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James Duncan

Publications and source records attributed to James Duncan.

3 recordsLinked to original sources

Exploring the ability of chlorophyll b to bind to the CP43' protein induced under iron deprivation in a mutant of Synechocystis PCC 6803 containing the cao gene.

Cyanobacteria, unlike plants and green algae, do not contain chlorophyll (Chl) b. This is because of the absence of the cao gene which encodes the enzyme that catalyses a two step oxygenation of chlorophyllide a to chlorophyllide b. Recently, however, the cao gene of higher plants was engineered into Synechocystis PCC 6803 leading to Chl b synthesis in this cyanobacterium [Satoh et al., J. Biol. Chem. 276 (2001) 4293-4297]. Here we use this same cao-plus mutant to show that Chl b can bind to the CP43' protein, expressed in cells exposed to low iron levels, which normally binds Chl a only. In so doing CP43' is changed to a Chl a/Chl b-binding protein and in this respect resembles the closely related Chl a/Chl b-binding Pcb protein of prochlorophytes (green oxyphotobacteria). The results emphasise the possibility of using an in vitro system to elucidate factors which control the binding of these two different forms of chlorophylls to the six transmembrane helical light-harvesting proteins of oxygenic photosynthetic organisms.

Bacterial Proteins↗

Integrated approach to teaching and testing in histology with real and virtual imaging.

The University of Iowa College of Medicine histology teaching laboratory incorporates extensive Web- and computer-based teaching modalities, including the Virtual Microscope (VM), as emerging learning aids in histology and pathology laboratory instruction. We report here our experience in offering a multiple resource-based approach to laboratory instruction while retaining the opportunity and requirement of examining actual microscopic slide preparations with the microscope. Acceptance of this approach has been high among our students and faculty, and performance levels established over years of teaching histology by traditional means have been maintained.

Anatomy↗

Model-driven brain shift compensation.

Surgical navigation systems provide the surgeon with a display of preoperative and intraoperative data in the same coordinate system. However, the systems currently in use in neurosurgery are subject to inaccuracy caused by intraoperative brain deformation (brain shift), since they typically assume that the intracranial structures are rigid. Experiments show brain shift of up to 1 cm, making it the dominant error in the system. We propose a biomechanical-model-based approach for brain shift compensation. Two models are presented: a damped spring-mass model and a model based on continuum mechanics. Both models are guided by limited intraoperative (exposed brain) surface data, with the aim to recover the deformation in the full volume. The two models are compared and their advantages and disadvantages discussed. A partial validation using intraoperative MR image sequences indicates that the approach reduces the error caused by brain shift.

Brain↗