM, and 0.37 microM, respectively.
Results of the Ontology Alignment Evaluation Initiative 2016Manel Achichi, Michelle Cheatham, Zlatan Dragisic et al.|City Research Online (City University London)|2016 <p>Ontology matching consists of finding correspondences between semantically related entities of different ontologies. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2017 campaign offered 9 tracks with 23 test cases, and was attended by 21 participants. This paper is an overall presentation of that campaign.</p>
Implementation and relevance of FAIR data principles in biopharmaceutical R&D• Biopharma R&D productivity can be improved by implementing the FAIR Data Principles. • FAIR enables powerful new AI analytics to access data for machine learning and prediction. • FAIR is a fundamental enabler for digital transformation of biopharma R&D. • The Pistoia Alliance supports sharing of best practices for FAIR implementation. Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural and food production, is becoming increasingly data-driven and can significantly improve its efficiency and effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship. By so doing, the plethora of new and powerful analytical tools such as artificial intelligence and machine learning will be able, automatically and at scale, to access the data from which they learn, and on which they thrive. FAIR is a fundamental enabler for digital transformation.