Biomedical subjects

Raghu Machiraju

Publications and source records attributed to Raghu Machiraju.

3 recordsLinked to original sources

PubMed2007

Rb is critical in a mammalian tissue stem cell population.

The inactivation of the retinoblastoma (Rb) tumor suppressor gene in mice results in ectopic proliferation, apoptosis, and impaired differentiation in extraembryonic, neural, and erythroid lineages, culminating in fetal death by embryonic day 15.5 (E15.5). Here we show that the specific loss of Rb in trophoblast stem (TS) cells, but not in trophoblast derivatives, leads to an overexpansion of trophoblasts, a disruption of placental architecture, and fetal death by E15.5. Despite profound placental abnormalities, fetal tissues appeared remarkably normal, suggesting that the full manifestation of fetal phenotypes requires the loss of Rb in both extraembryonic and fetal tissues. Loss of Rb resulted in an increase of E2f3 expression, and the combined ablation of Rb and E2f3 significantly suppressed Rb mutant phenotypes. This rescue appears to be cell autonomous since the inactivation of Rb and E2f3 in TS cells restored placental development and extended the life of embryos to E17.5. Taken together, these results demonstrate that loss of Rb in TS cells is the defining event causing lethality of Rb(-/-) embryos and reveal the convergence of extraembryonic and fetal functions of Rb in neural and erythroid development. We conclude that the Rb pathway plays a critical role in the maintenance of a mammalian stem cell population.

Animals↗

PubMedDate not supplied

Spatial domain wavelet design for feature preservation in computational data sets.

High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters.

Algorithms↗

PubMedDate not supplied

Vortex visualization for practical engineering applications.

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability.

Journal Article↗