Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- LANGUAGE (2)
- ARCH (1)
- Adaptive testing (1)
- Autopoesis (1)
- Computer Science (1)
-
- Conditional heteroskedasticity (1)
- Dissipative Structures (1)
- Econometric Methods (1)
- Empirical Finance (1)
- Extracellular Protein Homeostasis (1)
- General Systems Theory (1)
- Integrated optimization methods - Books (1)
- Librarianship (1)
- Meta-Systems (1)
- Ontology (1)
- Reflexive (1)
- Semiparametric (1)
- Social (1)
- Systems Theory (1)
- VISUO-SPATIAL (1)
- Worlds (1)
- Publication
- File Type
Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Perl For Librarians (Workshop On The Practical Extraction And Reporting Language), Patrick Yott, Christopher Hoebeke
Perl For Librarians (Workshop On The Practical Extraction And Reporting Language), Patrick Yott, Christopher Hoebeke
Christopher H Hoebeke
No abstract provided.
Clusterin Is An Atp-Independent Chaperone With Very Broad Substrate Specificity That Stabilizes Stressed Proteins In A Folding-Competent State, Stephen Poon, Simon Easterbrook-Smith, Mark Rybchyn, John Carver, Mark Wilson
Clusterin Is An Atp-Independent Chaperone With Very Broad Substrate Specificity That Stabilizes Stressed Proteins In A Folding-Competent State, Stephen Poon, Simon Easterbrook-Smith, Mark Rybchyn, John Carver, Mark Wilson
Mark R Wilson
We recently reported that the ubiquitous, secreted protein clusterin has chaperone activity in vitro [Humphreys et al. (1999) J. Biol. Chem. 274, 6875−6881]. In this study, we demonstrate that clusterin (i) inhibits stress-induced precipitation of a very broad range of structurally divergent protein substrates, (ii) binds irreversibly via an ATP-independent mechanism to stressed proteins to form solubilized high molecular weight complexes, (iii) lacks detectable ATPase activity, (iv) when acting alone, does not effect refolding of stressed proteins in vitro, and (v) stabilizes stressed proteins in a state competent for refolding by heat shock protein 70 (HSP70). Furthermore, we show that, …
Logic-Based Methods For Optimization: Combining Optimization And Constraint Satisfaction, John Hooker
Logic-Based Methods For Optimization: Combining Optimization And Constraint Satisfaction, John Hooker
John Hooker
No abstract provided.
Collaborative Activity Between Parietal And Dorso-Lateral Prefrontal Cortex In Dynamic Spatial Working Memory Revealed By Fmri, Vaibhav A. Diwadkar, Patricia A. Carpenter, Marcel Adam Just
Collaborative Activity Between Parietal And Dorso-Lateral Prefrontal Cortex In Dynamic Spatial Working Memory Revealed By Fmri, Vaibhav A. Diwadkar, Patricia A. Carpenter, Marcel Adam Just
Marcel Adam Just
No abstract provided.
The Neural Basis Of Strategy And Skill In Sentence-Picture Verification, Erik D. Reichle, Patricia A. A. Carpenter, Marcel Adam Just
The Neural Basis Of Strategy And Skill In Sentence-Picture Verification, Erik D. Reichle, Patricia A. A. Carpenter, Marcel Adam Just
Marcel Adam Just
No abstract provided.
Working Memory And Executive Function: Evidence From Neuroimaging, Patricia A. Carpenter, Marcel Adam Just, Erik D. Reichle
Working Memory And Executive Function: Evidence From Neuroimaging, Patricia A. Carpenter, Marcel Adam Just, Erik D. Reichle
Marcel Adam Just
No abstract provided.
Reflexive Autopoietic Systems Theory, Kent D. Palmer
Reflexive Autopoietic Systems Theory, Kent D. Palmer
Kent D. Palmer
Exploring the Meta-systems of Emergent Worlds
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Douglas G. Steigerwald
Specification tests for conditional heteroskedasticity that are derived under the assumption that the density of the innovation is Gaussian may not be powerful in light of the recent empirical results that the density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first-order and second-order theory for our procedures. Simulations indicate that asymptotic power gains are achievable in finite samples.