89,426 research outputs found
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
Comprehensive Health Care Reform and Biomedical Innovation
Considers ways to control the costs of development, adoption, and diffusion of new technologies as part of comprehensive healthcare reform. Discusses how cost control interventions might affect coverage, physician payments, and care processes
Mechanism-Based Redesign of GAP to Activate Oncogenic Ras
Ras GTPases play a crucial role in cell signaling pathways. Mutations of the Ras gene occur in about one third of cancerous cell lines and are often associated with detrimental clinical prognosis. Hot spot residues Gly12, Gly13, and Gln61 cover 97% of oncogenic mutations, which impair the enzymatic activity in Ras. Using QM/MM free energy calculations, we present a two-step mechanism for the GTP hydrolysis catalyzed by the wild-type Ras.GAP complex. We found that the deprotonation of the catalytic water takes place via the Gln61 as a transient Brønsted base. We also determined the reaction profiles for key oncogenic Ras mutants G12D and G12C using QM/MM minimizations, matching the experimentally observed loss of catalytic activity, thereby validating our reaction mechanism. Using the optimized reaction paths, we devised a fast and accurate procedure to design GAP mutants that activate G12D Ras. We replaced GAP residues near the active site and determined the activation barrier for 190 single mutants. We furthermore built a machine learning for ultrafast screening, by fast prediction of the barrier heights, tested both on the single and double mutations. This work demonstrates that fast and accurate screening can be accomplished via QM/MM reaction path optimizations to design protein sequences with increased catalytic activity. Several GAP mutations are predicted to re-enable catalysis in oncogenic G12D, offering a promising avenue to overcome aberrant Ras-driven signal transduction by activating enzymatic activity instead of inhibition. The outlined computational screening protocol is readily applicable for designing ligands and cofactors analogously
Experience with the Open Source based implementation for ATLAS Conditions Data Management System
Conditions Data in high energy physics experiments is frequently seen as
every data needed for reconstruction besides the event data itself. This
includes all sorts of slowly evolving data like detector alignment, calibration
and robustness, and data from detector control system. Also, every Conditions
Data Object is associated with a time interval of validity and a version.
Besides that, quite often is useful to tag collections of Conditions Data
Objects altogether. These issues have already been investigated and a data
model has been proposed and used for different implementations based in
commercial DBMSs, both at CERN and for the BaBar experiment. The special case
of the ATLAS complex trigger that requires online access to calibration and
alignment data poses new challenges that have to be met using a flexible and
customizable solution more in the line of Open Source components. Motivated by
the ATLAS challenges we have developed an alternative implementation, based in
an Open Source RDBMS. Several issues were investigated land will be described
in this paper:
-The best way to map the conditions data model into the relational database
concept considering what are foreseen as the most frequent queries.
-The clustering model best suited to address the scalability problem.
-Extensive tests were performed and will be described.
The very promising results from these tests are attracting the attention from
the HEP community and driving further developments.Comment: 8 pages, 4 figures, 3 tables, conferenc
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Clinical leadership in action
The nature, scope and potential for clinical leadership are explored by focusing on four cases. These were cross-boundary service redesign attempts for dementia and sexual health in London and Greater Manchester. Each case contained multiple organisations, including GPs and primary care trusts, acute hospital trusts, mental health trusts, local authorities and independent sector provider
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