D
David C. Reutens
Researcher at University of Queensland
Publications - 367
Citations - 11854
David C. Reutens is an academic researcher from University of Queensland. The author has contributed to research in topics: Epilepsy & Cognition. The author has an hindex of 55, co-authored 356 publications receiving 10668 citations. Previous affiliations of David C. Reutens include Royal Perth Hospital & Royal Melbourne Hospital.
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Structural investigation on phenyl- and pyridin-2-ylamino(methylene)naphthalen-2(3H)-one. Substituent effects on the NMR chemical shifts
TL;DR: The pyridyl‐substituted Schiff bases containing hydroxyl moiety were found to show the most downfield shift for the NH protons in DMSO solvent, and this was rationalized due to the formation of a six‐ and five‐membered ring using hydrogen bonds for these two compounds.
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3D-Spatial encoding with permanent magnets for ultra-low field magnetic resonance imaging.
TL;DR: It is demonstrated that a single encoding magnet moving around the sample in a single revolution suffices for the generation of a 3D image by back projection.
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Heteronuclear NMR Spectroscopic Investigations of Gallium Complexes of Substituted Thiosemicarbazones Including X-Ray Crystal Structure, a New Halogen Exchange Strategy, and 18F Radiolabelling
Taracad K. Venkatachalam,Gregory K. Pierens,Paul V. Bernhardt,Damion H.R. Stimson,Rajiv Bhalla,Lynette K. Lambert,David C. Reutens +6 more
TL;DR: In this article, five thiosemicarbazone ligands have been synthesized, and their coordination chemistry with gallium was investigated, and the X-ray crystal structures of two of the complexes were reported.
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Role of the Hippocampus During Logical Reasoning and Belief Bias in Aging.
TL;DR: The whole-brain results showed that older adults recruited the hippocampus during the premise integration stage more than their younger counterparts, and the integrity of the left cingulum bundle was associated with the higher rejection of believable premises more than unbelievable ones.
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Optimal-Margin Evolutionary Classifier
TL;DR: In this article, an evolutionary algorithm was proposed to find a hyperplane that best classifies instances while minimizing the classification risk, and the proposed algorithm is significantly more robust against noise and outliers comparing to other methods.