Vascular Endothelial Growth Factor-A (VEGFA) signaling is crucial to the cellular processes involved in angiogenesis. Previously, we assembled a network of molecular reactions induced by VEGFA in human umbilical vein endothelial cell populations. Considering transcriptome as a read-out of the transcriptional and epigenomic regulatory network, we now present an analysis of VEGFA-induced temporal transcriptome datasets from 6 non-synchronized studies. From these datasets, applying a confidence criterion, a set of early VEGFA-responsive signature genes were derived and evaluated for their co-expression potential with respect to multiple cancer gene expression datasets. Further, inclusive of a set of ligand-receptor pairs, a list of ligand and receptor signaling systems that potentially fine-tune the endothelial cell functions subsequent to VEGFA signaling were also derived. We believe that a number of these signaling systems would concurrently and/or hierarchically fine-tune the signaling network of endothelial cell populations towards the processes associated with angiogenesis through autocrine, paracrine, juxtacrine, and matricrine modes. By further analysis of published literature on VEGFA signaling, we also present an improved update-version of our previous VEGFA signaling network model in endothelial cells as a platform for analysis of crosstalk with these signaling systems.

No abstract

No abstract

Active noise control (ANC) systems are tailored for user-specific scenarios which are required in biomedical applications due to the physical restrictions in the placement of sensors and actuators. This study examines the role of spectral flatness of acoustic channels and room reflection coefficients in ANC performance. Each room has a unique characteristic response in transforming a source signal. By employing preliminary measurements and numerical simulation, the authors show that improved noise control is possible by optimizing room reverberation and spectral flatness of the secondary acoustic channel. This result has potential application in improving existing ANC systems in biomedical applications like fMRI.